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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import copy
import logging
import os
import re
import six

from builtins import bytes
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace

from .proto.graph_pb2 import GraphDef
from .proto.node_def_pb2 import NodeDef
from .proto.tensor_shape_pb2 import TensorShapeProto

def _make_unique_name(seen, name, min_version=0):
    '''
    Make the name unique by appending a unique number to the name. Used for SSA.

Args:
        seen (set): Set of names that have already been used (with respect to
            some context).
        name (string): The name to make unique
        min_version (number): Starting index. Is incremented continually until
            it can make the resulting name unique relative to 'seen'.

Returns:
        x (string): A version of name that is not in seen.
    '''
    assert name is not None
    i = min_version
    x = '%s_%d' % (name, i) if i else name
    while x in seen:
        i += 1
        x = '%s_%d' % (name, i)
    seen.add(x)
    return x

def _rename_tensorflow_style(shapes, blob_name_tracker, ops):
    '''
    Convert some of the common names in Caffe2 to tensorflow.
    NOTE: The common names in both Caffe2 and Tensorflow are currently
        hardcoded, if either side changes at some point, then this code should
        change as well.

Returns:
        None. The _rename_all() call modifies blob_name_tracker and ops in-place.
    '''
    WEIGHT = re.compile(r"(_w)$")
    WEIGHT_ = re.compile(r"(_w_)")
    BN = re.compile(r"(_bn)$")
    BN_ = re.compile(r"(_bn_)")
    BIAS = re.compile(r"(_b)$")
    BIAS_ = re.compile(r"(_b_)")
    SCALE = re.compile(r"(_s)$")
    SCALE_ = re.compile(r"(_s_)")
    SUM = re.compile(r"(_sum)$")
    SUM_ = re.compile(r"(_sum_)")
    BRANCH = re.compile(r"(_branch)")

def f(name):
        inter_name = WEIGHT_.sub('/weight_', WEIGHT.sub('/weight', name))
        inter_name = BN_.sub('/batchnorm_', BN.sub('/batchnorm', inter_name))
        inter_name = BIAS_.sub('/bias_', BIAS.sub('/bias', inter_name))
        inter_name = SCALE_.sub('/scale_', SCALE.sub('/scale', inter_name))
        inter_name = SUM_.sub('/sum_', SUM.sub('/sum', inter_name))
        new_name = BRANCH.sub('/branch', inter_name)
        return new_name
    _rename_all(shapes, blob_name_tracker, ops, f)

def _convert_to_ssa(shapes, blob_name_tracker, ops):
    '''
    Convert an operator graph to SSA (i.e. out-of-place).
    i.e. blobs will be renamed so that each blob is produced only once.

Returns:
        None. Modifies blob_name_tracker and ops in-place.
    '''
    ir = core.IR(ops)
    seen = set()
    versioned = {}
    new_shapes = {}
    new_blob_name_tracker = {}

def ssa_name(name, versions):
        assert name in versions
        version = versions[name]
        if (name, version) in versioned:
            return versioned[(name, version)]
        # Always setting name2 = `{name}_{version}` would work, but we also try
        # to avoid a trailing `_0`, so we have to be careful not to introduce
        # name collisions, such as (foo_1, 0) = foo_1 = (foo, 1).
        # Note: operator names (if any) will be handled later.
        new_name = _make_unique_name(seen, name, min_version=version)
        versioned[(name, version)] = new_name
        # Transfer shape.
        if name in shapes:
            new_shapes[new_name] = shapes[name]
        if blob_name_tracker and name in blob_name_tracker:
            new_blob_name_tracker[new_name] = blob_name_tracker[name]
        return new_name

for (op, ssa) in zip(ops, ir.ssa):
        assert op is ssa.op
        inputs = list(op.input)
        outputs = list(op.output)
        del op.input[:]
        del op.output[:]
        op.input.extend(ssa_name(name, ssa.in_versions) for name in inputs)
        op.output.extend(ssa_name(name, ssa.out_versions) for name in outputs)

shapes.clear()
    shapes.update(new_shapes)
    if blob_name_tracker:
        blob_name_tracker.clear()
        blob_name_tracker.update(new_blob_name_tracker)

def _get_blob_names(ops):
    '''
    Get all the operator input and output blobs and perform dedup on their names.

