


'''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  :	List[str]			      =       "."

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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    int											=     SavedModel()
							__snake_case	:    int											=     []

							with open(os.path.join(__lowerCamelCase							,			"utils"							,			"tf_ops"							,			"onnx.json"			)			) as f:
														__snake_case	:    Optional[int]											=     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()			)

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

							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__":
			_snake_case  :	Union[str, Any]			      =       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  :	str			      =       parser.parse_args()

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





import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


_snake_case  :	List[str]			      =       logging.get_logger(__name__)

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Any					,			**lowerCamelCase :						Union[str, Any]       )			->					None:
														warnings.warn(
														    "The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
														    " Please use PoolFormerImageProcessor instead."					,			lowerCamelCase					,			)
														super().__init__(*lowerCamelCase					,			**lowerCamelCase       )

