"""ProfileStats object for runtime and memory profiling.
"""
#
# TODO: measure memory usage like ProfileMode did
# TODO: put the optimization tips into a tips section??
# TODO: add tip to use specify_shape (is specify_shape even in library doc?)
# TODO: ensure field width for string fields makes columns line up
# TODO: what to do about 'diff summary'? (ask Fred?)
#
__authors__ = "James Bergstra"
__reviewer__ = "Razvan Pascanu"
__copyright__ = "(c) 2011, Universite de Montreal"
__license__ = "3-clause BSD License"
__contact__ = "theano-dev <theano-dev@googlegroups.com>"

__docformat__ = "restructuredtext en"
import atexit
import copy
import os
import sys
import time
from theano.compat.python2x import defaultdict

import numpy

import theano
from theano.gof import graph
from theano.configparser import AddConfigVar, BoolParam, IntParam, StrParam


import_time = time.time()
config = theano.config

_atexit_print_list = []
_atexit_registered = False

AddConfigVar('profiling.time_thunks',
             """Time individual thunks when profiling""",
             BoolParam(True),
             in_c_key=False)

AddConfigVar('profiling.n_apply',
             "Number of Apply instances to print by default",
             IntParam(20, lambda i: i > 0),
             in_c_key=False)

AddConfigVar('profiling.n_ops',
             "Number of Ops to print by default",
             IntParam(20, lambda i: i > 0),
             in_c_key=False)

AddConfigVar('profiling.output_line_width',
             "Max line width for the profiling output",
             IntParam(512, lambda i: i > 0),
             in_c_key=False)

AddConfigVar('profiling.min_memory_size',
             """For the memory profile, do not print Apply nodes if the size
             of their outputs (in bytes) is lower than this threshold""",
             IntParam(1024, lambda i: i >= 0),
             in_c_key=False)

AddConfigVar('profiling.min_peak_memory',
             """The min peak memory usage of the order""",
             BoolParam(False),
             in_c_key=False)

AddConfigVar('profiling.destination',
             """
             File destination of the profiling output
             """,
             StrParam('stderr'),
             in_c_key=False)


def _atexit_print_fn():
    """Print ProfileStat objects in _atexit_print_list to _atexit_print_file
    """
    to_sum = []

    if config.profiling.destination == 'stderr':
        destination_file = sys.stderr
    elif config.profiling.destination == 'stdout':
        destination_file = sys.stdout
    else:
        destination_file = open(config.profiling.destination, 'w')

    for ps in _atexit_print_list:
        if ps.fct_callcount or ps.compile_time > 0:
            ps.summary(file=destination_file,
                       n_ops_to_print=config.profiling.n_ops,
                       n_apply_to_print=config.profiling.n_apply)
            if not isinstance(ps, ScanProfileStats):
                to_sum.append(ps)
        else:
            # TODO print the name if there is one!
            print 'Skipping empty Profile'
    if len(to_sum) > 1:
        # Make a global profile
        cum = copy.copy(to_sum[0])
        msg = ("Sum of all(%d) printed profiles at exit excluding Scan op"
               " profile." % len(to_sum))
        cum.message = msg
        for ps in to_sum[1:]:
            for attr in ["compile_time", "fct_call_time", "fct_callcount",
                         "vm_call_time", "optimizer_time", "linker_time",
                         "validate_time", "import_time"]:
                setattr(cum, attr, getattr(cum, attr) + getattr(ps, attr))

            # merge dictonary
            for attr in ["apply_time", "apply_callcount",
                         "apply_cimpl", "variable_shape", "variable_strides"]:
                cum_attr = getattr(cum, attr)
                for key, val in getattr(ps, attr).iteritems():
                    assert key not in cum_attr
                    cum_attr[key] = val

            if cum.optimizer_profile and ps.optimizer_profile:
                merge = cum.optimizer_profile[0].merge_profile(
                    cum.optimizer_profile[1],
                    ps.optimizer_profile[1])
                cum.optimizer_profile = (cum.optimizer_profile[0], merge)
            else:
                cum.optimizer_profile = None

        cum.summary(file=destination_file,
                    n_ops_to_print=config.profiling.n_ops,
                    n_apply_to_print=config.profiling.n_apply)


class ProfileStats(object):

    """
    Object to store runtime and memory profiling information for all of
    Theano's operations: compilation, optimization, execution.
    """

    #
    # Note on implementation:
    # Class variables are used here so that each one can be
    # documented and initialized together.
    # dictionary variables are initialized with None.
    #

    compile_time = 0.0
    # Total time spent in body of orig_function,
    # dominated by graph optimization and compilation of C
    #

    fct_call_time = 0.0
    # The total time spent in Function.__call__
    #

    fct_callcount = 0
    # Number of calls to Function.__call__
    #

    vm_call_time = 0.0
    # Total time spent in Function.fn.__call__
    #

    apply_time = None
    # dict from node -> float runtime
    #

    apply_callcount = None
    # dict from node -> number of executions
    #

    apply_cimpl = None
    # dict from node -> bool (1 if c, 0 if py)
    #

    message = None
    # pretty string to print in summary, to identify this output
    #

    variable_shape = {}
    # Variable -> shapes
    #

    variable_strides = {}
    # Variable -> strides
    #

    optimizer_time = 0.0
    # time spent optimizing graph (FunctionMaker.__init__)

    validate_time = 0.0
    # time spent in fgraph.validate
    # This is a subset of optimizer_time that is dominated by toposort()
    # when the destorymap feature is included.

    linker_time = 0.0
    # time spent linking graph (FunctionMaker.create)

    import_time = 0.0
    # time spent in importing compiled python module.

    line_width = config.profiling.output_line_width

    optimizer_profile = None
    # None or tuple (the optimizer, the profile it returned)

    # param is called flag_time_thunks because most other attributes with time
    # in the name are times *of* something, rather than configuration flags.
    def __init__(self, atexit_print=True, flag_time_thunks=None, **kwargs):
        """
        atexit_print - bool. True means that this object will be printed to
                       stderr (using .summary()) at the end of the program.
        **kwargs - misc initializers. These should (but need not) match the
                   names of the class vars declared in this class.
        """
        if (hasattr(theano, 'sandbox') and
                hasattr(theano.sandbox, 'cuda') and
                theano.sandbox.cuda.cuda_enabled):
            if os.environ.get('CUDA_LAUNCH_BLOCKING', '0') != '1':
                raise Exception(
                    "You are running the Theano profiler with CUDA enabled."
                    " Theano GPU ops execution is asynchronous by default."
                    " So by default, the profile is useless."
                    " You must set the environment variable"
                    " CUDA_LAUNCH_BLOCKING to 1 to tell the CUDA driver to"
                    " synchronize the execution to get a meaningful profile.")

