'''simple docstring'''




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






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					["image_processor", "tokenizer"]
       snake_case       :str				    =					"AutoImageProcessor"
       snake_case       :Optional[Any]				    =					"AutoTokenizer"






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              super().__init__(UpperCamelCase_				,     UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		self.image_processor






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

              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:
                     __UpperCAmelCase  :				str       				=		self.tokenizer(UpperCamelCase_				,     return_tensors=UpperCamelCase_				,     **UpperCamelCase_      )

              if images is not None:
                     __UpperCAmelCase  :				int       				=		self.image_processor(UpperCamelCase_				,     return_tensors=UpperCamelCase_				,     **UpperCamelCase_      )

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






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              return self.tokenizer.batch_decode(*UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              return self.tokenizer.decode(*UpperCamelCase_				,     **UpperCamelCase_      )







       @property
       def 			_snake_case						(      self      ):
              return ["input_ids", "attention_mask", "pixel_values"]







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

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