Args:
        ops: List of Caffe2 operators to extract inputs and outputs from

Returns:
        set containing distinct inputs and outputs from 'ops'
    '''
    names = set()
    for op in ops:
        names.update(op.input)
        names.update(op.output)
    return {name: name for name in names}

def _remap_keys(old_dict, rename_fn):
    '''
    Rename keys of 'old_dict' according to 'rename_fn'.

Args:
        old_dict: Dictionary (i.e. containing blob_name -> blob_name
            relationships.)
        remap_fn: Function string -> string for renaming.

Returns:
        None. Modifies old_dict in-place.
    '''
    new_dict = {rename_fn(key): value for key,
                value in six.iteritems(old_dict)}
    old_dict.clear()
    old_dict.update(new_dict)

def _rename_all(shapes, blob_name_tracker, ops, rename_fn):
    '''
    Rename all the names in the operators.

Args:
        shapes: Dictionary mapping blob names to their shapes/dimensions.
        blob_name_tracker: Dictionary of all unique blob names (with respect to
            some context).
        ops: List of Caffe2 operators
        rename_fn: Function string -> string that specifies how to rename

Returns:
        None. Modifies shapes, blob_name_tracker and ops in-place using the
            specified 'rename_fn'.
    '''
    seen = set()
    renamed = {}

def g(name):
        """ Collision-free version of f.
        """
        if name is None:
            return None
        if name in renamed:
            return renamed[name]
        new_name = _make_unique_name(seen, rename_fn(name))
        renamed[name] = new_name
        return new_name

for op in ops:
        inputs = list(op.input)
        outputs = list(op.output)
        del op.input[:]
        del op.output[:]
        op.input.extend(g(name) for name in inputs)
        op.output.extend(g(name) for name in outputs)

_remap_keys(shapes, g)
    if blob_name_tracker:
        _remap_keys(blob_name_tracker, g)
    # Rename all operator names (if any) independently so that the
    # unique-fication happens only once in _fill_missing_operator_names().
    seen.clear()
    renamed.clear()
    for op in ops:
        op.name = g(op.name)

def _add_gradient_scope(shapes, blob_name_tracker, ops):
    """
    For all operators or blobs with name containing "_grad", add a
    "GRADIENTS/" scope.
    Note: breaks graph execution since the blob -> gradient mapping is
    hardcoded.

Returns:
        None. Modifies shapes, blob_name_tracker and ops in-place by renaming.
    """
    def f(name):
        if '_grad' in name:
            return 'GRADIENTS/{}'.format(name)
        else:
            return name
    _rename_all(shapes, blob_name_tracker, ops, f)

def _replace_colons(shapes, blob_name_tracker, ops, repl):
    '''
    `:i` has a special meaning in Tensorflow. This function replaces all colons
    with $ to avoid any possible conflicts.

Args:
        shapes: Dictionary mapping blob names to their shapes/dimensions.
        blob_name_tracker: Dictionary of all unique blob names (with respect to
            some context).
        ops: List of Caffe2 operators
        repl: String representing the text to replace ':' with. Usually this is
            '$'.

Returns:
        None. Modifies blob_name_tracker in-place.

'''
    def f(name):
        return name.replace(':', repl)
    _rename_all(shapes, blob_name_tracker, ops, f)

def _fill_missing_operator_names(ops):
    '''
    Give missing operators a name.
    We expect C2 operators to be generally unnamed. This gives them a scope
    (inferred from their outputs) and a name after their type. Duplicates will
    be postfixed by an index.

Args:
        ops: List of Caffe2 operators to assign names to.

Returns:
        None: Modifies 'ops' in-place.
    '''
    seen = set()
    for op in ops:
        # Make sure operator names don't collide with blobs.
        seen.update(op.input)
        seen.update(op.output)
    for op in ops:
        if op.name:
            name = op.name
        elif op.output or op.input:
            name_list = [os.path.dirname(name)
                         for name in op.output or op.input]
            scope = os.path.commonprefix(name_list)
            name = os.path.join(scope, op.type)
        else:
            name = op.type
        assert(name)
        op.name = _make_unique_name(seen, name)

def _tf_device(device_option):
    '''
    Handle the devices.

