





"""simple docstring"""





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 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       __magic_name__     :Optional[int]		  =					["""image_processor""", """tokenizer"""]
       __magic_name__     :Union[str, Any]		  =					"""LayoutLMv3ImageProcessor"""
       __magic_name__     :str		  =					("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")






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



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							None
         if "feature_extractor" in kwargs:
           warnings.warn(
               'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
               ' instead.'    , __UpperCAmelCase    , )
           lowerCAmelCase__			:Union[str, Any]											=							kwargs.pop('feature_extractor'      )

         lowerCAmelCase__			:Union[str, Any]											=							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__(__UpperCAmelCase    , __UpperCAmelCase      )






       def __call__(    self    , __UpperCAmelCase    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = True    , __UpperCAmelCase = False    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = 0    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = False    , __UpperCAmelCase = False    , __UpperCAmelCase = False    , __UpperCAmelCase = False    , __UpperCAmelCase = True    , __UpperCAmelCase = None    , **__UpperCAmelCase    , ):



         '''simple docstring'''

         if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'      )

         if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'      )

         # first, apply the image processor
         lowerCAmelCase__			:List[str]											=							self.image_processor(images=__UpperCAmelCase    , return_tensors=__UpperCAmelCase      )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
             lowerCAmelCase__			:str											=							[text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowerCAmelCase__			:Union[str, Any]											=							features['words']

         lowerCAmelCase__			:str											=							self.tokenizer(
             text=text if text is not None else features['words']    , text_pair=text_pair if text_pair is not None else None    , boxes=boxes if boxes is not None else features['boxes']    , word_labels=__UpperCAmelCase    , add_special_tokens=__UpperCAmelCase    , padding=__UpperCAmelCase    , truncation=__UpperCAmelCase    , max_length=__UpperCAmelCase    , stride=__UpperCAmelCase    , pad_to_multiple_of=__UpperCAmelCase    , return_token_type_ids=__UpperCAmelCase    , return_attention_mask=__UpperCAmelCase    , return_overflowing_tokens=__UpperCAmelCase    , return_special_tokens_mask=__UpperCAmelCase    , return_offsets_mapping=__UpperCAmelCase    , return_length=__UpperCAmelCase    , verbose=__UpperCAmelCase    , return_tensors=__UpperCAmelCase    , **__UpperCAmelCase    , )

         # add pixel values
         lowerCAmelCase__			:List[str]											=							features.pop('pixel_values'      )
         if return_overflowing_tokens is True:
           lowerCAmelCase__			:Any											=							self.get_overflowing_images(__UpperCAmelCase    , encoded_inputs['overflow_to_sample_mapping']      )
         lowerCAmelCase__			:Optional[Any]											=							images

         return encoded_inputs






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							[]
         for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]      )

         if len(__UpperCAmelCase      ) != len(__UpperCAmelCase      ):
           raise ValueError(
               'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
               F" {len(__UpperCAmelCase      )} and {len(__UpperCAmelCase      )}"      )

         return images_with_overflow






       def      snake_case						(    self    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

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






       def      snake_case						(    self    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

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






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return ["input_ids", "bbox", "attention_mask", "pixel_values"]






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

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






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

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







"""simple docstring"""





from typing import TYPE_CHECKING

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


__A											=		{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

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

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """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											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
