


from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


__UpperCamelCase       :       Any						       =				{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :       Optional[Any]						       =				["""DPTFeatureExtractor"""]
  __UpperCamelCase       :       int						       =				["""DPTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :       Tuple						       =				[
      """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPTForDepthEstimation""",
      """DPTForSemanticSegmentation""",
      """DPTModel""",
      """DPTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

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






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       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[Any]			=      ["image_processor", "tokenizer"]
     A:		Optional[Any]			=      "LayoutLMv2ImageProcessor"
     A:		List[str]			=      ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


     def __init__(      self							:     Dict							,	lowerCamelCase__							:     Union[str, Any]=None							,	lowerCamelCase__							:     List[str]=None							,	**lowerCamelCase__							:     Any			)  ->      Optional[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.'''							,	lowerCamelCase__							,	)
       UpperCamelCase__					:    str          =   kwargs.pop('''feature_extractor'''			)

      UpperCamelCase__					:    List[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__(lowerCamelCase__							,	lowerCamelCase__			)


     def __call__(      self							:     Optional[Any]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	lowerCamelCase__							:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None							,	lowerCamelCase__							:     Union[List[List[int]], List[List[List[int]]]] = None							,	lowerCamelCase__							:     Optional[Union[List[int], List[List[int]]]] = 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__							:     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__					:    Optional[Any]          =   self.image_processor(images=lowerCamelCase__							,	return_tensors=lowerCamelCase__			)

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

      UpperCamelCase__					:    Optional[int]          =   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=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
      UpperCamelCase__					:    Optional[Any]          =   features.pop('''pixel_values'''			)
      if return_overflowing_tokens is True:
       UpperCamelCase__					:    Union[str, Any]          =   self.get_overflowing_images(lowerCamelCase__							,	encoded_inputs['''overflow_to_sample_mapping''']			)
      UpperCamelCase__					:    Tuple          =   images

      return encoded_inputs


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     List[Any]			)  ->      str:





      '''simple docstring'''






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

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

      return images_with_overflow


     def      UpperCAmelCase__	(      self							:     int							,	*lowerCamelCase__							:     Dict							,	**lowerCamelCase__							:     str			)  ->      str:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     List[str]							,	*lowerCamelCase__							:     int							,	**lowerCamelCase__							:     str			)  ->      Tuple:





      '''simple docstring'''






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


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      str:





      '''simple docstring'''






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


     @property
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''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							:     Optional[Any]			)  ->      Optional[int]:





      '''simple docstring'''






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