Args:
        device_option (caffe2_pb2.DeviceOption): DeviceOption protobuf,
            associated to an operator, that contains information such as
            device_type (optional), cuda_gpu_id (optional), node_name (optional,
            tells which node the operator should execute on). See caffe2.proto
            in caffe2/proto for the full list.

Returns:
        Formatted string representing device information contained in
            device_option.
    '''
    if not device_option.HasField("device_type"):
        return ""
    if device_option.device_type == caffe2_pb2.CPU or device_option.device_type == caffe2_pb2.MKLDNN:
        return "/cpu:*"
    if device_option.device_type == caffe2_pb2.CUDA:
        return "/gpu:{}".format(device_option.device_id)
    raise Exception("Unhandled device", device_option)

def _add_tf_shape(attr_dict, ints):
    '''
    Converts a list of ints to a TensorShapeProto representing the dimensions of
    a blob/object.

Args:
        attr_dict: Dictionary to update (usually attributes of a Node)
        ints: List of integers representing dimensions of some object.

Returns:
        None. Modifies attr_dict in-place.
    '''
    shape_proto = TensorShapeProto()
    for i in ints:
        dim = TensorShapeProto.Dim()
        dim.size = i
        shape_proto.dim.extend([dim])
    attr_dict['_output_shapes'].list.shape.extend([shape_proto])

def _set_tf_attr(attr_dict, arg):
    '''
    Add attributes to a node. Key is the arg.name, and values can be shape,
        floats, strings, ints or an empty list.

Args:
        attr_dict: Dictionary to update (usually attributes of a Node)
        arg: Object with name and data fields.

Returns:
        None. Modifies attr_dict in-place.
    '''
    k = arg.name
    if k == 'shape' and arg.ints:
        _add_tf_shape(attr_dict, arg.ints)
        return
    # Float
    if arg.HasField("f"):
        attr_dict[k].f = arg.f
        return
    # Integer
    if arg.HasField("i"):
        attr_dict[k].i = arg.i
        return
    # String
    if arg.HasField("s"):
        attr_dict[k].s = (
            arg.s if isinstance(arg.s, bytes) else str(arg.s).encode('utf-8')
        )
        return
    if arg.floats:
        attr_dict[k].list.f.extend(arg.floats)
        return
    if arg.ints:
        attr_dict[k].list.i.extend(arg.ints)
        return
    if arg.strings:
        attr_dict[k].list.s.extend(
            s if isinstance(s, bytes) else str(s).encode('utf-8')
            for s in arg.strings
        )
        return
    # The value is an empty list.
    attr_dict[k].list.s.extend([])

def _operator_to_node(shapes, op):
    '''
    Converts an operator to a node in a TF graph.

Args:
        shapes: Dictionary mapping blob names to their shapes/dimensions.
        op: The Caffe2 operator to convert to a TF graph node.

Returns:
        n: The TF graph node created from op.
    '''
    assert op.name, op
    n = NodeDef()
    n.name = op.name
    n.input.extend(op.input)
    n.op = op.type
    n.device = _tf_device(op.device_option)
    if shapes:
        # Add shapes in order.
        for output in op.output:
            if output not in shapes:
                break
            _add_tf_shape(n.attr, shapes[output])
    for arg in op.arg:
        _set_tf_attr(n.attr, arg)
    return n

def _operator_to_node_simp(op, inter_blobs, seen):
    '''
    Convert the operators to nodes.