        self.apply_callcount = {}
        self.output_size = {}
        self.apply_time = {}
        self.apply_cimpl = {}
        self.variable_shape = {}
        self.variable_strides = {}
        if flag_time_thunks is None:
            self.flag_time_thunks = config.profiling.time_thunks
        else:
            self.flag_time_thunks = flag_time_thunks
        self.__dict__.update(kwargs)
        #print >> sys.stderr, "self.message", self.message
        if atexit_print:
            global _atexit_print_list
            _atexit_print_list.append(self)
            global _atexit_registered
            if not _atexit_registered:
                atexit.register(_atexit_print_fn)
                _atexit_registered = True

    def class_time(self):
        """dict op -> total time on thunks"""
        # timing is stored by node, we compute timing by class on demand
        rval = {}
        for node, t in self.apply_time.items():
            typ = type(node.op)
            rval.setdefault(typ, 0)
            rval[typ] += t
        return rval

    def class_callcount(self):
        """dict op -> total number of thunk calls"""
        # timing is stored by node, we compute timing by class on demand
        rval = {}
        for node, count in self.apply_callcount.items():
            typ = type(node.op)
            rval.setdefault(typ, 0)
            rval[typ] += count
        return rval

    def class_nodes(self):
        """dict op -> total number of nodes"""
        # timing is stored by node, we compute timing by class on demand
        rval = {}
        for node, count in self.apply_callcount.items():
            typ = type(node.op)
            rval.setdefault(typ, 0)
            rval[typ] += 1
        return rval

    def class_impl(self):
        """dict op -> total number of nodes"""
        # timing is stored by node, we compute timing by class on demand
        rval = {}
        for node in self.apply_callcount:
            typ = type(node.op)
            if self.apply_cimpl[node]:
                impl = 'C '
            else:
                impl = 'Py'
            rval.setdefault(typ, impl)
            if rval[typ] != impl and len(rval[typ]) == 2:
                rval[typ] += impl
        return rval

    def op_time(self):
        """dict op -> total time on thunks"""
        # timing is stored by node, we compute timing by Op on demand
        rval = {}
        for node, t in self.apply_time.items():
            rval.setdefault(node.op, 0)
            rval[node.op] += t
        return rval

    def fill_node_total_time(self, node, total_times):
        """node -> fill total time icluding its parents (returns nothing)"""
        # timing is stored by node, we compute total time on demand
        total = self.apply_time[node]
        for parent in node.get_parents():
            if parent.owner in self.apply_time.keys():
                if parent.owner not in total_times.keys():
                    self.fill_node_total_time(parent.owner, total_times)
                total += total_times[parent.owner]
        total_times[node] = total

    def compute_total_times(self):
        """dict op -> total time icluding the time for parents"""
        rval = {}
        for node in self.apply_time.keys():
            if node not in rval:
                self.fill_node_total_time(node, rval)
        return rval

    def op_callcount(self):
        """dict op -> total number of thunk calls"""
        # timing is stored by node, we compute timing by Op on demand
        rval = {}
        for node, count in self.apply_callcount.items():
            rval.setdefault(node.op, 0)
            rval[node.op] += count
        return rval

    def op_nodes(self):
        """dict op -> total number of nodes"""
        # timing is stored by node, we compute timing by Op on demand
        rval = {}
        for node, count in self.apply_callcount.items():
            rval.setdefault(node.op, 0)
            rval[node.op] += 1
        return rval

    def op_impl(self):
        """dict op -> 'C' or 'Py' depending how the op is implemented"""
        # timing is stored by node, we compute timing by Op on demand
        rval = {}
        for node in self.apply_callcount:
            if self.apply_cimpl[node]:
                rval[node.op] = 'C '
            else:
                rval[node.op] = 'Py'
        return rval

    def summary_class(self, file=sys.stderr, N=None):
        if self.apply_time:
            local_time = sum(self.apply_time.values())
        else:
            local_time = 0
        if local_time == 0:
            print >> file, ('ProfileMode.summary_class: total time 0'
                            ' (did you forget to enable counters?)')
            return
        class_time = self.class_time()
        class_call = self.class_callcount()
        class_apply = self.class_nodes()
        class_impl = self.class_impl()
        if N is None:
            N = len(self.class_time)
        otimes = [(t * 100 / local_time,
                   t,
                   clas,
                   class_impl.get(clas, '  '),
                   class_call.get(clas, 0),
                   class_apply.get(clas, 0))
                  for clas, t in class_time.items()]
        otimes.sort(key=lambda t: (t[1], t[4], t[5]), reverse=True)
        tot = 0
        print >> file, 'Class'
        print >> file, '---'
        #print >> file, '<% time> <cumulative %%> <apply time>,'
        #print >>file, '<cumulative seconds> <time per call> <nb_call>'
        #print >>file, '<Class name>'
        hs = []
        # formatting string
        es = []

        hs += ['<% time>']
        es += ['  %4.1f%% ']

        hs += ['<sum %>']
        es += [' %5.1f%% ']

        hs += ['<apply time>']
        es += ['   %7.3fs ']

        hs += ['<time per call>']
        es += ['     %8.2es ']

        hs += ['<type>']
        es += ['   %2s ']

        hs += ['<#call>']
        es += ['%6d  ']

        hs += ['<#apply>']
        es += [' %4d  ']

        upto_length = numpy.sum([len(x) for x in hs]) + len(hs)
        maxlen = max(self.line_width - upto_length, 0)
        hs += ['<Class name>']
        es += ['%s']
        header_str = ' '.join(hs)
        format_str = ' '.join(es)

        print >> file, header_str

        for f, t, a, impl, nb_call, nb_apply in otimes[:N]:
            if nb_call == 0:
                assert t == 0
                continue
            tot += t
            ftot = tot * 100 / local_time
            # Remove the useless start and end of the class name:
            # "<class 'theano.sandbox.cuda.blas.GpuDot22'>" -> "theano.sandbox.cuda.blas.GpuDot22"
            class_name = str(a)[8:-2][:maxlen]
            print >> file, format_str % (f, ftot, t, t / nb_call,
                                         impl, nb_call,
                                         nb_apply, class_name)
            # While this carries over less information, it is arranged such
            # that it way more readeable that the previous output of the
            # profiler
        print >>file, '   ... (remaining %i Classes account for %6.2f%%(%.2fs) of the runtime)'\
            % (max(0, len(otimes) - N),
               sum(f for f, t, a, ci, nb_call, nb_op in otimes[N:]),
               sum(t for f, t, a, ci, nb_call, nb_op in otimes[N:]))
        print >> file, ''

    def summary_ops(self, file=sys.stderr, N=None):
        if self.apply_time:
            local_time = sum(self.apply_time.values())
        else:
            local_time = 0
        if local_time == 0:
            print >> file, ('ProfileMode.summary_ops: total time 0'
                            ' (did you forget to enable counters?)')
            return
        op_time = self.op_time()
        op_call = self.op_callcount()
        op_apply = self.op_nodes()
        op_impl = self.op_impl()
        otimes = [(t * 100 / local_time,
                   t,
                   op,
                   op_impl.get(op, '  '),
                   op_call.get(op, 0),
                   op_apply.get(op, 0))
                  for op, t in op_time.items()]
        otimes.sort(key=lambda t: (t[1], t[4], t[5]), reverse=True)
        tot = 0
        print >> file, 'Ops'
        print >> file, '---'
        #print >> file, '<% time> <cumulative %%> <apply time>,'
        #print >>file, '<cumulative seconds> <time per call> <nb_call>'
        #print >>file, '<Op name>'
        hs = []
        # formatting string
        es = []

        hs += ['<% time>']
        es += ['  %4.1f%% ']

