




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
a_   		=     "."

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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						SavedModel()
    __UpperCamelCase  		=						[]

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

    __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(snake_case__      )
    __UpperCamelCase  		=						[]

    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__":
 a_   		=     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)"
 )
 a_   		=     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  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						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 ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						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				)







      def __call__(     self					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          if text is None and visual_prompt is None and images is None:
              raise ValueError('''You have to specify either text, visual prompt or images.'''				)

          if text is not None and visual_prompt is not None:
              raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

          if text is not None:
              __UpperCamelCase  		=						self.tokenizer(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if images is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None and images is not None:
              __UpperCamelCase  		=						{
                  '''pixel_values''': image_features.pixel_values,
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          elif text is not None and images is not None:
              __UpperCamelCase  		=						image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          elif visual_prompt is not None:
              __UpperCamelCase  		=						{
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          else:
              return BatchEncoding(data=dict(**snake_case				) ,       tensor_type=snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          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







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