Args:
        op: Caffe2 operator to convert to node
        inter_blobs: Set of intermediate blobs
        seen: Names that have already been used and are not unique

Returns:
        nodes: Nodes representing 'op' and the outputs of 'op'
    '''
    assert op
    nodes = []
    outputs = [o for o in op.output if o not in inter_blobs]
    seen.update(outputs)
    len_outputs = len(outputs)
    if len_outputs == 1:
        n = NodeDef()
        n.name = outputs[0]
        # Here we are sure the name is unique.
        n.input.extend(op.input)
        n.op = op.type
        n.device = _tf_device(op.device_option)
        for arg in op.arg:
            _set_tf_attr(n.attr, arg)
        nodes.append(n)
    elif len_outputs > 1:
        # Create a name that is likely unique
        if op.name:
            name = op.name
        else:
            name_list = [name for name in outputs]
            scope = os.path.commonprefix(name_list)
            name = os.path.join(scope, op.type)
        assert(name)
        op.name = _make_unique_name(seen, name)
        device = _tf_device(op.device_option)

# Create additional output nodes
        for output in outputs:
            n = NodeDef()
            n.name = output
            n.input.extend([op.name])
            n.op = 'Blob'
            n.device = device
            nodes.append(n)

# Node for the current op
        n = NodeDef()
        n.name = op.name
        n.input.extend(op.input)
        n.op = op.type
        n.device = device
        for arg in op.arg:
            _set_tf_attr(n.attr, arg)
        nodes.append(n)

return nodes

def _blob_to_node(producing_ops, shapes, name):
    '''
    Converts a blob (operator input or output) to a node in a TF graph.

Args:
        producing_ops: Dictionary of blob name to list of
            (producing_op, blob_index within producing_op.output) mapping.
        shapes: Dictionary mapping blob names to their shapes/dimensions.
        name: String representing the name of this blob.

Returns:
        n: The TF graph node created from this blob.
    '''
    assert name
    n = NodeDef()
    n.name = name
    # Get all ops that have the blob corresponding to 'name' as one of their
    # outputs. See _operators_to_graph_def.
    produced_by = producing_ops.get(name, [])
    if len(produced_by) > 0:
        n.op = 'Blob'
    else:
        # This blob is not produced but is instead a TF Placeholder where a
        # value is passed in.
        n.op = 'Placeholder'
    n.input.extend('%s:%d' % (p_op.name, i) for p_op, i in produced_by)
    if produced_by:
        device = produced_by[0][0].device_option
        if (all(producer[0].device_option == device for producer in produced_by)):
            n.device = _tf_device(device)
    if shapes and name in shapes:
        _add_tf_shape(n.attr, shapes[name])
    return n

def _clear_debug_info(ops, perform_clear):
    '''
    Removes debug information from operators, they are copious.

Args:
        ops: List of Caffe2 operators
        perform_clear: Boolean passed from _operators_to_graph_def specifying
            whether to remove the debug information. This boolean is passed into
            this function to reduce the complexity of _operators_to_graph_def.

Returns:
        None. Modifies the list of Caffe2 operators in-place and removes the
        'debug_info' field.

'''
    if not perform_clear:
        return

for op in ops:
        if op.HasField('debug_info'):
            op.ClearField('debug_info')

def _check_if_forward(blob):
    '''
    Blobs with names containing '_m' or 'grad' are part of the backward pass.
        This function references facebookresearch/Detectron/detectron/utils/net.py.

Args:
        blob: The blob to inspect

Returns:
        Boolean representing whether this blob is part of the forward pass
    '''
    #
    return (blob.find('__m') < 0 or blob.find('grad') < 0)

def _check_if_cpu(blob):
    '''
    Check if the blob's name starts with '_gpu'.

Args:
        blob: The blob to inspect

Returns:
        Boolean representing whether this blob is associated with a gpu
    '''
    return not blob.startswith('_gpu')

def _compute_in_out(ops):
    '''
    Find the input, intermediate and output nodes of a set of operators.

Args:
        ops: List of Caffe2 operators to look through

Returns:
        input_blobs: The input nodes of the set of operators
        inter_blobs: The intermediate nodes of the set of operators
        output_blobs: The output nodes of the set of operators
    '''
    in_blobs = set()
    out_blobs = set()

for op in ops:
        for input_blob in op.input:
            in_blobs.add(input_blob)
        for output_blob in op.output:
            out_blobs.add(output_blob)

input_blobs = list(in_blobs.difference(out_blobs))
    output_blobs = list(out_blobs.difference(in_blobs))
    inter_blobs = {b for b in output_blobs if b.startswith('_')}
    output_blobs = [b for b in output_blobs if b not in inter_blobs]

return input_blobs, inter_blobs, output_blobs

def _filter_ops(ops, filter_fn, perform_filter):
    '''
    Filter unwanted operators based on criteria in 'filter_fn'.

