





'''simple docstring'''
from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

a__							:							Optional[int] =logging.get_logger(__name__)
def 				lowercase__   (   __lowercase						:  Dict				,  __lowercase						:  Optional[int]				,  __lowercase						:  str     ) ->      Optional[int]:



       """simple docstring"""






       return [
           int(1000 * (box[0] / width)     ),
           int(1000 * (box[1] / height)     ),
           int(1000 * (box[2] / width)     ),
           int(1000 * (box[3] / height)     ),
       ]
def 				lowercase__   (   __lowercase						:  np.ndarray				,  __lowercase						:  Optional[str]				,  __lowercase						:  Optional[str] = None     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  tesseract_config if tesseract_config is not None else ''

       # apply OCR
       __UpperCamelCase       =  to_pil_image(__lowercase     )
       __UpperCamelCase       ,						__UpperCamelCase       =  pil_image.size
       __UpperCamelCase       =  pytesseract.image_to_data(__lowercase				,  lang=__lowercase				,  output_type='dict'				,  config=__lowercase     )
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  data['text'], data['left'], data['top'], data['width'], data['height']

       # filter empty words and corresponding coordinates
       __UpperCamelCase       =  [idx for idx, word in enumerate(__lowercase     ) if not word.strip()]
       __UpperCamelCase       =  [word for idx, word in enumerate(__lowercase     ) if idx not in irrelevant_indices]
       __UpperCamelCase       =  [coord for idx, coord in enumerate(__lowercase     ) if idx not in irrelevant_indices]
       __UpperCamelCase       =  [coord for idx, coord in enumerate(__lowercase     ) if idx not in irrelevant_indices]
       __UpperCamelCase       =  [coord for idx, coord in enumerate(__lowercase     ) if idx not in irrelevant_indices]
       __UpperCamelCase       =  [coord for idx, coord in enumerate(__lowercase     ) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       __UpperCamelCase       =  []
       for x, y, w, h in zip(__lowercase				,  __lowercase				,  __lowercase				,  __lowercase     ):
              __UpperCamelCase       =  [x, y, x + w, y + h]
              actual_boxes.append(__lowercase     )

       # finally, normalize the bounding boxes
       __UpperCamelCase       =  []
       for box in actual_boxes:
              normalized_boxes.append(normalize_box(__lowercase				,  __lowercase				,  __lowercase     )     )

       assert len(__lowercase     ) == len(__lowercase     ), "Not as many words as there are bounding boxes"

       return words, normalized_boxes


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					=["pixel_values"]






 def __init__(       self       :							str							,	__A       :							bool = True							,	__A       :							Dict[str, int] = None							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							bool = True							,	__A       :							Optional[str] = None							,	__A       :							Optional[str] = ""							,	**__A       :							int							,	):
        super().__init__(**__A	)
        __UpperCamelCase       =  size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        __UpperCamelCase       =  get_size_dict(__A	)

        __UpperCamelCase       =  do_resize
        __UpperCamelCase       =  size
        __UpperCamelCase       =  resample
        __UpperCamelCase       =  apply_ocr
        __UpperCamelCase       =  ocr_lang
        __UpperCamelCase       =  tesseract_config






 def        _lowerCamelCase					(       self       :							str							,	__A       :							np.ndarray							,	__A       :							Dict[str, int]							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							Optional[Union[str, ChannelDimension]] = None							,	**__A       :							Any							,	):
        __UpperCamelCase       =  get_size_dict(__A	)
        if "height" not in size or "width" not in size:
               raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''	)
        __UpperCamelCase       =  (size['height'], size['width'])
        return resize(__A							,	size=__A							,	resample=__A							,	data_format=__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							ImageInput							,	__A       :							bool = None							,	__A       :							Dict[str, int] = None							,	__A       :							PILImageResampling = None							,	__A       :							bool = None							,	__A       :							Optional[str] = None							,	__A       :							Optional[str] = None							,	__A       :							Optional[Union[str, TensorType]] = None							,	__A       :							ChannelDimension = ChannelDimension.FIRST							,	**__A       :							Optional[int]							,	):
        __UpperCamelCase       =  do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase       =  size if size is not None else self.size
        __UpperCamelCase       =  get_size_dict(__A	)
        __UpperCamelCase       =  resample if resample is not None else self.resample
        __UpperCamelCase       =  apply_ocr if apply_ocr is not None else self.apply_ocr
        __UpperCamelCase       =  ocr_lang if ocr_lang is not None else self.ocr_lang
        __UpperCamelCase       =  tesseract_config if tesseract_config is not None else self.tesseract_config