        hs += ['<sum %>']
        es += [' %5.1f%% ']

        hs += ['<apply time>']
        es += ['   %7.3fs ']

        hs += ['<time per call>']
        es += ['     %8.2es ']

        hs += ['<type>']
        es += ['   %2s ']

        hs += ['<#call>']
        es += ['  %4d  ']

        hs += ['<#apply>']
        es += ['  %4d  ']

        upto_length = numpy.sum([len(x) for x in hs]) + len(hs)
        maxlen = max(self.line_width - upto_length, 0)
        hs += ['<Op name>']
        es += ['%s']
        header_str = ' '.join(hs)
        format_str = ' '.join(es)

        print >> file, header_str

        for f, t, a, impl, nb_call, nb_apply in otimes[:N]:
            if nb_call == 0:
                assert t == 0
                continue
            tot += t
            ftot = tot * 100 / local_time
            print >> file, format_str % (f, ftot, t, t / nb_call,
                                         impl, nb_call,
                                         nb_apply, str(a)[:maxlen])
            # While this carries over less information, it is arranged such
            # that it way more readeable that the previous output of the
            # profiler
        print >>file, '   ... (remaining %i Ops account for %6.2f%%(%.2fs) of the runtime)'\
            % (max(0, len(otimes) - N),
               sum(f for f, t, a, ci, nb_call, nb_op in otimes[N:]),
               sum(t for f, t, a, ci, nb_call, nb_op in otimes[N:]))
        print >> file, ''

    def summary_nodes(self, file=sys.stderr, N=None):
        if self.apply_time:
            local_time = sum(self.apply_time.values())
        else:
            local_time = 0
        if local_time == 0:
            print >> file, ('ProfileMode.summary_nodes: total time 0'
                            ' (did you forget to enable counters?)')
            return

        print >> file, 'Apply'
        print >> file, '------'
        #print >> file, '<% time> <cumulative %%> <apply time> <cumulative seconds> <time per call> <nb_call> <Apply Op name>'
        # headers
        hs = []
        # formatting string
        es = []

        hs += ['<% time>']
        es += ['  %4.1f%% ']

        hs += ['<sum %>']
        es += [' %5.1f%% ']

        hs += ['<apply time>']
        es += ['   %7.3fs ']

        hs += ['<time per call>']
        es += ['     %8.2es ']

        hs += ['<#call>']
        es += [' %4d  ']

        hs += ['<id>']
        es += ['%3d']

        es += ['%s', '%s']
        if self.variable_shape:
            hs += ['<Mflops>', '<Gflops/s>']

        upto_length = numpy.sum([len(x) for x in hs]) + len(hs)
        maxlen = max(self.line_width - upto_length, 0)
        hs += ['<Apply name>']
        es += ['%s']

        header_str = ' '.join(hs)
        format_str = ' '.join(es)

        print >> file, header_str

        topos = {}  # Only do the topo once per fct.
        atimes = []
        for a, t in self.apply_time.items():
            if a.fgraph not in topos:
                topo = a.fgraph.toposort()
                topos[a.fgraph] = topo
            else:
                topo = topos[a.fgraph]
            atimes.append((
                t * 100 / local_time,
                t,
                a,
                topo.index(a),
                self.apply_callcount[a]))
        del topos

        atimes.sort(reverse=True, key=lambda t: (t[1], t[3]))
        tot = 0
        for (f, t, a, nd_id, nb_call) in atimes[:N]:
            tot += t
            ftot = tot * 100 / local_time
            if nb_call == 0:
                continue
            if not self.variable_shape:
                flops = ""
                flops_s = ""
            elif hasattr(a.op, 'flops'):
                fl = a.op.flops([self.variable_shape[var]
                                 for var in a.inputs],
                                [self.variable_shape[var]
                                 for var in a.outputs])
                flops = '%8.1f' % (fl / 1024. / 1024)
                flops_s = '%10.1f' % (fl / 1024. / 1024 / 1024 / t)
            else:
                flops = "        "
                flops_s = "          "
            print >> file, format_str % (f, ftot, t, t / nb_call, nb_call,
                                         nd_id,
                                         flops, flops_s,
                                         str(a)[:maxlen])
            if not config.profile_memory:
                continue
            for idx, var in enumerate(a.inputs):
                sh = self.variable_shape.get(var, 'no shape')
                st = self.variable_strides.get(var, 'no strides')
                dtype = getattr(var, 'dtype', 'no dtype')
                print >> file, "    input %d: dtype=%s, shape=%s, strides=%s " % (
                    idx, dtype, sh, st)
            for idx, var in enumerate(a.outputs):
                sh = self.variable_shape.get(var, 'no shape')
                st = self.variable_strides.get(var, 'no strides')
                dtype = getattr(var, 'dtype', 'no dtype')
                print >> file, "    output %d: dtype=%s, shape=%s, strides=%s " % (
                    idx, dtype, sh, st)
            # Same as before, this I've sacrificied some information making
            # the output more readable
            # print >> file, '   %4.1f%%  %5.1f%%  %5.3fs  %5.3fs %.2es  %i  %s'%(
            #        f, ftot, t, tot, t/nb_call,nb_call, str(a))
        print >> file, '   ... (remaining %i Apply instances account for %.2f%%(%.2fs) of the runtime)'\
            % (max(0, len(atimes) - N),
               sum(f for f, t, a, nd_id, nb_call in atimes[N:]),
               sum(t for f, t, a, nd_id, nb_call in atimes[N:]))
        print >> file, ''

    def summary_function(self, file):
        print >> file, 'Function profiling'
        print >> file, '=================='
        print >> file, '  Message: %s' % self.message
        print >> file, '  Time in %i calls to Function.__call__: %es' % (
            self.fct_callcount, self.fct_call_time)
        if self.fct_call_time > 0:
            print >> file, '  Time in Function.fn.__call__: %es (%.3f%%)' % (
                self.vm_call_time,
                100 * self.vm_call_time / self.fct_call_time)
            local_time = sum(self.apply_time.values())
            if local_time > 0:
                print >> file, '  Time in thunks: %es (%.3f%%)' % (
                    local_time, 100 * local_time / self.fct_call_time)
        print >> file, '  Total compile time: %es' % self.compile_time
        print >> file, '    Number of Apply nodes: %s' % len(self.apply_time)
        print >> file, '    Theano Optimizer time: %es' % self.optimizer_time
        print >> file, '       Theano validate time: %es' % self.validate_time
        print >> file, ('    Theano Linker time (includes C,'
                        ' CUDA code generation/compiling): %es' %
                        self.linker_time)
        print >> file, '       Import time %es' % self.import_time
        print >> file, ''

