
"""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		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						["image_processor", "tokenizer"]
     lowercase						       =						"LayoutLMv3ImageProcessor"
     lowercase						       =						("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")





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

      '''simple docstring'''




      __UpperCamelCase      =	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     ,					)
       __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     ,					__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
      __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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''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 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




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





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

      '''simple docstring'''




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





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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





     @property
     def 						UpperCAmelCase (     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 						UpperCAmelCase (     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"""

import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


UpperCamelCase     :    Optional[int]       =			logging.get_logger(__name__)

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





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

      '''simple docstring'''




      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'     ,					__UpperCAmelCase     ,					)
      super().__init__(*__UpperCAmelCase     ,					**__UpperCAmelCase				)



