
"""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
UpperCAmelCase__   :					List[str]												=				'.'

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

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: List[Any]     =     SavedModel()
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     []

							with open(os.path.join(_snake_case     ,"""utils"""     ,"""tf_ops"""     ,"""onnx.json"""		)		) as f:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     json.load(_snake_case		)["""opsets"""]

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

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

							SCREAMING_SNAKE_CASE__		: 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
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     sorted(_snake_case		)
							SCREAMING_SNAKE_CASE__		: int     =     []

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

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


if __name__ == "__main__":
					UpperCAmelCase__   :					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)'
					)
					UpperCAmelCase__   :					int												=				parser.parse_args()

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






"""simple docstring"""




import torch

def lowercase_						(     ):
							if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.cuda.device_count()
							else:
														SCREAMING_SNAKE_CASE__		: int     =     0
							print(f'''Successfully ran on {num_gpus} GPUs'''		)


if __name__ == "__main__":
					main()




