

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
A									=  '.'

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





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			SavedModel()
	snake_case_   		=			[]

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

	snake_case_   		=			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
	snake_case_   		=			sorted(lowercase__						)
	snake_case_   		=			[]

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

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



def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			len(lowercase__						)
	for _ in range(lowercase__						):
		for i in range(_ % 2      ,			arr_size - 1      ,			2						):
			if arr[i + 1] < arr[i]:
				snake_case_		,							snake_case_   		=			arr[i + 1], arr[i]

	return arr


if __name__ == "__main__":
		A									=  list(range(10, 0, -1))
		print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