Args:
        ops: List of Caffe2 operators to filter
        filter_fn: Criteria function for whether inputs/outputs in an operator
            should be filtered.
        perform_filter: Boolean passed from _operators_to_graph_def specifying
            whether to filter operators

Returns:
        new_ops: Subset of ops containing a subset of their inputs and outputs.
    '''
    if not perform_filter:
        return ops

new_ops = []
    for op in ops:
        inputs = list(op.input)
        outputs = list(op.output)
        del op.input[:]
        del op.output[:]
        new_inputs = [i for i in inputs if filter_fn(i)]
        new_outputs = [o for o in outputs if filter_fn(o)]

# Only add the op if output is not empty
        if new_outputs:
            op.input.extend(new_inputs)
            op.output.extend(new_outputs)
            new_ops.append(op)

return new_ops

def _operators_to_graph_def(
    shapes,
    ops,
    colon_replacement='$',
    with_ssa=True,
    with_gradient_scope=True,
    blob_name_tracker=None,
    show_simplified=False,
    custom_rename=None
):
    '''
    Main function to convert set of operators to a graph.

Args:
        shapes: Dictionary mapping blob names to their shapes/dimensions.
        ops: List of Caffe2 operators, representing some computation graph
        ### **kwargs (model_to_graph_def, nets_to_graph_def, protos_to_graph_def) ###
        colon_replacement: Symbol to replace ':' with. ':i' in TF has a special
            meaning, so we need to replace it with a non-conflicting symbol.
        with_ssa: Boolean
        with_gradient_scope: Boolean
        blob_name_tracker: Dictionary tracking names of blobs (inputs/outputs
            from operators)
        show_simplified: Whether to show a simplified version of the model graph
            Sets all of the following values:
                clear_debug_info: Boolean representing whether to silence debug
                    info (which can be very verbose)
                show_forward_only: Boolean representing whether to only show
                    blobs involved in the forward pass
                show_cpu_only: Boolean representing whether to only show blobs
                    that are not associated with a gpu
                use_tensorflow_naming: Boolean representing whether to convert
                    some common Caffe2 naming conventions to their Tensorflow
                    counterparts
        custom_rename: Function string -> string that defines a custom
            renaming function to use.

Returns:
        current_graph: GraphDef representing the computation graph formed by the
            set of operators.
    '''
    if blob_name_tracker is not None:
        blob_name_tracker.clear()
    else:
        blob_name_tracker = {}

blob_name_tracker.update(_get_blob_names(ops))

_clear_debug_info(ops, show_simplified)  # clear_debug_info
    ops = _filter_ops(ops, _check_if_forward,
                      show_simplified)  # show_forward_only
    ops = _filter_ops(ops, _check_if_cpu, show_simplified)  # show_cpu_only
    if custom_rename:
        _rename_all(shapes, blob_name_tracker, ops, custom_rename)
    if colon_replacement:
        _replace_colons(shapes, blob_name_tracker, ops, colon_replacement)
    if with_ssa:
        _convert_to_ssa(shapes, blob_name_tracker, ops)
    if with_gradient_scope:
        _add_gradient_scope(shapes, blob_name_tracker, ops)
    _fill_missing_operator_names(ops)
    if show_simplified:  # use_tensorflow_naming
        _rename_tensorflow_style(shapes, blob_name_tracker, ops)
    producing_ops = {}
    blobs = set()
    input_blobs, inter_blobs, _ = _compute_in_out(ops)
    current_graph = GraphDef()
    seen = set(input_blobs)
    for op in ops:
        nodes_from_op = _operator_to_node_simp(op, inter_blobs, seen) if \
            show_simplified else \
            [_operator_to_node(shapes, op)]  # .extend() expects an iterable
        current_graph.node.extend(nodes_from_op)
        for input_blob in op.input:
            blobs.add(input_blob)
        for i, output_blob in enumerate(op.output):
            blobs.add(output_blob)
            producing_ops.setdefault(output_blob, []).append((op, i))

if show_simplified:
        # Show a cleaner, easier-to-interpret version of the model graph
        blobs = input_blobs

for blob in blobs:
        current_graph.node.extend([_blob_to_node(producing_ops, {}, blob)])

return current_graph

def _propagate_device_option(net_def):
    '''
    Propagate the device options from net to operators.