        # The validation time is a subset of optimizer_time
        assert self.validate_time < self.optimizer_time

    def summary_memory(self, file, N=None):
        fct_memory = {}  # fgraph->dict(node->[outputs size])
        fct_shapes = {}  # fgraph->dict(node->[outputs shapes]))
        var_mem = {}  # varible->size in bytes; don't include input variables
        node_mem = {}  # node->total outputs size (only dense outputs)

        for node in self.apply_callcount.keys():
            fct_memory.setdefault(node.fgraph, {})
            fct_memory[node.fgraph].setdefault(node, [])
            fct_shapes.setdefault(node.fgraph, {})
            fct_shapes[node.fgraph].setdefault(node, [])
            sum_dense = 0
            for out in node.outputs:
                if out in self.variable_shape.keys():
                    sh = self.variable_shape[out]
                    if hasattr(out.type, 'get_size'):
                        v = out.type.get_size(sh)
                        sum_dense += v
                    else:
                        v = 0  # 'Unknown'
                else:
                    v = 0  # 'Variable isnt created'

                var_mem[out] = v
                fct_memory[node.fgraph][node].append(v)
                fct_shapes[node.fgraph][node].append(sh)
            node_mem[node] = sum_dense
        del v

        # Find the function that used the most of that statistic
        max_sum_size = 0

        # statistics with the old order
        # TODO: Make list more flexible with mulitply GPUs later
        max_node_memory_size = [0, 0, 0]
        max_running_max_memory_size = [0, 0, 0]
        max_node_memory_saved_by_view = 0
        max_node_memory_saved_by_inplace = 0

        # statistics with the new order
        new_max_node_memory_size = [0, 0, 0]
        new_max_running_max_memory_size = [0, 0, 0]
        new_max_node_memory_saved_by_view = 0
        new_max_node_memory_saved_by_inplace = 0

        # track min peak memory usage
        min_max_peak = 0
        min_peak_time = 0

        def count_running_memory(order, fgraph, nodes_mem):
            """
            Calculate memory with specific node order 
            Return a list including the following values
            1.  node_memory_size
                Sum of the size of all variables that actually allocate
                memory (excluding views, and inplace);
            2. running_memory_size
                The memory allocated after the current apply node
            3. running_max_memory_size
                The maximum of running_memory_size during the function   
            4.  node_memory_saved_by_view
                The sum of memory saved by returning view instead of new
                allocation 
            5.  node_memory_saved_by_inplace
                The sum of memory saved by reusing the input instead of
                new allocation
            """
            from theano.sandbox.cuda import CudaNdarrayType
            # Initial Mem info values [CPU, GPU]
            node_memory_size = [0, 0]
            running_memory_size = [0, 0]
            running_max_memory_size = [0, 0]
            node_memory_saved_by_view = 0
            node_memory_saved_by_inplace = 0
            # This take only the inputs/outputs dependencies.
            dependencies = fgraph.profile.dependencies

            # Initial compute_map which is used to check if a node is valid
            compute_map = defaultdict(lambda: [0])
            for var in fgraph.inputs:
                compute_map[var][0] = 1

            # two data structure used to mimic Python gc
            viewed_by = {}  # {var1: [vars that view var1]}
            # The len of the list is the value of python ref count. But we use a list, not just the ref count value.
            # This is more safe to help detect potential bug  in the algo
            for var in fgraph.variables:
                viewed_by[var] = []
            view_of = {}  # {var1: original var viewed by var1}
            # The orignal mean that we don't keep trac of all the intermediate
            # relationship in the view.

            for node in order:
                for var in node.outputs:
                    compute_map[var][0] = 1
                idx = 0
                dmap = getattr(node.op, 'destroy_map', None)
                vmap = getattr(node.op, 'view_map', None)
                val = nodes_mem[node]

                for v in val:
                    # TODO check the op returned a view
                    if dmap and idx in dmap:
                        node_memory_saved_by_inplace += v
                    # TODO check the op returned a view
                    elif vmap and idx in vmap:
                        node_memory_saved_by_view += v
                    idx += 1

                # Update the Python emulating dicts and add the memory
                # allocated by the node
                idx2 = 0
                for out in node.outputs:
                    if isinstance(out.type, CudaNdarrayType):
                        cg = 1
                    else:
                        cg = 0
                    ins = None
                    if dmap and idx2 in dmap:
                        vidx = dmap[idx2]
                        assert len(
                            vidx) == 1, "Here we only support the possibility to destroy one input"
                        ins = node.inputs[vidx[0]]
                    if vmap and idx2 in vmap:
                        assert ins is None
                        vidx = vmap[idx2]
                        assert len(
                            vidx) == 1, "Here we only support the possibility to view one input"
                        ins = node.inputs[vidx[0]]
                    if ins is not None:
                        # This is needed for destroy_map in case it
                        # return a partial view that is destroyed.  So
                        # the output could be different then the
                        # input.
                        assert isinstance(ins, theano.Variable)
                        # we keep trac of view only again the origin
                        origin = view_of.get(ins, ins)
                        view_of[out] = origin
                        viewed_by[origin].append(out)
                    else:
                        running_memory_size[cg] += var_mem[out]
                        node_memory_size[cg] += var_mem[out]
                    idx2 += 1

                running_max_memory_size[0] = max(running_max_memory_size[0],
                                                 running_memory_size[0])
                running_max_memory_size[1] = max(running_max_memory_size[1],
                                                 running_memory_size[1])

                # Mimic the combination of Theano and Python gc
                for ins in node.inputs:
                    assert not (ins in view_of and viewed_by[ins])
                    # we trac the original var, so this shouldn't happen
                    if isinstance(ins.type, CudaNdarrayType):
                        cg = 1
                    else:
                        cg = 0
                    if (dependencies[ins] and
                            ins not in fgraph.outputs and
                            ins.owner and
                            all([compute_map[v][0] for v in dependencies[ins]])):
                        if ins not in view_of and not viewed_by.get(ins, []):
                            running_memory_size[cg] -= var_mem[ins]
                        elif ins in view_of:
                            origin = view_of[ins]
                            viewed_by[origin].remove(ins)
                            if (not viewed_by[origin] and
                                    origin not in fgraph.inputs and
                                    not isinstance(origin, theano.Constant)):
                                running_memory_size[cg] -= var_mem[origin]
                    else:
                        # ins is viewed_by something else, so its
                        # memory isn't freed
                        pass

            return [node_memory_size, running_memory_size,
                    running_max_memory_size, node_memory_saved_by_inplace,
                    node_memory_saved_by_view]

        def count_minimum_peak(node_list, fgraph, nodes_mem):
            global mem_count, mem_bound, max_mem_count
            node_list = list(node_list)
            mem_count = 0
            max_mem_count = 0
            mem_bound = numpy.inf
            # This take only the inputs/outputs dependencies.
            dependencies = fgraph.profile.dependencies
            done_set = set([])
            done_dict = {}

            # Initial compute_map which is used to check if a node is valid
            compute_map = defaultdict(lambda: [0])
            for var in fgraph.inputs:
                compute_map[var][0] = 1
            for var in node_list:
                for val in var.inputs:
                    if isinstance(val, graph.Constant):
                        compute_map[val][0] = 1

            # Initial executable_nodes
            executable_nodes = set()
            for var in fgraph.inputs:
                for c, _ in var.clients:
                    if c != "output":
                        deps = c.inputs + c.destroy_dependencies
                        if all(compute_map[v][0] for v in deps):
                            executable_nodes.add(c)

            def min_memory_generator(executable_nodes, viewed_by, view_of):
                """
                Generate all valid node order from node_list
                and compute its memory peak.