        __UpperCamelCase       =  make_list_of_images(__A	)

        if not valid_images(__A	):
               raise ValueError(
                   'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                   'torch.Tensor, tf.Tensor or jax.ndarray.'	)

        if do_resize and size is None:
               raise ValueError('Size must be specified if do_resize is True.'	)

        # All transformations expect numpy arrays.
        __UpperCamelCase       =  [to_numpy_array(__A	) for image in images]

        if apply_ocr:
               requires_backends(self							,	'pytesseract'	)
               __UpperCamelCase       =  []
               __UpperCamelCase       =  []
               for image in images:
                      __UpperCamelCase       ,						__UpperCamelCase       =  apply_tesseract(__A							,	__A							,	__A	)
                      words_batch.append(__A	)
                      boxes_batch.append(__A	)

        if do_resize:
               __UpperCamelCase       =  [self.resize(image=__A							,	size=__A							,	resample=__A	) for image in images]

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        __UpperCamelCase       =  [flip_channel_order(__A	) for image in images]
        __UpperCamelCase       =  [to_channel_dimension_format(__A							,	__A	) for image in images]

        __UpperCamelCase       =  BatchFeature(data={'pixel_values': images}							,	tensor_type=__A	)

        if apply_ocr:
               __UpperCamelCase       =  words_batch
               __UpperCamelCase       =  boxes_batch
        return data








"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        a     (   a_	):
    UpperCAmelCase_ :     List[Any]	     =["image_processor", "tokenizer"]
    UpperCAmelCase_ :     str	     ="AutoImageProcessor"
    UpperCAmelCase_ :     Any	     ="AutoTokenizer"







    def __init__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'							,		_lowerCamelCase							,		)
        lowercase							    =       kwargs.pop('feature_extractor'       )

      lowercase							    =       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       )
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*_lowerCamelCase							,		**_lowerCamelCase       )

      lowercase							    =       kwargs.pop('images'							,		_lowerCamelCase       )
      lowercase							    =       kwargs.pop('text'							,		_lowerCamelCase       )
      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       args[0]
        lowercase							    =       args[1:]

      if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.'       )

      if images is not None:
        lowercase							    =       self.image_processor(_lowerCamelCase							,		*_lowerCamelCase							,		**_lowerCamelCase       )
      if text is not None:
        lowercase							    =       self.tokenizer(_lowerCamelCase							,		**_lowerCamelCase       )

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowercase							    =       encodings['input_ids']
        return inputs







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.batch_decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    @contextmanager
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
          'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
          'your images inputs, or in a separate call.'       )
      lowercase							    =       True
      lowercase							    =       self.tokenizer
      yield
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=None       ):
      if added_vocab is None:
        lowercase							    =       self.tokenizer.get_added_vocab()

      lowercase							    =       {}

      while tokens:
        lowercase							    =       re.search(R'<s_(.*?)>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        if start_token is None:
          break
        lowercase							    =       start_token.group(1       )
        lowercase							    =       re.search(RF'</s_{key}>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        lowercase							    =       start_token.group()
        if end_token is None:
          lowercase							    =       tokens.replace(_lowerCamelCase							,		''       )
        else:
          lowercase							    =       end_token.group()
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.search(F'{start_token_escaped}(.*?){end_token_escaped}'							,		_lowerCamelCase							,		re.IGNORECASE       )
          if content is not None:
            lowercase							    =       content.group(1       ).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowercase							    =       self.tokenajson(_lowerCamelCase							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )
              if value:
                if len(_lowerCamelCase       ) == 1:
                  lowercase							    =       value[0]
                lowercase							    =       value
            else:  # leaf nodes
              lowercase							    =       []
              for leaf in content.split(R'<sep/>'       ):
                lowercase							    =       leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowercase							    =       leaf[1:-2]  # for categorical special tokens
                output[key].append(_lowerCamelCase       )
              if len(output[key]       ) == 1:
                lowercase							    =       output[key][0]

          lowercase							    =       tokens[tokens.find(_lowerCamelCase       ) + len(_lowerCamelCase       ) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )

      if len(_lowerCamelCase       ):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def 			UpperCamelCase_      (			self       ):
      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       ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor


