


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

lowerCamelCase						=		logging.get_logger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




  """simple docstring"""






  return [
      int(1_000 * (box[0] / width)	),
      int(1_000 * (box[1] / height)	),
      int(1_000 * (box[2] / width)	),
      int(1_000 * (box[3] / height)	),
  ]






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




  """simple docstring"""






  lowerCAmelCase__       :      int   					= to_pil_image(_a	)
  lowerCAmelCase__       :      Optional[Any]   					= pil_image.size
  lowerCAmelCase__       :      List[Any]   					= pytesseract.image_to_data(_a     ,       lang=_a     ,       output_type='''dict'''     ,       config=_a	)
  lowerCAmelCase__       :      str   					= data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

  # filter empty words and corresponding coordinates
  lowerCAmelCase__       :      Any   					= [idx for idx, word in enumerate(_a	) if not word.strip()]
  lowerCAmelCase__       :      int   					= [word for idx, word in enumerate(_a	) if idx not in irrelevant_indices]
  lowerCAmelCase__       :      Union[str, Any]   					= [coord for idx, coord in enumerate(_a	) if idx not in irrelevant_indices]
  lowerCAmelCase__       :      int   					= [coord for idx, coord in enumerate(_a	) if idx not in irrelevant_indices]
  lowerCAmelCase__       :      Optional[int]   					= [coord for idx, coord in enumerate(_a	) if idx not in irrelevant_indices]
  lowerCAmelCase__       :      Optional[Any]   					= [coord for idx, coord in enumerate(_a	) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  lowerCAmelCase__       :      List[Any]   					= []
  for x, y, w, h in zip(_a     ,       _a     ,       _a     ,       _a	):
    lowerCAmelCase__       :      Optional[Any]   					= [x, y, x + w, y + h]
    actual_boxes.append(_a	)

  # finally, normalize the bounding boxes
  lowerCAmelCase__       :      Optional[Any]   					= []
  for box in actual_boxes:
    normalized_boxes.append(normalize_box(_a     ,       _a     ,       _a	)	)

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

  return words, normalized_boxes





class  _a							(    _lowercase):
 _a  :  Optional[Any]				   =							['''pixel_values''']




 def __init__( self						:		str		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Dict[str, int] = None		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = PILImageResampling.BILINEAR		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		float = 1 / 255		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]] = None		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]] = None		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		, _SCREAMING_SNAKE_CASE						:		Optional[str] = ""		, **_SCREAMING_SNAKE_CASE						:		int		, )->	None:
   super().__init__(**_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Optional[Any]   					= size if size is not None else {'''height''': 224, '''width''': 224}
   lowerCAmelCase__       :      Optional[Any]   					= get_size_dict(_SCREAMING_SNAKE_CASE		)

   lowerCAmelCase__       :      int   					= do_resize
   lowerCAmelCase__       :      str   					= size
   lowerCAmelCase__       :      str   					= resample
   lowerCAmelCase__       :      Any   					= do_rescale
   lowerCAmelCase__       :      List[Any]   					= rescale_value
   lowerCAmelCase__       :      Tuple   					= do_normalize
   lowerCAmelCase__       :      int   					= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowerCAmelCase__       :      Dict   					= image_std if image_std is not None else IMAGENET_STANDARD_STD
   lowerCAmelCase__       :      int   					= apply_ocr
   lowerCAmelCase__       :      Tuple   					= ocr_lang
   lowerCAmelCase__       :      Optional[Any]   					= tesseract_config




