
"""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
A__      :	Dict          =    """."""

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



def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						SavedModel()
     UpperCAmelCase__							: Optional[Any] 							=						[]

     with open(os.path.join(lowerCAmelCase       ,    "utils"       ,    "tf_ops"       ,    "onnx.json"		)		) as f:
          UpperCAmelCase__							: Optional[Any] 							=						json.load(lowerCAmelCase		)["opsets"]

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

     with open(lowerCAmelCase       ,    "rb"		) as f:
          saved_model.ParseFromString(f.read()		)

     UpperCAmelCase__							: Tuple 							=						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__							: Optional[Any] 							=						sorted(lowerCAmelCase		)
     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(lowerCAmelCase		)

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


if __name__ == "__main__":
    A__      :	Union[str, Any]          =    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)"""
    )
    A__      :	List[str]          =    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 _LazyModule


A__      :	List[str]          =    {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_tapex import TapexTokenizer


else:
    import sys

    A__      :	Any          =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)



