


'''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 __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Optional[int]				)      ->     int:







					'''simple docstring'''






					_a						=		SavedModel()
					_a						=		[]

					with open(os.path.join(snake_case__      ,  "utils"      ,  "tf_ops"      ,  "onnx.json"				)				) as f:
										_a						=		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						=		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						=		sorted(snake_case__				)
					_a						=		[]

					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__":
			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 __UpperCamelCase   (			__lowerCamelCase		: int = 400_0000				)      ->     int:







     '''simple docstring'''






     _a						=		[]
     _a			,			_a						=		0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(__lowerCamelCase				)
          _a			,			_a						=		b, a + b
     return sum(__lowerCamelCase				)


if __name__ == "__main__":
   print(f'''{solution() = }''')

