



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__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       Union[str, Any] ,       UpperCamelCase_				:       str	)->  Dict:
    A__		 =					SavedModel()
    A__		 =					[]

    with open(os.path.join(UpperCamelCase_ ,       '''utils''' ,       '''tf_ops''' ,       '''onnx.json'''	)	) as f:
        A__		 =					json.load(UpperCamelCase_	)['''opsets''']

    for i in range(1 ,       opset + 1	):
        onnx_ops.extend(onnx_opsets[str(UpperCamelCase_	)]	)

    with open(UpperCamelCase_ ,       '''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(UpperCamelCase_	)
    A__		 =					[]

    for op in model_op_names:
        if op not in onnx_ops and op not in INTERNAL_OPS:
            incompatible_ops.append(UpperCamelCase_	)

    if strict and len(UpperCamelCase_	) > 0:
        raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops	)
    elif len(UpperCamelCase_	) > 0:
        print(f"Found the following incompatible ops for the opset {opset}:"	)
        print(*UpperCamelCase_ ,       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)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase						  =		logging.get_logger(__name__)



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''timm_backbone'''




     def __init__(				self ,						a__=None ,						a__=3 ,						a__=True ,						a__=True ,						a__=None ,						**a__ ,						):
         super().__init__(**a__)
         A__		 =					backbone
         A__		 =					num_channels
         A__		 =					features_only
         A__		 =					use_pretrained_backbone
         A__		 =					True
         A__		 =					out_indices if out_indices is not None else (-1,)
