






"""simple docstring"""




import os

import numpy
import onnx

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Any:
  UpperCamelCase_: Union[str, Any]   				=    a.name
  UpperCamelCase_: Any   				=    b.name

  UpperCamelCase_: str   				=    ''
  UpperCamelCase_: Tuple   				=    ''

  UpperCamelCase_: Dict   				=    a == b

  UpperCamelCase_: List[str]   				=    name_a
  UpperCamelCase_: int   				=    name_b

  return res

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Dict:
  for i, input_name in enumerate(node_proto.input  ):
    if input_name == name:
      node_proto.input.insert(UpperCAmelCase__     ,   UpperCAmelCase__  )
      node_proto.input.pop(i + 1  )

  if node_proto.op_type == "If":
    _graph_replace_input_with(node_proto.attribute[0].g     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
    _graph_replace_input_with(node_proto.attribute[1].g     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  if node_proto.op_type == "Loop":
    _graph_replace_input_with(node_proto.attribute[0].g     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Dict:
  for n in graph_proto.node:
    _node_replace_input_with(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: List[Any]   				=    list(model.graph.initializer  )
  UpperCamelCase_: Optional[int]   				=    list(model_without_ext.graph.initializer  )
  for i, ref_i in ind_to_replace:
    assert inits_with_data[i].name == inits[i].name
    assert inits_with_data[ref_i].name == inits[ref_i].name
    assert i > ref_i

    UpperCamelCase_: Any   				=    inits[i].name
    UpperCamelCase_: int   				=    inits[ref_i].name

    model_without_ext.graph.initializer.remove(inits[i]  )

    # for n in model.graph.node:
    _graph_replace_input_with(model_without_ext.graph     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__  )	->    Tuple:
  UpperCamelCase_: Optional[Any]   				=    os.path.dirname(UpperCAmelCase__  )
  UpperCamelCase_: Optional[Any]   				=    os.path.basename(UpperCAmelCase__  )

  UpperCamelCase_: Any   				=    onnx.load(os.path.join(UpperCAmelCase__     ,   UpperCAmelCase__  )  )

  UpperCamelCase_: Tuple   				=    list(model.graph.initializer  )

  UpperCamelCase_: Optional[Any]   				=    set()
  UpperCamelCase_: str   				=    {}
  UpperCamelCase_: Any   				=    []

  UpperCamelCase_: List[str]   				=    0

  for i in range(len(UpperCAmelCase__  )  ):
    if i in dup_set:
      continue

    for j in range(i + 1     ,   len(UpperCAmelCase__  )  ):
      if j in dup_set:
        continue
      if _is_equal_tensor_proto(inits[i]     ,   inits[j]  ):
        dup_set.add(UpperCAmelCase__  )
        dup_set.add(UpperCAmelCase__  )

        UpperCamelCase_: int   				=    inits[j].data_type
        UpperCamelCase_: Optional[int]   				=    numpy.prod(inits[j].dims  )
        if dtype == 1:
          mem_size *= 4
        elif dtype == 6:
          mem_size *= 4
        elif dtype == 7 or dtype == 1_1:
          mem_size *= 8
        else:
          print('unexpected data type: '     ,   UpperCAmelCase__  )
        total_reduced_size += mem_size

        UpperCamelCase_: Tuple   				=    inits[i].name
        UpperCamelCase_: int   				=    inits[j].name

        if name_i in dup_map:
          dup_map[name_i].append(UpperCAmelCase__  )
        else:
          UpperCamelCase_: Dict   				=    [name_j]
        ind_to_replace.append((j, i)  )

  print('total reduced size: '     ,   total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4     ,   'GB'  )

  UpperCamelCase_: Union[str, Any]   				=    sorted(UpperCAmelCase__  )
  _remove_dup_initializers_from_model(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )

  UpperCamelCase_: Tuple   				=    'optimized_' + model_file_name
  UpperCamelCase_: Optional[Any]   				=    os.path.join(UpperCAmelCase__     ,   UpperCAmelCase__  )
  onnx.save(UpperCAmelCase__     ,   UpperCAmelCase__  )

  return new_model







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_		:		Tuple		     =			{
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			['FunnelTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			[
       'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FunnelBaseModel',
       'FunnelForMaskedLM',
       'FunnelForMultipleChoice',
       'FunnelForPreTraining',
       'FunnelForQuestionAnswering',
       'FunnelForSequenceClassification',
       'FunnelForTokenClassification',
       'FunnelModel',
       'FunnelPreTrainedModel',
       'load_tf_weights_in_funnel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		str		     =			[
       'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFunnelBaseModel',
       'TFFunnelForMaskedLM',
       'TFFunnelForMultipleChoice',
       'TFFunnelForPreTraining',
       'TFFunnelForQuestionAnswering',
       'TFFunnelForSequenceClassification',
       'TFFunnelForTokenClassification',
       'TFFunnelModel',
       'TFFunnelPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
   from .tokenization_funnel import FunnelTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_funnel_fast import FunnelTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_funnel import (
          FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
          FunnelPreTrainedModel,
          load_tf_weights_in_funnel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_funnel import (
          TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFunnelBaseModel,
          TFFunnelForMaskedLM,
          TFFunnelForMultipleChoice,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForSequenceClassification,
          TFFunnelForTokenClassification,
          TFFunnelModel,
          TFFunnelPreTrainedModel,
      )

else:
   import sys

   A_		:		Any		     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)