





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             =							'.'

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:Optional[Any]   ,   SCREAMING_SNAKE_CASE		:List[Any]   )      ->   List[Any]:
 __lowerCAmelCase					:	Optional[int]      =       SavedModel()
 __lowerCAmelCase					:	Tuple      =       []

 with open(os.path.join(SCREAMING_SNAKE_CASE   ,   """utils"""   ,   """tf_ops"""   ,   """onnx.json"""   )   ) as f:
  __lowerCAmelCase					:	Tuple      =       json.load(SCREAMING_SNAKE_CASE   )["""opsets"""]

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

 with open(SCREAMING_SNAKE_CASE   ,   """rb"""   ) as f:
  saved_model.ParseFromString(f.read()   )

 __lowerCAmelCase					:	Dict      =       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					:	Tuple      =       sorted(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Any      =       []

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

 if strict and len(SCREAMING_SNAKE_CASE   ) > 0:
  raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops   )
 elif len(SCREAMING_SNAKE_CASE   ) > 0:
  print(F'''Found the following incompatible ops for the opset {opset}:'''   )
  print(*SCREAMING_SNAKE_CASE   ,   sep="""\n"""   )
 else:
  print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''   )


if __name__ == "__main__":
      _UpperCAmelCase             =							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             =							parser.parse_args()

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






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        snake_case_    (		__lowercase						):
  A_      							=   ['image_processor', 'tokenizer']
  A_      							=   'ChineseCLIPImageProcessor'
  A_      							=   ('BertTokenizer', 'BertTokenizerFast')






  def __init__(							self			:					Optional[Any]       ,  _snake_case			:					List[Any]=None       ,  _snake_case			:					str=None       ,  **_snake_case			:					int      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       None
   if "feature_extractor" in kwargs:
    warnings.warn(
        """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
        """ instead."""       ,  _snake_case       ,  )
    __lowerCAmelCase					:	List[str]      =       kwargs.pop("""feature_extractor"""      )

   __lowerCAmelCase					:	Union[str, Any]      =       image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError("""You need to specify an `image_processor`."""      )
   if tokenizer is None:
    raise ValueError("""You need to specify a `tokenizer`."""      )

   super().__init__(_snake_case       ,  _snake_case      )
   __lowerCAmelCase					:	Any      =       self.image_processor






  def __call__(							self			:					Optional[Any]       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					List[str]=None       ,  **_snake_case			:					Any      )->Union[str, Any]:






   '''simple docstring'''

   if text is None and images is None:
    raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

   if text is not None:
    __lowerCAmelCase					:	List[Any]      =       self.tokenizer(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )

   if images is not None:
    __lowerCAmelCase					:	Tuple      =       self.image_processor(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )

   if text is not None and images is not None:
    __lowerCAmelCase					:	Optional[Any]      =       image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**_snake_case      )       ,  tensor_type=_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  *_snake_case			:					Union[str, Any]       ,  **_snake_case			:					int      )->Optional[Any]:






   '''simple docstring'''

   return self.tokenizer.batch_decode(*_snake_case       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  *_snake_case			:					Dict       ,  **_snake_case			:					Any      )->Dict:






   '''simple docstring'''

   return self.tokenizer.decode(*_snake_case       ,  **_snake_case      )






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.tokenizer.model_input_names
   __lowerCAmelCase					:	Tuple      =       self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->Union[str, Any]:






   '''simple docstring'''

   warnings.warn(
       """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,  _snake_case       ,  )
   return self.image_processor_class