

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

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


def       snake_case   (	snake_case      : str					,		snake_case      : str					,		snake_case      : Any			) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							SavedModel()
    lowerCAmelCase 						=							[]

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

    lowerCAmelCase 						=							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
    lowerCAmelCase 						=							sorted(snake_case			)
    lowerCAmelCase 						=							[]

    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__":
     _UpperCamelCase						:  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)"
     )
     _UpperCamelCase						:  Optional[Any]							       =				parser.parse_args()

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








'''simple docstring'''



def       snake_case   (	snake_case      : list					,		snake_case      : list					,		snake_case      : int					,		snake_case      : int					,		snake_case      : int			) ->  int:


    """simple docstring"""


    if index == number_of_items:
        return 0
    lowerCAmelCase 						=							0
    lowerCAmelCase 						=							0
    lowerCAmelCase 						=							knapsack(snake_case					,		snake_case					,		snake_case					,		snake_case					,		index + 1			)
    if weights[index] <= max_weight:
        lowerCAmelCase 						=							values[index] + knapsack(
            snake_case					,		snake_case					,		snake_case					,		max_weight - weights[index]					,		index + 1			)
    return max(snake_case					,		snake_case			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()





