






"""simple docstring"""



from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

_UpperCamelCase		:  Any						=		logging.get_logger(__name__)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple		,					__snake_case			:  int		,					__snake_case			:  int    ):





  '''simple docstring'''





  return [
      int(10_00 * (box[0] / width)    ),
      int(10_00 * (box[1] / height)    ),
      int(10_00 * (box[2] / width)    ),
      int(10_00 * (box[3] / height)    ),
  ]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  np.ndarray		,					__snake_case			:  Optional[str]		,					__snake_case			:  Optional[str]    ):





  '''simple docstring'''





  lowercase							    =       to_pil_image(SCREAMING_SNAKE_CASE_    )
  lowercase							    =       pil_image.size
  lowercase							    =       pytesseract.image_to_data(SCREAMING_SNAKE_CASE_		,					lang=SCREAMING_SNAKE_CASE_		,					output_type='dict'		,					config=SCREAMING_SNAKE_CASE_    )
  lowercase							    =       data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

  return words, normalized_boxes



class        a     (   __snake_case	):
    UpperCAmelCase_ :     List[str]	     =["""pixel_values"""]







    def __init__(			self							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = PILImageResampling.BILINEAR							,		_lowerCamelCase = True							,		_lowerCamelCase = 1 / 2_5_5							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = ""							,		**_lowerCamelCase							,		):
      super().__init__(**_lowerCamelCase       )
      lowercase							    =       size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
      lowercase							    =       get_size_dict(_lowerCamelCase       )

      lowercase							    =       do_resize
      lowercase							    =       size
      lowercase							    =       resample
      lowercase							    =       do_rescale
      lowercase							    =       rescale_value
      lowercase							    =       do_normalize
      lowercase							    =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      lowercase							    =       image_std if image_std is not None else IMAGENET_STANDARD_STD
      lowercase							    =       apply_ocr
      lowercase							    =       ocr_lang
      lowercase							    =       tesseract_config







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = PILImageResampling.BILINEAR							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      lowercase							    =       get_size_dict(_lowerCamelCase       )
      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()}'       )
      lowercase							    =       (size['height'], size['width'])
      return resize(_lowerCamelCase							,		size=_lowerCamelCase							,		resample=_lowerCamelCase							,		data_format=_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      return rescale(_lowerCamelCase							,		scale=_lowerCamelCase							,		data_format=_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		**_lowerCamelCase							,		):
      return normalize(_lowerCamelCase							,		mean=_lowerCamelCase							,		std=_lowerCamelCase							,		data_format=_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase=None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = ChannelDimension.FIRST							,		**_lowerCamelCase							,		):
      lowercase							    =       do_resize if do_resize is not None else self.do_resize
      lowercase							    =       size if size is not None else self.size
      lowercase							    =       get_size_dict(_lowerCamelCase       )
      lowercase							    =       resample if resample is not None else self.resample
      lowercase							    =       do_rescale if do_rescale is not None else self.do_rescale
      lowercase							    =       rescale_factor if rescale_factor is not None else self.rescale_factor
      lowercase							    =       do_normalize if do_normalize is not None else self.do_normalize
      lowercase							    =       image_mean if image_mean is not None else self.image_mean
      lowercase							    =       image_std if image_std is not None else self.image_std
      lowercase							    =       apply_ocr if apply_ocr is not None else self.apply_ocr
      lowercase							    =       ocr_lang if ocr_lang is not None else self.ocr_lang
      lowercase							    =       tesseract_config if tesseract_config is not None else self.tesseract_config

      lowercase							    =       make_list_of_images(_lowerCamelCase       )

      if not valid_images(_lowerCamelCase       ):
        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.'       )

      if do_rescale and rescale_factor is None:
        raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

      if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'       )

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

      # Tesseract OCR to get words + normalized bounding boxes
      if apply_ocr:
        requires_backends(self							,		'pytesseract'       )
        lowercase							    =       []
        lowercase							    =       []
        for image in images:
          lowercase							    =       apply_tesseract(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       )
          words_batch.append(_lowerCamelCase       )
          boxes_batch.append(_lowerCamelCase       )

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

      if do_rescale:
        lowercase							    =       [self.rescale(image=_lowerCamelCase							,		scale=_lowerCamelCase       ) for image in images]

      if do_normalize:
        lowercase							    =       [self.normalize(image=_lowerCamelCase							,		mean=_lowerCamelCase							,		std=_lowerCamelCase       ) for image in images]

      lowercase							    =       [to_channel_dimension_format(_lowerCamelCase							,		_lowerCamelCase       ) for image in images]

      lowercase							    =       BatchFeature(data={'pixel_values': images}							,		tensor_type=_lowerCamelCase       )

      if apply_ocr:
        lowercase							    =       words_batch
        lowercase							    =       boxes_batch
      return data





def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if not all(x.isalpha() for x in string			):
          raise ValueError('String must only contain alphabetic characters.'			)

     lowercase__    :   Optional[int]      =       sorted(string.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == len(set(SCREAMING_SNAKE_CASE_			)			)


if __name__ == "__main__":
       snake_case_			  = input('''Enter a string ''').strip()

       snake_case_			  = is_isogram(input_str)
       print(F'''{input_str} is {'an' if isogram else 'not an'} isogram.''')

