





"""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
lowercase__				:Tuple				   =       '.'

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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						SavedModel()
  __UpperCAmelCase    :						Any       =						[]

  with open(os.path.join(UpperCAmelCase_  ,			'''utils'''  ,			'''tf_ops'''  ,			'''onnx.json'''  )  ) as f:
    __UpperCAmelCase    :						List[str]       =						json.load(UpperCAmelCase_  )['''opsets''']

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

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

  __UpperCAmelCase    :						Union[str, Any]       =						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    :						Union[str, Any]       =						sorted(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						[]

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

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


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
  parser.add_argument(
      '--opset', default=1_2, 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)'
  )
  lowercase__				:Optional[Any]				   =       parser.parse_args()

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






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->bool:





  """simple docstring"""


  return numa ^ numa < 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()