





'''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
_SCREAMING_SNAKE_CASE   =      '''.'''

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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[Any]			):
			__lowercase			  =							SavedModel()
			__lowercase			  =							[]

			with open(os.path.join(lowerCamelCase_						,    '''utils'''						,    '''tf_ops'''						,    '''onnx.json'''			)			) as f:
						__lowercase			  =							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()			)

			__lowercase			  =							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
			__lowercase			  =							sorted(lowerCamelCase_			)
			__lowercase			  =							[]

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

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












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]			):
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						__lowercase			  =							0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors




def 				_lowerCAmelCase  (			):
			__lowercase			  =							1
			__lowercase			  =							1

			while True:
						i += 1
						t_num += i

						if count_divisors(lowerCamelCase_			) > 5_0_0:
									break

			return t_num


if __name__ == "__main__":
	print(solution())