                :param executable_nodes: Set of executable nodes
                """
                global mem_count, mem_bound, max_mem_count

                for node in executable_nodes:
                    new_exec_nodes = executable_nodes.copy()
                    new_exec_nodes.remove(node)

                    # Check if cut path now
                    if max_mem_count > mem_bound:
                        continue

                    viewof_change = []
                    # Use to track view_of changes

                    viewedby_add = defaultdict(lambda: [])
                    viewedby_remove = defaultdict(lambda: [])
                    # Use to track viewed_by changes

                    for var in node.outputs:
                        compute_map[var][0] = 1

                    mem_created = 0
                    mem_freed = 0
                    max_storage = max_mem_count

                    dmap = getattr(node.op, 'destroy_map', None)
                    vmap = getattr(node.op, 'view_map', None)

                    idx = 0
                    # Update the Python emulating dicts and add the
                    # memory allocated by the node
                    for out in node.outputs:
                        ins = None
                        if dmap and idx in dmap:
                            vidx = dmap[idx]
                            assert len(
                                vidx) == 1, "Here we only support the possibility to destroy one input"
                            ins = node.inputs[vidx[0]]
                        if vmap and idx in vmap:
                            assert ins is None
                            vidx = vmap[idx]
                            assert len(
                                vidx) == 1, "Here we only support the possibility to destroy one input"
                            ins = node.inputs[vidx[0]]
                        if ins is not None:
                            # This is needed for destroy_map in case it
                            # return a partial view that is destroyed.  So
                            # the output could be different then the
                            # input.
                            assert isinstance(ins, theano.Variable)
                            # We keep trac of view only again the original
                            origin = view_of.get(ins, ins)
                            view_of[out] = origin
                            viewof_change.append(out)
                            viewed_by[origin].append(out)
                            viewedby_add[origin].append(out)
                        else:
                            mem_created += var_mem[out]
                        idx += 1

                    mem_count += mem_created
                    max_mem_count = max(max_mem_count, mem_count)

                    # Mimic the combination of Theano and Python gc.
                    for ins in node.inputs:
                        assert not (ins in view_of and
                                    viewed_by[ins])
                        # We track of the original var, so this shouldn't
                        # happen
                        if (dependencies[ins] and
                                ins not in fgraph.outputs and
                                ins.owner and
                                all([compute_map[v][0] for v in dependencies[ins]])):
                            if ins not in view_of and not viewed_by.get(ins, []):
                                mem_freed += var_mem[ins]
                            elif ins in view_of:
                                origin = view_of[ins]
                                viewed_by[origin].remove(ins)
                                viewedby_remove[origin].append(ins)
                                if (not viewed_by[origin] and
                                        origin not in fgraph.inputs and
                                        not isinstance(origin, theano.Constant)):
                                    mem_freed += var_mem[origin]
                        else:
                            # ins is viewed_by something else, so its
                            # memory isn't freed
                            pass

                    mem_count -= mem_freed

                    done_set.add(node)
                    frozen_set = frozenset(done_set)
                    if done_dict.get(frozen_set, max_mem_count + 1) > max_mem_count:
                        # check if frozen_set is in done_set
                        # no, add it to done_set
                        # yes, then compare the past mem and current mem
                        # bigger, update the value and continue
                        # smaller, stop this iteration, move to next node
                        done_dict[frozen_set] = max_mem_count

                        for var in node.outputs:
                            for c, _ in var.clients:
                                if c != "output":
                                    deps = c.inputs + c.destroy_dependencies
                                    if all(compute_map[v][0] for v in deps):
                                        new_exec_nodes.add(c)

                        if not new_exec_nodes:
                            # Check and Update mem_bound
                            if max_mem_count < mem_bound:
                                mem_bound = max_mem_count
                        else:
                            min_memory_generator(
                                new_exec_nodes, viewed_by, view_of)

                    # Reset track variables
                    done_set.remove(node)
                    mem_count -= mem_created
                    max_mem_count = max_storage
                    mem_count += mem_freed
                    for var in node.outputs:
                        compute_map[var][0] = 0

                    for k_remove, v_remove in viewedby_remove.iteritems():
                        for i in v_remove:
                            viewed_by[k_remove].append(i)

                    for k_add, v_add in viewedby_add.iteritems():
                        for i in v_add:
                            viewed_by[k_add].remove(i)

                    for k in viewof_change:
                        del view_of[k]

            # two data structure used to mimic Python gc
            viewed_by = {}  # {var1: [vars that view var1]}
            # The len of the list is the value of python ref count. But we use a list, not just the ref count value.
            # This is more safe to help detect potential bug  in the algo
            for var in fgraph.variables:
                viewed_by[var] = []
            view_of = {}  # {var1: original var viewed by var1}
            # The orignal mean that we don't keep trac of all the intermediate
            # relationship in the view.

            min_memory_generator(executable_nodes, viewed_by, view_of)

            return mem_bound

        for fgraph, nodes_mem in fct_memory.iteritems():
            # Sum of the size of all variables in bytes
            sum_size = sum([sum([v for v in val if not isinstance(v, str)])
                            for key, val in nodes_mem.iteritems()])

            order = fgraph.toposort()
            # A list of intermediate variable that are not need
            # after the execution of the corresponding node.
            # It mean that after executing the node,
            # the corresponding variable can be gc.

            old_running_memory = count_running_memory(order, fgraph, nodes_mem)

            new_order = fgraph.profile.node_executed_order
            # A list of new executed node order

            new_running_memory = count_running_memory(new_order,
                                                      fgraph, nodes_mem)

            # Store the max of some stats by any function in this profile.
            max_sum_size = max(max_sum_size, sum_size)
            max_node_memory_size[0] = max(max_node_memory_size[0],
                                          sum(old_running_memory[0]))
            max_running_max_memory_size[0] = max(max_running_max_memory_size[0],
                                                 sum(old_running_memory[2]))

            # Separate CPU and GPU
            max_node_memory_size[1] = max(max_node_memory_size[1],
                                          old_running_memory[0][0])
            max_node_memory_size[2] = max(max_node_memory_size[2],
                                          old_running_memory[0][1])
            max_running_max_memory_size[1] = max(max_running_max_memory_size[1],
                                                 old_running_memory[2][0])
            max_running_max_memory_size[2] = max(max_running_max_memory_size[2],
                                                 old_running_memory[2][1])

            max_node_memory_saved_by_inplace = max(
                max_node_memory_saved_by_inplace, old_running_memory[3])
            max_node_memory_saved_by_view = max(max_node_memory_saved_by_view,
                                                old_running_memory[4])

            # Store max of some stats with new order
            new_max_node_memory_size[0] = max(new_max_node_memory_size[0],
                                              sum(new_running_memory[0]))
            new_max_running_max_memory_size[0] = max(new_max_running_max_memory_size[0],
                                                     sum(new_running_memory[2]))

            # Separate CPU and GPU
            new_max_node_memory_size[1] = max(new_max_node_memory_size[1],
                                              new_running_memory[0][0])
            new_max_node_memory_size[2] = max(new_max_node_memory_size[2],
                                              new_running_memory[0][1])
            new_max_running_max_memory_size[1] = max(new_max_running_max_memory_size[1],
                                                     new_running_memory[2][0])
            new_max_running_max_memory_size[2] = max(new_max_running_max_memory_size[2],
                                                     new_running_memory[2][1])

            new_max_node_memory_saved_by_inplace = max(
                new_max_node_memory_saved_by_inplace, new_running_memory[3])
            new_max_node_memory_saved_by_view = max(new_max_node_memory_saved_by_view,
                                                    new_running_memory[4])

