



'''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: Optional[Any]							=						'.'

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

  with open(os.path.join(_A			,      'utils'			,      'tf_ops'			,      'onnx.json'    )    ) as f:
    a						: List[str]					       =					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()    )

  a						: Optional[int]					       =					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
  a						: List[str]					       =					sorted(_A    )
  a						: Union[str, Any]					       =					[]

  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: Optional[int]							=						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)'
     )
     lowerCAmelCase: Any							=						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_tokenizers_available, is_torch_available


lowerCAmelCase: Optional[int]							=						{
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: int							=						[
         'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'GraphormerForGraphClassification',
         'GraphormerModel',
         'GraphormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_graphormer import (
              GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              GraphormerForGraphClassification,
              GraphormerModel,
              GraphormerPreTrainedModel,
          )


else:
     import sys

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