





"""simple docstring"""


import warnings

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



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Dict											=     ["""image_processor""", """tokenizer"""]
       snake_case__				:							Tuple											=     """AutoImageProcessor"""
       snake_case__				:							Tuple											=     """AutoTokenizer"""

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				None
        if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."   ,					__SCREAMING_SNAKE_CASE   ,					)
         snake_case__    							=				kwargs.pop("feature_extractor"     )

        snake_case__    							=				image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
         raise ValueError("You have to specify an image_processor."     )
        if tokenizer is None:
         raise ValueError("You have to specify a tokenizer."     )

        super().__init__(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				self.image_processor

       def __call__(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					**__SCREAMING_SNAKE_CASE     ):






        '''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:
         snake_case__    							=				self.tokenizer(__SCREAMING_SNAKE_CASE   ,					return_tensors=__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     )

        if images is not None:
         snake_case__    							=				self.image_processor(__SCREAMING_SNAKE_CASE   ,					return_tensors=__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     )

        if text is not None and images is not None:
         snake_case__    							=				image_features.pixel_values
         return encoding
        elif text is not None:
         return encoding
        else:
         return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,					tensor_type=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					*__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					*__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.tokenizer.model_input_names
        snake_case__    							=				self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,					__SCREAMING_SNAKE_CASE   ,					)
        return self.image_processor








"""simple docstring"""


from typing import TYPE_CHECKING

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


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				["""BartTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