            # Config: whether print min memory peak
            if config.profiling.min_peak_memory:
                node_list = fgraph.apply_nodes
                ttt = time.time()
                min_peak = count_minimum_peak(node_list, fgraph, nodes_mem)
                min_peak_time += time.time() - ttt
                min_max_peak = max(min_max_peak, min_peak)

            del fgraph, nodes_mem

        if len(fct_memory) > 1:
            print >> file,  ("Memory Profile "
                             "(the max between all functions in that profile)")
        else:
            print >> file,  "Memory Profile"

        print >> file, "(Sparse variables are ignored)"
        print >> file, "(For values in brackets, it's for linker = c|py"

        print >> file,  "---"
#        print >> file,  "    Max if no gc, inplace and view: %dKB" % int(
#            round(max_sum_size / 1024))
        print >> file,  "    Max if no gc (allow_gc=False): %dKB (%dKB)" % (int(round(
            new_max_node_memory_size[0] / 1024.)), int(round(
                max_node_memory_size[0] / 1024.)))
        print >> file,  "    CPU: %dKB (%dKB)" % ((int(round(
            new_max_node_memory_size[1] / 1024.)), int(round(
                max_node_memory_size[1] / 1024.))))
        print >> file,  "    GPU: %dKB (%dKB)" % ((int(round(
            new_max_node_memory_size[2] / 1024.)), int(round(
                max_node_memory_size[2] / 1024.))))

        print >> file,  "---"

        print >> file,  "    Max if linker=cvm(default): %dKB (%dKB)" % (int(round(
            new_max_running_max_memory_size[0] / 1024.)), int(round(
                max_running_max_memory_size[0] / 1024.)))
        print >> file,  "    CPU: %dKB (%dKB)" % ((int(round(
            new_max_running_max_memory_size[1] / 1024.)), int(round(
                max_running_max_memory_size[1] / 1024.))))
        print >> file,  "    GPU: %dKB (%dKB)" % ((int(round(
            new_max_running_max_memory_size[2] / 1024.)), int(round(
                max_running_max_memory_size[2] / 1024.))))

        print >> file,  "---"

        if min_max_peak:
            print >> file,  "    Minimum peak from all valid apply node order is %dKB(took %.3fs to compute)" % (int(round(
                min_max_peak / 1024.)), min_peak_time)
        print >> file,  "    Memory saved if views are used: %dKB (%dKB)" % (int(
            round(new_max_node_memory_saved_by_view / 1024.)), int(
            round(max_node_memory_saved_by_view / 1024.)))
        print >> file,  "    Memory saved if inplace ops are used: %dKB (%dKB)" % \
            (int(round(new_max_node_memory_saved_by_inplace / 1024.)),
             int(round(max_node_memory_saved_by_inplace / 1024.)))
        print >> file,  "    Memory saved if gc is enabled: %dKB (%dKB)" % (int(
            round(new_max_node_memory_size[0] - new_max_running_max_memory_size[0]) / 1024.), int(
            round(max_node_memory_size[0] - max_running_max_memory_size[0]) / 1024.))

        print >> file,  "---"

        if (hasattr(theano, 'sandbox') and
            hasattr(theano.sandbox, 'cuda') and
            hasattr(theano.sandbox.cuda, 'cuda_ndarray') and
            hasattr(theano.sandbox.cuda.cuda_ndarray.cuda_ndarray,
                    'theano_allocated')):
            _, gpu_max = theano.sandbox.cuda.cuda_ndarray.cuda_ndarray.theano_allocated()
            print >> file,  ("    Max Memory allocated on the GPU "
                             "(for all functions): %dKB" %
                             int(round(gpu_max / 1024.)))

        print >> file, ""
        if len(fct_memory) > 1:
            print >> file,  (
                "    This list is based on all functions in the profile")
        print >> file,  ("    <Sum apply outputs (bytes)>"
                         " <Apply outputs shape>"
                         " <created/inplace/view>"
                         " <Apply node>")
        print >> file, ""
        items = node_mem.items()
        items.sort(key=lambda a: a[1], reverse=True)
        for idx, (node, node_outputs_size) in enumerate(items[:N]):
            code = ['c'] * len(node.outputs)
            for out, inp in getattr(node.op, 'destroy_map', {}).iteritems():
                code[out] = "i"
            for out, inp in getattr(node.op, 'view_map', {}).iteritems():
                code[out] = "v"
            shapes = str(fct_shapes[node.fgraph][node])

            if all([hasattr(out.type, 'get_size')
                    for out in node.outputs]):
                size = "%9dB" % node_outputs_size
                if node_outputs_size < config.profiling.min_memory_size:
                    N = idx
                    break
            else:
                size = "%10s" % "Unknown"

            print >> file,  '     %s  %s %s %s' % (size,
                                                   shapes,
                                                   ' '.join(code), node)

        sum_remaining = sum(size for _, size in items[N:])
        size_sum_dense = sum(node_mem.values())
        if size_sum_dense == 0:
            p = "0%"
        else:
            p = "(%.2f%%)" % (float(sum_remaining) / size_sum_dense * 100)
        print >> file,  (
            '   ... (remaining %i Apply account for %4dB/%dB (%s) of the'
            ' Apply with dense outputs sizes)') % (max(0, len(node_mem) - N),
                                                   sum_remaining,
                                                   size_sum_dense, p
                                                   )
        print >> file, ''
        if N == 0:
            print >> file, ('    All Apply nodes have output sizes that take'
                            ' less than %dB.' %
                            config.profiling.min_memory_size)
        print >> file,  (
            "    <created/inplace/view> is taken from the Op's declaration.")
        print >> file,  ("    Apply nodes marked 'inplace' or 'view' may"
                         " actually allocate memory, this is not reported"
                         " here. If you use DebugMode, warnings will be"
                         " emitted in those cases.")
        print >> file, ''

    def summary(self, file=sys.stderr, n_ops_to_print=20,
                n_apply_to_print=20):
        self.summary_function(file)
        local_time = sum(self.apply_time.values())
        if local_time > 0:
            self.summary_class(file, n_ops_to_print)
            self.summary_ops(file, n_ops_to_print)
            self.summary_nodes(file, n_apply_to_print)
        elif self.fct_callcount > 0:
            print >> file, ("  No execution time accumulated "
                            "(hint: try config profiling.time_thunks=1)")
        if self.variable_shape or self.variable_strides:
            self.summary_memory(file, n_apply_to_print)
        if self.optimizer_profile:
            print >> file, "Optimizer Profile"
            print >> file, "-----------------"
            self.optimizer_profile[0].print_profile(file,
                                                    self.optimizer_profile[1])


if 0:  # old code still to be ported from ProfileMode
    def long_print(self, file=sys.stderr, fct_name=None, message=None,
                   n_apply_to_print=15, n_ops_to_print=20, print_apply=False):
        """
        Print a readable summary of the stats.

        param: n_apply_to_print the number of apply to print. Default 15.

