


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	: Union[str, Any]  				='.'

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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		SavedModel()
						__SCREAMING_SNAKE_CASE						:     str					=		[]

						with open(os.path.join(lowercase__    ,       '''utils'''    ,       '''tf_ops'''    ,       '''onnx.json'''      )      ) as f:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		json.load(lowercase__      )['''opsets''']

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

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

						__SCREAMING_SNAKE_CASE						:     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
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		sorted(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		[]

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

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


if __name__ == "__main__":
						__lowerCAmelCase	: List[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)'
						)
						__lowerCAmelCase	: str  				=parser.parse_args()

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




from ..utils import DummyObject, requires_backends
class        _lowercase      (			metaclass=A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		['''keras_nlp''']


				def __init__(       self			:Tuple			,			*lowerCAmelCase__			:Optional[Any]			,			**lowerCAmelCase__			:Dict	)		->			Dict:
										requires_backends(self			,			['''keras_nlp''']	)
