

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

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






						"""simple docstring"""





						A__					:       Tuple     =SavedModel()
						A__					:       str     =[]

						with open(os.path.join(__snake_case,		"""utils""",		"""tf_ops""",		"""onnx.json"""	)	) as f:
												A__					:       str     =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()	)

						A__					:       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__					:       Optional[int]     =sorted(__snake_case	)
						A__					:       List[Any]     =[]

						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__":
		__snake_case			:     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=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)'
		)
		__snake_case			:     Any										=      parser.parse_args()

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




'''simple docstring'''



from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def 						__lowerCamelCase	(       __snake_case					:		int	)	->							int:






						"""simple docstring"""





						A__					:       List[Any]     =prime_factors(__snake_case	)
						if is_square_free(__snake_case	):
												return -1 if len(__snake_case	) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