        param: n_ops_to_print the number of ops to print. Default 20.
        """
        local_time = sum(self.apply_time.values())

        print ''
        print 'ProfileMode.long_print()'
        print 'name = %s' % fct_name
        print 'msg = %s' % message
        print '---------------------------'
        print ''

        print 'Total time spent running thunks: %.3fs' % local_time

        sop_time = {}
        sop_call = {}
        sop_op = {}
        # map each op class to Bool. True iff all applies were done in c.
        sop_c = {}
        for a, t in op_time.items():
            typ = type(a)
            sop_time.setdefault(typ, 0)
            sop_time[typ] += t
            sop_op.setdefault(typ, 0)
            sop_op[typ] += 1
            sop_c.setdefault(typ, True)
            sop_c[typ] = sop_c[typ] and op_cimpl.get(a, False)
            sop_call[typ] = sop_call.get(typ, 0) + op_call[a]
        print '\nSingle Op-wise summary: <% of local_time spent on this kind of Op> <cumulative %%> <self seconds> <cumulative seconds> <time per call> <nb_call> <nb_op> <nb_op> <Op name>'
        sotimes = [(t * 100 / local_time, t, a, sop_c[a],
                    sop_call[a], sop_op[a]) for a, t in sop_time.items()]
        sotimes.sort(key=lambda t: (t[1], t[4], t[5]), reverse=True)
        tot = 0
        for f, t, a, ci, nb_call, nb_op in sotimes[:n_ops_to_print]:
            if nb_call == 0:
                assert t == 0
                continue
            tot += t
            ftot = tot * 100 / local_time
            if ci:
                msg = '*'
            else:
                msg = ' '
            print '   %4.1f%%  %5.1f%%  %5.3fs  %5.3fs  %.2es %s %5d %2d %s' % (f, ftot, t, tot, t / nb_call, msg, nb_call, nb_op, a)
        print '   ... (remaining %i Ops account for %.2f%%(%.2fs) of the runtime)'\
            % (max(0, len(sotimes) - n_ops_to_print),
               sum(f for f, t, a, ci, nb_call, nb_op in
                   sotimes[n_ops_to_print:]),
               sum(t for f, t, a, ci, nb_call, nb_op in
                   sotimes[n_ops_to_print:]))

        total_time = time.time() - import_time
        total_fct_time = sum(fct_call_time.values())
        total_fct_call = sum(fct_call.values())
        other_time = total_time - total_fct_time - compile_time
        print
        print 'Theano fct summary: <% total fct time> <total time> <time per call> <nb call> <fct name>'
        for key in fct_call.keys():
            if fct_call[key] > 0:
                print '   %4.1f%% %.3fs %.2es %d %s' % (
                    fct_call_time[key] / total_fct_time * 100,
                    fct_call_time[key],
                    fct_call_time[key] / fct_call[key],
                    fct_call[key], key.name)
            else:
                print '   NOT CALLED', key.name

        if total_fct_time > 0:
            time_pr_in_fct = local_time / total_fct_time * 100
            time_per_call = total_fct_time / total_fct_call
        else:
            time_pr_in_fct = 0
            time_per_call = 0

        print
        print 'Time since import %.3fs' % (total_time)
        print 'Compile time: %.3fs %.1f%%' % (compile_time,
                                              compile_time / total_time * 100)
        print 'Theano fct call %.3fs %.1f%%' % (total_fct_time,
                                                total_fct_time / total_time *
                                                100)
        print ('   Theano Op time (included in fct call, Time spent '
               'running thunks) %.3fs %.1f%%(of total) %.1f%%(of fct call)' %
               (local_time, local_time / total_time * 100, time_pr_in_fct))
        print 'Other time since import %.3fs %.1f%%' % (other_time, other_time / total_time * 100)
        print '%i Theano fct call, %.3fs per call' % (total_fct_call, time_per_call)

        print
        print "List of apply that don't have float64 as input but have float64 in outputs. Usefull to know if we forgot some cast when using floatX=float32 or gpu code."
        print '<Apply> <Apply position> <fct name> <inputs type> <outputs type>'
        for fct in fct_call.keys():
            for idx, node in enumerate(fct.maker.fgraph.toposort()):
                if any(hasattr(i, 'dtype') and i.dtype == 'float64' for i in node.outputs) and not any(hasattr(i, 'dtype') and i.dtype == 'float64' for i in node.inputs):
                    print str(node), idx, fct.name, str([getattr(i, 'dtype', None) for i in node.inputs]), str([getattr(i, 'dtype', None) for i in node.outputs])

        if any([x[2].__name__.startswith("Gpu") for x in sotimes]):
            cpu = []
            gpu = []
            trans = []
            for so in sotimes:
                if so[2].__name__ in ["HostFromGpu", "GpuFromHost"]:
                    trans.append(so)
                elif so[2].__name__.startswith("Gpu"):
                    gpu.append(so)
                else:
                    cpu.append(so)
            sum_cpu = sum(so[1] for so in cpu)
            sum_gpu = sum(so[1] for so in gpu)
            sum_trans = sum(so[1] for so in trans)
            print

            print "Spent %.3fs(%.3f%%) in cpu Op, %.3fs(%.3f%%) in gpu Op and %.3fs(%.3f%%) transfert Op" % (
                sum_cpu, sum_cpu / local_time * 100, sum_gpu, sum_gpu / local_time * 100, sum_trans, sum_trans / local_time * 100)

            print "Theano function input that are float64"
            print "<fct name> <input name> <input type> <str input>"
            for fct in fct_call.keys():
                for i in fct.input_storage:
                    if hasattr(i.type, 'dtype') and i.type.dtype == 'float64':
                        print fct.name, i.name, i.type, i

        print
        print "Here are tips to potentially make your code run faster (if you think of new ones, suggest them on the mailing list). Test them first as they are not guaranteed to always provide a speedup."
        from theano import tensor as T
        from theano.tensor.raw_random import RandomFunction
        import theano
        import theano.scalar as scal
        scalar_op_amdlibm_no_speed_up = [scal.LT, scal.GT, scal.LE, scal.GE, scal.EQ, scal.NEQ, scal.InRange, scal.Switch, scal.OR, scal.XOR, scal.AND, scal.Invert, scal.Maximum,
                                         scal.Minimum, scal.Add, scal.Mul, scal.Sub, scal.TrueDiv, scal.IntDiv, scal.Clip, scal.First, scal.Second, scal.Identity, scal.Cast, scal.Sgn, scal.Neg, scal.Inv, scal.Sqr]
        scalar_op_amdlibm_speed_up = [scal.Mod, scal.Pow, scal.Ceil, scal.Floor, scal.RoundHalfToEven, scal.RoundHalfAwayFromZero, scal.Log, scal.Log2, scal.Log10, scal.Log1p, scal.Exp,
                                      scal.Sqrt, scal.Abs, scal.Cos,  scal.Sin,  scal.Tan,  scal.Tanh,  scal.Cosh,  scal.Sinh, T.nnet.sigm.ScalarSigmoid, T.nnet.sigm.ScalarSoftplus]  # Abs, Mod in float{32,64} only

        def get_scalar_ops(s):
            if isinstance(s, theano.scalar.Composite):
                l = []
                for node in s.fgraph.toposort():
                    l += get_scalar_ops(node.op)
                return l
            else:
                return [s]

        def list_scalar_op(op):
            if isinstance(op.scalar_op, theano.scalar.Composite):
                return get_scalar_ops(op.scalar_op)
            else:
                return [op.scalar_op]

        def amdlibm_speed_up(op):
            if not isinstance(op, T.Elemwise):
                return False
            else:
                l = list_scalar_op(op)
                for s_op in l:
                    if s_op.__class__ in scalar_op_amdlibm_speed_up:
                        return True
                    elif s_op.__class__ not in scalar_op_amdlibm_no_speed_up:
                        print "We don't know if amdlibm will accelerate this scalar op.", s_op
                return False

        def exp_float32_op(op):
            if not isinstance(op, T.Elemwise):
                return False
            else:
                l = list_scalar_op(op)
                return any([s_op.__class__ in [scal.Exp] for s_op in l])

