



"""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    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''image_processor''', '''tokenizer''']
       UpperCamelCase		=  '''LayoutLMv2ImageProcessor'''
       UpperCamelCase		=  ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')



       def __init__(							self    :						Any	,       _UpperCAmelCase    :						Tuple=None	,       _UpperCAmelCase    :						List[str]=None	,       **_UpperCAmelCase    :						Optional[int]					)				->		Any:


             '''simple docstring'''




             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."	,       _UpperCAmelCase	,       )
                   UpperCAmelCase_    				=							kwargs.pop("feature_extractor"					)

             UpperCAmelCase_    				=							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    :						Optional[int]	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,       _UpperCAmelCase    :						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,       _UpperCAmelCase    :						Union[List[List[int]], List[List[List[int]]]] = None	,       _UpperCAmelCase    :						Optional[Union[List[int], List[List[int]]]] = None	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Union[bool, str, PaddingStrategy] = False	,       _UpperCAmelCase    :						Union[bool, str, TruncationStrategy] = None	,       _UpperCAmelCase    :						Optional[int] = None	,       _UpperCAmelCase    :						int = 0	,       _UpperCAmelCase    :						Optional[int] = None	,       _UpperCAmelCase    :						Optional[bool] = None	,       _UpperCAmelCase    :						Optional[bool] = None	,       _UpperCAmelCase    :						bool = False	,       _UpperCAmelCase    :						bool = False	,       _UpperCAmelCase    :						bool = False	,       _UpperCAmelCase    :						bool = False	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Optional[Union[str, TensorType]] = None	,       **_UpperCAmelCase    :						Any	,       )				->		BatchEncoding:


             '''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."					)

             if return_overflowing_tokens is True and return_offsets_mapping is False:
                   raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."					)

             # first, apply the image processor
             UpperCAmelCase_    				=							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					):
                         UpperCAmelCase_    				=							[text]  # add batch dimension (as the image processor always adds a batch dimension)
                   UpperCAmelCase_    				=							features["words"]

             UpperCAmelCase_    				=							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
             UpperCAmelCase_    				=							features.pop("pixel_values"					)
             if return_overflowing_tokens is True:
                   UpperCAmelCase_    				=							self.get_overflowing_images(_UpperCAmelCase	,       encoded_inputs["overflow_to_sample_mapping"]					)
             UpperCAmelCase_    				=							images

             return encoded_inputs



       def   lowercase__       (							self    :						str	,       _UpperCAmelCase    :						Optional[int]	,       _UpperCAmelCase    :						int					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							[]
             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   lowercase__       (							self    :						str	,       *_UpperCAmelCase    :						Dict	,       **_UpperCAmelCase    :						Union[str, Any]					)				->		Any:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Optional[Any]	,       *_UpperCAmelCase    :						List[Any]	,       **_UpperCAmelCase    :						Optional[Any]					)				->		Union[str, Any]:


             '''simple docstring'''




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



       @property
       def   lowercase__       (							self    :						Dict					)				->		Dict:


             '''simple docstring'''




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



       @property
       def   lowercase__       (							self    :						Any					)				->		Union[str, Any]:


             '''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   lowercase__       (							self    :						Optional[int]					)				->		Optional[int]:


             '''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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase      =		{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		["""PerceiverFeatureExtractor"""]
  lowerCamelCase      =		["""PerceiverImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		[
      """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PerceiverForImageClassificationConvProcessing""",
      """PerceiverForImageClassificationFourier""",
      """PerceiverForImageClassificationLearned""",
      """PerceiverForMaskedLM""",
      """PerceiverForMultimodalAutoencoding""",
      """PerceiverForOpticalFlow""",
      """PerceiverForSequenceClassification""",
      """PerceiverLayer""",
      """PerceiverModel""",
      """PerceiverPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
  from .tokenization_perceiver import PerceiverTokenizer

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_perceiver import PerceiverFeatureExtractor
    from .image_processing_perceiver import PerceiverImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_perceiver import (
        PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PerceiverForImageClassificationConvProcessing,
        PerceiverForImageClassificationFourier,
        PerceiverForImageClassificationLearned,
        PerceiverForMaskedLM,
        PerceiverForMultimodalAutoencoding,
        PerceiverForOpticalFlow,
        PerceiverForSequenceClassification,
        PerceiverLayer,
        PerceiverModel,
        PerceiverPreTrainedModel,
    )

else:
  import sys

  lowerCamelCase      =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




