
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  			=					logging.get_logger(__name__)




def 				a						(							snake_case__:					str						,   snake_case__:					str						,   snake_case__:					Any					):


				'''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 				a						(							snake_case__:					np.ndarray						,   snake_case__:					Optional[str]						,   snake_case__:					Optional[str] = None					):


				'''simple docstring'''
				lowercase_       =					tesseract_config if tesseract_config is not None else ''''''

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

				# filter empty words and corresponding coordinates
				lowercase_       =					[idx for idx, word in enumerate(snake_case__					) if not word.strip()]
				lowercase_       =					[word for idx, word in enumerate(snake_case__					) if idx not in irrelevant_indices]
				lowercase_       =					[coord for idx, coord in enumerate(snake_case__					) if idx not in irrelevant_indices]
				lowercase_       =					[coord for idx, coord in enumerate(snake_case__					) if idx not in irrelevant_indices]
				lowercase_       =					[coord for idx, coord in enumerate(snake_case__					) if idx not in irrelevant_indices]
				lowercase_       =					[coord for idx, coord in enumerate(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(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					):
								lowercase_       =					[x, y, x + w, y + h]
								actual_boxes.append(snake_case__					)

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

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

				return words, normalized_boxes


class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	['pixel_values']
	def __init__(       self  :			Union[str, Any]       ,							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_  :			Optional[str] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = ""       ,							**SCREAMING_SNAKE_CASE_  :			int       ,							)       ->     None:
					super().__init__(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
					lowercase_       =					get_size_dict(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					do_resize
					lowercase_       =					size
					lowercase_       =					resample
					lowercase_       =					apply_ocr
					lowercase_       =					ocr_lang
					lowercase_       =					tesseract_config
	def 						_lowercase  (       self  :			str       ,							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_  :			Dict       ,							)       ->     np.ndarray:
					lowercase_       =					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()}'''       )
					lowercase_       =					(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 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			ImageInput       ,							SCREAMING_SNAKE_CASE_  :			bool = None       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, int] = None       ,							SCREAMING_SNAKE_CASE_  :			PILImageResampling = 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_  :			Dict       ,							)       ->     PIL.Image.Image:
					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(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					resample if resample is not None else self.resample
					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(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.'''       )

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

					if apply_ocr:
									requires_backends(self       ,							'''pytesseract'''       )
									lowercase_       =					[]
									lowercase_       =					[]
									for image in images:
													lowercase_  ,	lowercase_       =					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:
									lowercase_       =					[self.resize(image=SCREAMING_SNAKE_CASE_       ,							size=SCREAMING_SNAKE_CASE_       ,							resample=SCREAMING_SNAKE_CASE_       ) for image in images]

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

					lowercase_       =					BatchFeature(data={'''pixel_values''': images}       ,							tensor_type=SCREAMING_SNAKE_CASE_       )

					if apply_ocr:
									lowercase_       =					words_batch
									lowercase_       =					boxes_batch
					return data





"""simple docstring"""


import math






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					2
  _UpperCAmelCase         =					int(math.sqrt(lowercase						)						)  # Size of every segment
  _UpperCAmelCase         =					[True] * (end + 1)
  _UpperCAmelCase         =					[]

  while start <= end:
    if temp[start] is True:
      in_prime.append(lowercase						)
      for i in range(start * start      ,end + 1      ,lowercase						):
        _UpperCAmelCase         =					False
    start += 1
  prime += in_prime

  _UpperCAmelCase         =					end + 1
  _UpperCAmelCase         =					min(2 * end      ,lowercase						)

  while low <= n:
    _UpperCAmelCase         =					[True] * (high - low + 1)
    for each in in_prime:
      _UpperCAmelCase         =					math.floor(low / each						) * each
      if t < low:
        t += each

      for j in range(lowercase      ,high + 1      ,lowercase						):
        _UpperCAmelCase         =					False

    for j in range(len(lowercase						)						):
      if temp[j] is True:
        prime.append(j + low						)

    _UpperCAmelCase         =					high + 1
    _UpperCAmelCase         =					min(high + end      ,lowercase						)

  return prime

print(sieve(1_0**6))
