import os

import numpy
import onnx





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     Tuple:






      '''simple docstring'''





      UpperCAmelCase			:       Union[str, Any]			     =a.name
      UpperCAmelCase			:       int			     =b.name

      UpperCAmelCase			:       Any			     =""
      UpperCAmelCase			:       Tuple			     =""

      UpperCAmelCase			:       List[Any]			     =a == b

      UpperCAmelCase			:       Any			     =name_a
      UpperCAmelCase			:       Optional[int]			     =name_b

      return res





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase  )->     Dict:






      '''simple docstring'''





      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      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase  )->     Tuple:






      '''simple docstring'''





      for n in graph_proto.node:
            _node_replace_input_with(_UpperCAmelCase						,    _UpperCAmelCase						,    _UpperCAmelCase  )





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase  )->     Optional[int]:






      '''simple docstring'''





      UpperCAmelCase			:       Optional[int]			     =list(model.graph.initializer  )
      UpperCAmelCase			:       Optional[Any]			     =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			:       Optional[Any]			     =inits[i].name
            UpperCAmelCase			:       Union[str, Any]			     =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      lowerCAmelCase_       ( __lowerCAmelCase  )->     Dict:






      '''simple docstring'''





      UpperCAmelCase			:       Union[str, Any]			     =os.path.dirname(_UpperCAmelCase  )
      UpperCAmelCase			:       Any			     =os.path.basename(_UpperCAmelCase  )

      UpperCAmelCase			:       int			     =onnx.load(os.path.join(_UpperCAmelCase						,    _UpperCAmelCase  )  )

      UpperCAmelCase			:       Any			     =list(model.graph.initializer  )

      UpperCAmelCase			:       Optional[Any]			     =set()
      UpperCAmelCase			:       Any			     ={}
      UpperCAmelCase			:       Any			     =[]

      UpperCAmelCase			:       int			     =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			:       List[str]			     =inits[j].data_type
                        UpperCAmelCase			:       Any			     =numpy.prod(inits[j].dims  )
                        if dtype == 1:
                              mem_size *= 4
                        elif dtype == 6:
                              mem_size *= 4
                        elif dtype == 7 or dtype == 11:
                              mem_size *= 8
                        else:
                              print('''unexpected data type: '''						,    _UpperCAmelCase  )
                        total_reduced_size += mem_size

                        UpperCAmelCase			:       Optional[int]			     =inits[i].name
                        UpperCAmelCase			:       Optional[int]			     =inits[j].name

                        if name_i in dup_map:
                              dup_map[name_i].append(_UpperCAmelCase  )
                        else:
                              UpperCAmelCase			:       List[Any]			     =[name_j]
                        ind_to_replace.append((j, i)  )

      print('''total reduced size: '''						,    total_reduced_size / 10_24 / 10_24 / 10_24						,    '''GB'''  )

      UpperCAmelCase			:       List[Any]			     =sorted(_UpperCAmelCase  )
      _remove_dup_initializers_from_model(_UpperCAmelCase						,    _UpperCAmelCase						,    _UpperCAmelCase  )

      UpperCAmelCase			:       Union[str, Any]			     ="optimized_" + model_file_name
      UpperCAmelCase			:       Union[str, Any]			     =os.path.join(_UpperCAmelCase						,    _UpperCAmelCase  )
      onnx.save(_UpperCAmelCase						,    _UpperCAmelCase  )

      return new_model



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]   	=						{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any   	=						[
						    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """WavLMForAudioFrameClassification""",
						    """WavLMForCTC""",
						    """WavLMForSequenceClassification""",
						    """WavLMForXVector""",
						    """WavLMModel""",
						    """WavLMPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase							:  Union[str, Any]   	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