Args:
        net_def: A caffe2_pb2.NetDef representing a computation graph. The graph
            consists of Caffe2 operators.

Returns:
        None. Iterates through all ops contained within the net. For each op,
            modifies the op device_option in-place to be the net device_option
            if the op has no pre-existing device_option, and leaves the op as-is
            if it already has a device_option.
    '''
    if not net_def.HasField("device_option"):
        return
    for op in net_def.op:
        if not op.HasField("device_option"):
            op.device_option.CopyFrom(net_def.device_option)

def _try_get_shapes(nets):
    '''
    Get missing shapes for all blobs contained in the nets.

Args:
        nets: List of core.Net to extract blob shape information from.

Returns:
        Dictionary containing blob name to shape/dimensions mapping. The net
            is a computation graph that is composed of operators, and the
            operators have input and output blobs, each with their own dims.
    '''
    try:
        # Note: this will inspect the workspace for better or worse.
        # We don't care about the types, only the shapes
        shapes, _ = workspace.InferShapesAndTypes(nets)
        return shapes
    except Exception as e:
        logging.warning('Failed to compute shapes: %s', e)
        return {}

def model_to_graph_def(model, **kwargs):
    '''
    Convert a Caffe2 model to a Tensorflow graph. This function extracts
    'param_init_net' and 'net' from the model and passes it to nets_to_graph()
    for further processing.

Args:
        model (cnn.CNNModelHelper, model_helper.ModelHelper): The model to
            extract the nets (instances of core.Net) from.

Returns:
        Call to nets_to_graph_def() with extracted 'param_init_net', 'net' and
            **kwargs. See _operators_to_graph_def for detailed **kwargs.
    '''
    nets = [model.param_init_net, model.net]
    return nets_to_graph_def(nets, **kwargs)

def nets_to_graph_def(nets, shapes=None, **kwargs):
    '''
    Convert a set of Caffe2 nets to a Tensorflow graph.

Args:
        nets: List of core.Nets. core.Net is a wrapper around a NetDef protobuf.
            The corresponding protobuf can be extracted using .Proto().
        shapes: Dictionary mapping blob names to their shapes/dimensions.

Returns:
        Call to protos_to_graph_def() with the extracted NetDef protobufs and
            **kwargs. See _operators_to_graph_def for detailed **kwargs.
    '''
    # if shapes is None:
    #     shapes = _try_get_shapes(nets)
    # _try_get_shapes(nets) depends on workspace.InferShapesAndTypes(nets),
    # which is currently broken (segfault). We omit the shapes for now.
    shapes = {}
    nets = [copy.deepcopy(net.Proto()) for net in nets]
    shapes = copy.deepcopy(shapes)
    return protos_to_graph_def(nets, shapes, **kwargs)

def protos_to_graph_def(net_defs, shapes=None, **kwargs):
    '''
    Convert a set of Caffe2 net definitions to a Tensorflow graph.

Args:
        net_defs: List of caffe2_pb2.NetDef protobufs representing computation
            graphs.
        shapes: Dictionary mapping blob names to their shapes/dimensions.

Returns:
        Call to _operators_to_graph_def() with the extracted operators from the
            NetDefs and **kwargs. See _operators_to_graph_def for detailed
            **kwargs.
    '''
    for net in net_defs:
        _propagate_device_option(net)
    shapes = copy.deepcopy(shapes or {})
    ops = [op for net_def in net_defs for op in net_def.op]
    return _operators_to_graph_def(shapes, ops, **kwargs)

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