





'''simple docstring'''






import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase						: Tuple		=   '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCamelCase						: int		=   [
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )     ->   int:


  """simple docstring"""

  _UpperCamelCase       			=SavedModel()
  _UpperCamelCase       			=[]

  with open(os.path.join(a__  ,      '''utils'''  ,      '''tf_ops'''  ,      '''onnx.json'''   )   ) as f:
    _UpperCamelCase       			=json.load(a__   )['''opsets''']

  for i in range(1  ,      opset + 1   ):
    onnx_ops.extend(onnx_opsets[str(a__   )]   )

  with open(a__  ,      '''rb'''   ) as f:
    saved_model.ParseFromString(f.read()   )

  _UpperCamelCase       			=set()

  # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
  for meta_graph in saved_model.meta_graphs:
    # Add operations in the graph definition
    model_op_names.update(node.op for node in meta_graph.graph_def.node   )

    # Go through the functions in the graph definition
    for func in meta_graph.graph_def.library.function:
      # Add operations in each function
      model_op_names.update(node.op for node in func.node_def   )

    # Convert to list, sorted if you want
  _UpperCamelCase       			=sorted(a__   )
  _UpperCamelCase       			=[]

  for op in model_op_names:
    if op not in onnx_ops and op not in INTERNAL_OPS:
      incompatible_ops.append(a__   )

  if strict and len(a__   ) > 0:
    raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops   )
  elif len(a__   ) > 0:
    print(f'''Found the following incompatible ops for the opset {opset}:'''   )
    print(*a__  ,      sep='''\n'''   )
  else:
    print(f'''The saved model {saved_model_path} can properly be converted with ONNX.'''   )


if __name__ == "__main__":
      __lowerCamelCase						: Dict		=   argparse.ArgumentParser()
      parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
      parser.add_argument(
          '--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
      )
      parser.add_argument(
          '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
      )
      parser.add_argument(
          '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
      )
      __lowerCamelCase						: Tuple		=   parser.parse_args()

      if args.framework == "onnx":
            onnx_compliancy(args.saved_model_path, args.strict, args.opset)













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase						: str		=   {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: List[str]		=   ['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: str		=   ['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Optional[Any]		=   [
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Union[str, Any]		=   [
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						: Optional[int]		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






