

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


UpperCamelCase__		 =     logging.get_logger(__name__)

class     __snake_case    (				UpperCamelCase__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["pixel_values"]


       def __init__(		self      , _UpperCamelCase = True      , _UpperCamelCase = 32      , _UpperCamelCase=PILImageResampling.BILINEAR      , _UpperCamelCase = True      , **_UpperCamelCase      , )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							do_resize
              __snake_case       	=							do_rescale
              __snake_case       	=							size_divisor
              __snake_case       	=							resample
              super().__init__(**__lowerCamelCase   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							get_image_size(__lowerCamelCase   )
              # Rounds the height and width down to the closest multiple of size_divisor
              __snake_case       	=							height // size_divisor * size_divisor
              __snake_case       	=							width // size_divisor * size_divisor
              __snake_case       	=							resize(__lowerCamelCase      , (new_h, new_w)      , resample=__lowerCamelCase      , data_format=__lowerCamelCase      , **__lowerCamelCase   )
              return image


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              return rescale(image=__lowerCamelCase      , scale=__lowerCamelCase      , data_format=__lowerCamelCase      , **__lowerCamelCase   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase=None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = ChannelDimension.FIRST      , **_UpperCamelCase      , )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							do_resize if do_resize is not None else self.do_resize
              __snake_case       	=							do_rescale if do_rescale is not None else self.do_rescale
              __snake_case       	=							size_divisor if size_divisor is not None else self.size_divisor
              __snake_case       	=							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"""   )

              __snake_case       	=							make_list_of_images(__lowerCamelCase   )

              if not valid_images(__lowerCamelCase   ):
                     raise ValueError("""Invalid image(s)"""   )

              # All transformations expect numpy arrays.
              __snake_case       	=							[to_numpy_array(__lowerCamelCase   ) for img in images]

              if do_resize:
                     __snake_case       	=							[self.resize(__lowerCamelCase      , size_divisor=__lowerCamelCase      , resample=__lowerCamelCase   ) for image in images]

              if do_rescale:
                     __snake_case       	=							[self.rescale(__lowerCamelCase      , scale=1 / 2_55   ) for image in images]

              __snake_case       	=							[to_channel_dimension_format(__lowerCamelCase      , __lowerCamelCase   ) for image in images]

              __snake_case       	=							{"""pixel_values""": images}
              return BatchFeature(data=__lowerCamelCase      , tensor_type=__lowerCamelCase   )








def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		set()

       # Replace all the whitespace in our sentence
       A__		   =		input_str.replace(''' '''					,	''''''    )
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower()    )
       return len(UpperCamelCase__    ) == 26


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		[False] * 26
       for char in input_str:
              if char.islower():
                     A__		   =		True
              elif char.isupper():
                     A__		   =		True
       return all(UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       return len({char for char in input_str.lower() if char.isalpha()}    ) == 26


def       UpperCamelCase__(			)->None:
       from timeit import timeit

       A__		   =		'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
       print(timeit('''is_pangram()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_faster()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_fastest()'''					,	setup=UpperCamelCase__    )    )
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()





