





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    : Optional[int]    	=  """."""

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


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						SavedModel()
       _UpperCamelCase              =						[]

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

       _UpperCamelCase              =						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
       _UpperCamelCase              =						sorted(lowerCAmelCase					)
       _UpperCamelCase              =						[]

       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__":
       lowercase    : List[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)"""
       )
       lowercase    : int    	=  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_clip import CLIPImageProcessor


lowercase    : str    	=  logging.get_logger(__name__)

class  __A(						__UpperCAmelCase ):

     def __init__(      self,       *A,       **A      ):





            """simple docstring"""

            warnings.warn(
                '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                ''' use CLIPImageProcessor instead.''',       A,       )
            super().__init__(*A,       **A      )






