




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_							(				lowerCamelCase__ ):


 '''simple docstring'''







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



 def __init__(							self  :		Any    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		List[Any]=None    ,    **_lowerCAmelCase  :		List[str]						):







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

       SCREAMING_SNAKE_CASE_     						=      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  :		List[str]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Optional[int] = None    ,    _lowerCAmelCase  :		List[str] = None    ,    _lowerCAmelCase  :		int = None    ,    _lowerCAmelCase  :		Tuple = None    ,    _lowerCAmelCase  :		str = True    ,    _lowerCAmelCase  :		Dict = False    ,    _lowerCAmelCase  :		List[Any] = None    ,    _lowerCAmelCase  :		Any = None    ,    _lowerCAmelCase  :		Optional[Any] = 0    ,    _lowerCAmelCase  :		Optional[int] = None    ,    _lowerCAmelCase  :		Optional[int] = None    ,    _lowerCAmelCase  :		Any = None    ,    _lowerCAmelCase  :		Optional[int] = False    ,    _lowerCAmelCase  :		Optional[int] = False    ,    _lowerCAmelCase  :		str = False    ,    _lowerCAmelCase  :		List[str] = False    ,    _lowerCAmelCase  :		int = True    ,    _lowerCAmelCase  :		Tuple = None    ,    **_lowerCAmelCase  :		List[Any]    ,    ):







       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
       SCREAMING_SNAKE_CASE_     						=      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						):
                   SCREAMING_SNAKE_CASE_     						=      [text]  # add batch dimension (as the image processor always adds a batch dimension)
             SCREAMING_SNAKE_CASE_     						=      features['words']

       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      features.pop('pixel_values'						)
       if return_overflowing_tokens is True:
             SCREAMING_SNAKE_CASE_     						=      self.get_overflowing_images(_lowerCAmelCase    ,    encoded_inputs['overflow_to_sample_mapping']						)
       SCREAMING_SNAKE_CASE_     						=      images

       return encoded_inputs



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      []
       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 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    *_lowerCAmelCase  :		Union[str, Any]    ,    **_lowerCAmelCase  :		Union[str, Any]						):
       return self.tokenizer.batch_decode(*_lowerCAmelCase    ,    **_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		int    ,    *_lowerCAmelCase  :		Dict    ,    **_lowerCAmelCase  :		str						):
       return self.tokenizer.decode(*_lowerCAmelCase    ,    **_lowerCAmelCase						)



 @property
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       return ["input_ids", "bbox", "attention_mask", "image"]



 @property
 def 				lowerCAmelCase_  (							self  :		Dict						):
       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 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,    _lowerCAmelCase    ,    )
       return self.image_processor











import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowerCamelCase__			:       Dict         =							logging.get_logger(__name__)





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    *_lowerCAmelCase  :		List[str]    ,    **_lowerCAmelCase  :		Optional[int]						):
       warnings.warn(
           'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use LayoutLMv2ImageProcessor instead.'    ,    _lowerCAmelCase    ,    )
       super().__init__(*_lowerCAmelCase    ,    **_lowerCAmelCase						)