        # tip 1
        if config.floatX == 'float64':
            print "  - Try the Theano flag floatX=float32"

        # tip 2
        if not config.lib.amdlibm and any([amdlibm_speed_up(a.op) for i, a in apply_time]):
            print "  - Try installing amdlibm and set the Theano flag lib.amdlibm=True. This speed up only some Elemwise operation."

        # tip 3
        if not config.lib.amdlibm and any([exp_float32_op(a.op) and a.inputs[0].dtype == 'float32' for i, a in apply_time]):
            print "  - With the default gcc libm, exp in float32 is slower than in float64! Try Theano flags floatX=float64 or install amdlibm and set the theano flags lib.amdlibm=True"

        # tip 4
        for a, t in apply_time.iteritems():
            node = a
            if (isinstance(node.op, T.Dot) and
                    all([len(i.type.broadcastable) == 2 for i in node.inputs])):
                print ("  - You have a dot operation that was not optimized "
                       "to dot22 that is faster. Make sure the inputs are "
                       "float32 or float64 and are the same for both inputs. "
                       "Currently they are: %s" %
                       [i.type for i in node.inputs])

        # tip 5
        for a, t in apply_time.iteritems():
            node = a
            if isinstance(node.op, RandomFunction):
                print ("  - Replace the default random number generator by "
                       "'from theano.sandbox.rng_mrg import MRG_RandomStreams "
                       "as RandomStreams' as this is is faster. It is still "
                       "experimental, but seams to work correctly.")
                if config.device.startswith("gpu"):
                    print ("     - MRG_RandomStreams is the only random number"
                           " supported on the GPU.")
                break

    def print_summary(self,
                      n_apply_to_print=config.ProfileMode.n_apply_to_print,
                      n_ops_to_print=config.ProfileMode.n_ops_to_print):
        """
        Print 3 summaries that show where the time is spent. The first shows an
        Apply-wise summary, the second shows an Op-wise summary, the third
        shows an type-Op-wise summary.

        The Apply-wise summary print the timing information for the worst
        offending Apply nodes. This corresponds to individual Op applications
        within your graph which take the longest to execute (so if you use dot
        twice, you will see two entries there).

        The Op-wise summary print the execution time of all Apply nodes
        executing the same Op are grouped together and the total execution time
        per Op is shown (so if you use dot twice, you will see only one entry
        there corresponding to the sum of the time spent in each of them). If
        two Op have different hash value, they will be separate.

        The type-Op-wise summary group the result by type of op. So event if
        two Op have different hash value, they will be merged.

        There is a hack with the Op-wise summary. Go see it if you want to know
        more.

        :param n_apply_to_print: the number of apply to print. Default 15, or
            n_ops_to_print flag.

        :param n_ops_to_print: the number of ops to print. Default 20, or
            n_apply_to_print flag.
        """
        fct_call_time = self.mode.fct_call_time
        fct_call = self.mode.fct_call
        apply_time = self.apply_time
        op_cimpl = self.op_cimpl
        message = self.message
        outputs_size = self.outputs_size

        self.print_summary_("print_summary",
                            None,
                            None,
                            None,
                            apply_time,
                            op_cimpl,
                            message,
                            outputs_size,
                            n_apply_to_print,
                            n_ops_to_print)

    def print_diff_summary(self, other, n_apply_to_print=15,
                           n_ops_to_print=20):
        """
        As print_summary, but print the difference on two different profile
        mode.

        TODO: Also we don't print the Apply-wise summary as it doesn't work for
        now.
        TODO: make comparaison with gpu code.

        :param other: the other instance of ProfileMode that we want to be
            compared to.

        :param n_apply_to_print: the number of apply to print. Default 15.

        :param n_ops_to_print: the number of ops to print. Default 20.
        """

        def diff_dict(a_time, b_time_):
            r = {}
            b_time = copy.copy(b_time_)
            for a, ta in a_time.items():
                r.setdefault(a, 0)
                tb = b_time.pop(a, 0)
                r[a] += ta - tb

            # they are missing in a
            for a, t in b_time.items():
                r.setdefault(a, 0)
                r[a] += t
            return r

        compile_time = self.compile_time - other.compile_time
        fct_call_time = diff_dict(self.fct_call_time, other.fct_call_time)
        fct_call = diff_dict(self.fct_call, other.fct_call)
        apply_time = diff_dict(self.apply_time, other.apply_time)
        op_cimpl = self.op_cimpl and other.op_cimpl
        message = self.message
        outputs_size = diff_dict(self.outputs_size, other.outputs_size)

        self.print_summary_(
            "print_diff_summary", compile_time, fct_call_time, fct_call,
            apply_time, op_cimpl, message, outputs_size,
            n_apply_to_print=n_apply_to_print,
            n_ops_to_print=n_ops_to_print, print_apply=False)


class ScanProfileStats(ProfileStats):
    callcount = 0.0
    nbsteps = 0.0
    call_time = 0.0

    def __init__(self, atexit_print=True, name=None, **kwargs):
        super(ScanProfileStats, self).__init__(atexit_print, **kwargs)
        self.name = name

    def summary_function(self, file):
        # RP: everytime we compile a function a ProfileStats is created for
        # that function. This means that everytime a optimization replaces
        # some scan op, some orphane ProfileStats remains in the air ..
        # also even without any optimization, scan compiles a dummy function
        # that will produce a ProfileStats that will correspond to a
        # function that will never be called. Printing several empty
        # Function profiling is just extremely confusing
        if self.callcount == 0:
            return
        print >> file, ''

        if self.name is not None:
            print >> file, 'Scan Op profiling (', self.name, ')'
        else:
            print >> file, 'Scan Op profiling'
        print >> file, '=================='
        print >> file, '  Message: %s' % self.message

        print >> file, ('  Time in %i calls of the op (for a total of %i '
                        'steps) %es' %
                        (self.callcount, self.nbsteps, self.call_time))
        print >> file, ''
        val = 0
        if self.call_time > 0:
            val = self.vm_call_time * 100 / self.call_time
        print >> file, '  Total time spent in calling the VM %es (%.3f%%)' % (
            self.vm_call_time, val)
        val = 100
        if self.call_time > 0:
            val = 100. - self.vm_call_time * 100 / self.call_time
        print >> file, '  Total overhead (computing slices..) %es (%.3f%%)' % (
            self.call_time - self.vm_call_time, val)
        print >> file, ''