 def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Dict[str, int]		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = PILImageResampling.BILINEAR		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	np.ndarray:
   lowerCAmelCase__       :      str   					= get_size_dict(_SCREAMING_SNAKE_CASE		)
   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()}'		)
   lowerCAmelCase__       :      Optional[int]   					= (size['''height'''], size['''width'''])
   return resize(_SCREAMING_SNAKE_CASE		, size=_SCREAMING_SNAKE_CASE		, resample=_SCREAMING_SNAKE_CASE		, data_format=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Union[int, float]		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Any		, )->	np.ndarray:
   return rescale(_SCREAMING_SNAKE_CASE		, scale=_SCREAMING_SNAKE_CASE		, data_format=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]]		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]]		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		, )->	np.ndarray:
   return normalize(_SCREAMING_SNAKE_CASE		, mean=_SCREAMING_SNAKE_CASE		, std=_SCREAMING_SNAKE_CASE		, data_format=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		ImageInput		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		Dict[str, int] = None		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		float = None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]] = None		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]] = None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, TensorType]] = None		, _SCREAMING_SNAKE_CASE						:		ChannelDimension = ChannelDimension.FIRST		, **_SCREAMING_SNAKE_CASE						:		List[Any]		, )->	PIL.Image.Image:
   lowerCAmelCase__       :      str   					= do_resize if do_resize is not None else self.do_resize
   lowerCAmelCase__       :      Dict   					= size if size is not None else self.size
   lowerCAmelCase__       :      Union[str, Any]   					= get_size_dict(_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      List[str]   					= resample if resample is not None else self.resample
   lowerCAmelCase__       :      Tuple   					= do_rescale if do_rescale is not None else self.do_rescale
   lowerCAmelCase__       :      Dict   					= rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCAmelCase__       :      Optional[int]   					= do_normalize if do_normalize is not None else self.do_normalize
   lowerCAmelCase__       :      Optional[Any]   					= image_mean if image_mean is not None else self.image_mean
   lowerCAmelCase__       :      Optional[int]   					= image_std if image_std is not None else self.image_std
   lowerCAmelCase__       :      str   					= apply_ocr if apply_ocr is not None else self.apply_ocr
   lowerCAmelCase__       :      Any   					= ocr_lang if ocr_lang is not None else self.ocr_lang
   lowerCAmelCase__       :      Optional[Any]   					= tesseract_config if tesseract_config is not None else self.tesseract_config

   lowerCAmelCase__       :      int   					= make_list_of_images(_SCREAMING_SNAKE_CASE		)

   if not valid_images(_SCREAMING_SNAKE_CASE		):
     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.
   lowerCAmelCase__       :      Optional[Any]   					= [to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

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

   if do_resize:
     lowerCAmelCase__       :      Dict   					= [self.resize(image=_SCREAMING_SNAKE_CASE		, size=_SCREAMING_SNAKE_CASE		, resample=_SCREAMING_SNAKE_CASE		) for image in images]

   if do_rescale:
     lowerCAmelCase__       :      List[Any]   					= [self.rescale(image=_SCREAMING_SNAKE_CASE		, scale=_SCREAMING_SNAKE_CASE		) for image in images]

   if do_normalize:
     lowerCAmelCase__       :      Any   					= [self.normalize(image=_SCREAMING_SNAKE_CASE		, mean=_SCREAMING_SNAKE_CASE		, std=_SCREAMING_SNAKE_CASE		) for image in images]

   lowerCAmelCase__       :      str   					= [to_channel_dimension_format(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		) for image in images]

   lowerCAmelCase__       :      Any   					= BatchFeature(data={'''pixel_values''': images}		, tensor_type=_SCREAMING_SNAKE_CASE		)

   if apply_ocr:
     lowerCAmelCase__       :      Optional[Any]   					= words_batch
     lowerCAmelCase__       :      Tuple   					= boxes_batch
   return data








from math import isqrt






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= [True] * max_number
		for i in range(2     ,       isqrt(max_number - 1	) + 1	):
				if is_prime[i]:
						for j in range(i**2     ,       _a     ,       _a	):
								lowerCAmelCase__       :      int   					= False

		return [i for i in range(2     ,       _a	) if is_prime[i]]






def 					lowerCamelCase_   (	_a = 10**8	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= calculate_prime_numbers(max_number // 2	)

		lowerCAmelCase__       :      Tuple   					= 0
		lowerCAmelCase__       :      List[Any]   					= 0
		lowerCAmelCase__       :      Optional[int]   					= len(_a	) - 1
		while left <= right:
				while prime_numbers[left] * prime_numbers[right] >= max_number:
						right -= 1
				semiprimes_count += right - left + 1
				left += 1

		return semiprimes_count


if __name__ == "__main__":
		print(f'''{solution() = }''')




