





import warnings

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







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					["""image_processor""", """tokenizer"""]
       lowerCAmelCase__						=					"""ChineseCLIPImageProcessor"""
       lowerCAmelCase__						=					("""BertTokenizer""", """BertTokenizerFast""")




       def __init__(  self			:   Optional[int]							,    A			:   List[Any]=None							,    A			:   Tuple=None							,    **A			:   str		):
         __snake_case:						List[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."""							,    A							,    )
           __snake_case:						Dict			  =						kwargs.pop("""feature_extractor"""		)

         __snake_case:						str			  =						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__(A							,    A		)
         __snake_case:						int			  =						self.image_processor




       def __call__(  self			:   List[Any]							,    A			:   int=None							,    A			:   str=None							,    A			:   int=None							,    **A			:   Tuple		):

         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:
           __snake_case:						Any			  =						self.tokenizer(A							,    return_tensors=A							,    **A		)

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

         if text is not None and images is not None:
           __snake_case:						Union[str, Any]			  =						image_features.pixel_values
           return encoding
         elif text is not None:
           return encoding
         else:
           return BatchEncoding(data=dict(**A		)							,    tensor_type=A		)




       def 							UpperCAmelCase__			(  self			:   List[Any]							,    *A			:   Optional[Any]							,    **A			:   Optional[Any]		):
         return self.tokenizer.batch_decode(*A							,    **A		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]							,    *A			:   Optional[Any]							,    **A			:   Optional[int]		):
         return self.tokenizer.decode(*A							,    **A		)




       @property
       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						Dict			  =						self.tokenizer.model_input_names
         __snake_case:						str			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)






       @property
       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,    A							,    )
         return self.image_processor_class













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






