





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       ["""image_processor""", """tokenizer"""]
      UpperCamelCase__  		=       """ViltImageProcessor"""
      UpperCamelCase__  		=       ("""BertTokenizer""", """BertTokenizerFast""")






      def __init__(		self:			Dict	,    __lowerCamelCase:			Dict=None	,    __lowerCamelCase:			Tuple=None	,    **__lowerCamelCase:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."	,    __lowerCamelCase	,    )
              UpperCamelCase__:	Dict					=     kwargs.pop("feature_extractor"					)

          UpperCamelCase__:	Dict					=     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__(__lowerCamelCase	,    __lowerCamelCase					)
          UpperCamelCase__:	Tuple					=     self.image_processor






      def __call__(		self:			str	,    __lowerCamelCase:			List[Any]	,    __lowerCamelCase:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,    __lowerCamelCase:			bool = True	,    __lowerCamelCase:			Union[bool, str, PaddingStrategy] = False	,    __lowerCamelCase:			Union[bool, str, TruncationStrategy] = None	,    __lowerCamelCase:			Optional[int] = None	,    __lowerCamelCase:			int = 0	,    __lowerCamelCase:			Optional[int] = None	,    __lowerCamelCase:			Optional[bool] = None	,    __lowerCamelCase:			Optional[bool] = None	,    __lowerCamelCase:			bool = False	,    __lowerCamelCase:			bool = False	,    __lowerCamelCase:			bool = False	,    __lowerCamelCase:			bool = False	,    __lowerCamelCase:			bool = True	,    __lowerCamelCase:			Optional[Union[str, TensorType]] = None	,    **__lowerCamelCase:			str	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Any					=     self.tokenizer(
              text=__lowerCamelCase	,    add_special_tokens=__lowerCamelCase	,    padding=__lowerCamelCase	,    truncation=__lowerCamelCase	,    max_length=__lowerCamelCase	,    stride=__lowerCamelCase	,    pad_to_multiple_of=__lowerCamelCase	,    return_token_type_ids=__lowerCamelCase	,    return_attention_mask=__lowerCamelCase	,    return_overflowing_tokens=__lowerCamelCase	,    return_special_tokens_mask=__lowerCamelCase	,    return_offsets_mapping=__lowerCamelCase	,    return_length=__lowerCamelCase	,    verbose=__lowerCamelCase	,    return_tensors=__lowerCamelCase	,    **__lowerCamelCase	,    )
          # add pixel_values + pixel_mask
          UpperCamelCase__:	Optional[Any]					=     self.image_processor(__lowerCamelCase	,    return_tensors=__lowerCamelCase					)
          encoding.update(__lowerCamelCase					)

          return encoding






      def 			UpperCAmelCase_       (		self:			Optional[Any]	,    *__lowerCamelCase:			Optional[Any]	,    **__lowerCamelCase:			List[str]					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			int	,    *__lowerCamelCase:			List[Any]	,    **__lowerCamelCase:			Any					):
          '''simple docstring'''


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






      @property
      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.tokenizer.model_input_names
          UpperCamelCase__:	Union[str, Any]					=     self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






      @property
      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


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






      @property
      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


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








from typing import TYPE_CHECKING

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


A__:    Optional[Any]  	=	{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[Any]  	=	[
       '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RobertaPreLayerNormForCausalLM''',
       '''RobertaPreLayerNormForMaskedLM''',
       '''RobertaPreLayerNormForMultipleChoice''',
       '''RobertaPreLayerNormForQuestionAnswering''',
       '''RobertaPreLayerNormForSequenceClassification''',
       '''RobertaPreLayerNormForTokenClassification''',
       '''RobertaPreLayerNormModel''',
       '''RobertaPreLayerNormPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Tuple  	=	[
       '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRobertaPreLayerNormForCausalLM''',
       '''TFRobertaPreLayerNormForMaskedLM''',
       '''TFRobertaPreLayerNormForMultipleChoice''',
       '''TFRobertaPreLayerNormForQuestionAnswering''',
       '''TFRobertaPreLayerNormForSequenceClassification''',
       '''TFRobertaPreLayerNormForTokenClassification''',
       '''TFRobertaPreLayerNormMainLayer''',
       '''TFRobertaPreLayerNormModel''',
       '''TFRobertaPreLayerNormPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''FlaxRobertaPreLayerNormForCausalLM''',
       '''FlaxRobertaPreLayerNormForMaskedLM''',
       '''FlaxRobertaPreLayerNormForMultipleChoice''',
       '''FlaxRobertaPreLayerNormForQuestionAnswering''',
       '''FlaxRobertaPreLayerNormForSequenceClassification''',
       '''FlaxRobertaPreLayerNormForTokenClassification''',
       '''FlaxRobertaPreLayerNormModel''',
       '''FlaxRobertaPreLayerNormPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roberta_prelayernorm import (
       ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
       RobertaPreLayerNormConfig,
       RobertaPreLayerNormOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaPreLayerNormForCausalLM,
          RobertaPreLayerNormForMaskedLM,
          RobertaPreLayerNormForMultipleChoice,
          RobertaPreLayerNormForQuestionAnswering,
          RobertaPreLayerNormForSequenceClassification,
          RobertaPreLayerNormForTokenClassification,
          RobertaPreLayerNormModel,
          RobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta_prelayernorm import (
          TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaPreLayerNormForCausalLM,
          TFRobertaPreLayerNormForMaskedLM,
          TFRobertaPreLayerNormForMultipleChoice,
          TFRobertaPreLayerNormForQuestionAnswering,
          TFRobertaPreLayerNormForSequenceClassification,
          TFRobertaPreLayerNormForTokenClassification,
          TFRobertaPreLayerNormMainLayer,
          TFRobertaPreLayerNormModel,
          TFRobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormPreTrainedModel,
      )

else:
   import sys

   A__:    Union[str, Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

