


'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase				=logging.get_logger(__name__)






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["pixel_values"]






       def __init__(     self    ,				snake_case = True    ,				snake_case = 3_2    ,				snake_case=PILImageResampling.BILINEAR    ,				snake_case = True    ,				**snake_case    ,				)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =do_resize
          _UpperCAmelCase  :  Optional[Any]				       =do_rescale
          _UpperCAmelCase  :  List[Any]				       =size_divisor
          _UpperCAmelCase  :  str				       =resample
          super().__init__(**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case = None    ,				**snake_case)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[int]				       =get_image_size(snake_case)
          # Rounds the height and width down to the closest multiple of size_divisor
          _UpperCAmelCase  :  Dict				       =height // size_divisor * size_divisor
          _UpperCAmelCase  :  List[str]				       =width // size_divisor * size_divisor
          _UpperCAmelCase  :  Optional[Any]				       =resize(snake_case    ,				(new_h, new_w)    ,				resample=snake_case    ,				data_format=snake_case    ,				**snake_case)
          return image






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = None    ,				**snake_case)		->    np.ndarray:




          '''simple docstring'''
          return rescale(image=snake_case    ,				scale=snake_case    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None    ,				snake_case = None    ,				snake_case=None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = ChannelDimension.FIRST    ,				**snake_case    ,				)		->    BatchFeature:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase  :  Optional[int]				       =do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase  :  List[Any]				       =size_divisor if size_divisor is not None else self.size_divisor
          _UpperCAmelCase  :  Optional[Any]				       =resample if resample is not None else self.resample

          if do_resize and size_divisor is None:
             raise ValueError('size_divisor is required for resizing')

          _UpperCAmelCase  :  str				       =make_list_of_images(snake_case)

          if not valid_images(snake_case):
             raise ValueError('Invalid image(s)')

          # All transformations expect numpy arrays.
          _UpperCAmelCase  :  Any				       =[to_numpy_array(snake_case) for img in images]

          if do_resize:
             _UpperCAmelCase  :  List[str]				       =[self.resize(snake_case    ,				size_divisor=snake_case    ,				resample=snake_case) for image in images]

          if do_rescale:
             _UpperCAmelCase  :  Union[str, Any]				       =[self.rescale(snake_case    ,				scale=1 / 2_5_5) for image in images]

          _UpperCAmelCase  :  Any				       =[to_channel_dimension_format(snake_case    ,				snake_case) for image in images]

          _UpperCAmelCase  :  str				       ={'pixel_values': images}
          return BatchFeature(data=snake_case    ,				tensor_type=snake_case)





'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =set()

   # Replace all the whitespace in our sentence
   _UpperCAmelCase  :  Dict				       =input_str.replace(' '			,       ''				)
   for alpha in input_str:
      if "a" <= alpha.lower() <= "z":
         frequency.add(alpha.lower()				)
   return len(__lowerCamelCase				) == 2_6





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Tuple				       =[False] * 2_6
   for char in input_str:
      if char.islower():
         _UpperCAmelCase  :  Dict				       =True
      elif char.isupper():
         _UpperCAmelCase  :  List[str]				       =True
   return all(__lowerCamelCase				)





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   return len({char for char in input_str.lower() if char.isalpha()}				) == 2_6





def lowerCamelCase__     (		):







   '''simple docstring'''




   from timeit import timeit

   _UpperCAmelCase  :  List[Any]				       ='from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
   print(timeit('is_pangram()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_faster()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_fastest()'			,       setup=__lowerCamelCase				)				)
   # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
   # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()

