






"""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
lowercase    			=       '''.'''

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






def _lowerCAmelCase	(  __lowerCamelCase:Dict ,						__lowerCamelCase:Union[str, Any] ,						__lowerCamelCase:Any   ):

						'''simple docstring'''






						__magic_name__     :   Any            =     SavedModel()
						__magic_name__     :   List[str]            =     []

						with open(os.path.join(__lowerCamelCase ,						"utils" ,						"tf_ops" ,						"onnx.json"   )   ) as f:
												__magic_name__     :   Tuple            =     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()   )

						__magic_name__     :   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
						__magic_name__     :   List[str]            =     sorted(__lowerCamelCase   )
						__magic_name__     :   Tuple            =     []

						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__":
			lowercase    			=       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)'''
			)
			lowercase    			=       parser.parse_args()

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











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     n ** (1 / 3)
						return (val * val * val) == n


if __name__ == "__main__":
			print(perfect_cube(27))
			print(perfect_cube(4))



