

'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowerCAmelCase            =		logging.get_logger(__name__)


if is_vision_available():
      import PIL






class     _SCREAMING_SNAKE_CASE       (       __a      ):
 __SCREAMING_SNAKE_CASE	:Any     			=	["""pixel_values"""]



 def __init__(       self						:       Tuple					, a__						:       Any = True					, a__						:       Optional[Any] = None					, a__						:       int = PILImageResampling.BICUBIC					, a__						:       List[Any] = True					, a__						:       List[Any] = None					, a__						:       int = True					, a__						:       List[str] = 1 / 255					, a__						:       List[Any] = True					, a__						:       Any = None					, a__						:       Any = None					, a__						:       Dict = True					, **a__						:       Any					, ):
      super().__init__(**a__     )
      __magic_name__		  =							size if size is not None else {'shortest_edge': 224}
      __magic_name__		  =							get_size_dict(a__					, default_to_square=a__     )
      __magic_name__		  =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
      __magic_name__		  =							get_size_dict(a__					, default_to_square=a__					, param_name='''crop_size'''     )

      __magic_name__		  =							do_resize
      __magic_name__		  =							size
      __magic_name__		  =							resample
      __magic_name__		  =							do_center_crop
      __magic_name__		  =							crop_size
      __magic_name__		  =							do_rescale
      __magic_name__		  =							rescale_factor
      __magic_name__		  =							do_normalize
      __magic_name__		  =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      __magic_name__		  =							image_std if image_std is not None else OPENAI_CLIP_STD
      __magic_name__		  =							do_convert_rgb



 def 					snake_case__    (       self						:       List[str]					, a__						:       Union[str, Any]					, a__						:       Optional[int]					, a__						:       Union[str, Any] = PILImageResampling.BICUBIC					, a__						:       List[str] = None					, **a__						:       int					, ):
      __magic_name__		  =							get_size_dict(a__					, default_to_square=a__     )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
      __magic_name__		  =							get_resize_output_image_size(a__					, size=size['''shortest_edge''']					, default_to_square=a__     )
      return resize(a__					, size=a__					, resample=a__					, data_format=a__					, **a__     )



 def 					snake_case__    (       self						:       Any					, a__						:       Any					, a__						:       int					, a__						:       List[str] = None					, **a__						:       List[Any]					, ):
      __magic_name__		  =							get_size_dict(a__     )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''     )
      return center_crop(a__					, size=(size['''height'''], size['''width'''])					, data_format=a__					, **a__     )



 def 					snake_case__    (       self						:       Tuple					, a__						:       Dict					, a__						:       Optional[int]					, a__						:       Tuple = None					, **a__						:       Optional[int]					, ):
      return rescale(a__					, scale=a__					, data_format=a__					, **a__     )



 def 					snake_case__    (       self						:       Any					, a__						:       Tuple					, a__						:       Union[str, Any]					, a__						:       Dict					, a__						:       Any = None					, **a__						:       Optional[Any]					, ):
      return normalize(a__					, mean=a__					, std=a__					, data_format=a__					, **a__     )



 def 					snake_case__    (       self						:       Optional[int]					, a__						:       Tuple					, a__						:       Tuple = None					, a__						:       int = None					, a__						:       Any = None					, a__						:       Union[str, Any] = None					, a__						:       int = None					, a__						:       Optional[Any] = None					, a__						:       List[Any] = None					, a__						:       Optional[int] = None					, a__						:       str = None					, a__						:       Dict = None					, a__						:       Dict = None					, a__						:       Union[str, Any] = None					, a__						:       Tuple = ChannelDimension.FIRST					, **a__						:       List[Any]					, ):
      __magic_name__		  =							do_resize if do_resize is not None else self.do_resize
      __magic_name__		  =							size if size is not None else self.size
      __magic_name__		  =							get_size_dict(a__					, param_name='''size'''					, default_to_square=a__     )
      __magic_name__		  =							resample if resample is not None else self.resample
      __magic_name__		  =							do_center_crop if do_center_crop is not None else self.do_center_crop
      __magic_name__		  =							crop_size if crop_size is not None else self.crop_size
      __magic_name__		  =							get_size_dict(a__					, param_name='''crop_size'''					, default_to_square=a__     )
      __magic_name__		  =							do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__		  =							rescale_factor if rescale_factor is not None else self.rescale_factor
      __magic_name__		  =							do_normalize if do_normalize is not None else self.do_normalize
      __magic_name__		  =							image_mean if image_mean is not None else self.image_mean
      __magic_name__		  =							image_std if image_std is not None else self.image_std
      __magic_name__		  =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      __magic_name__		  =							make_list_of_images(a__     )

      if not valid_images(a__     ):
           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_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop 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('''Image mean and std must be specified if do_normalize is True.'''     )

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
           __magic_name__		  =							[convert_to_rgb(a__     ) for image in images]

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

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

      if do_center_crop:
           __magic_name__		  =							[self.center_crop(image=a__					, size=a__     ) for image in images]

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

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

      __magic_name__		  =							[to_channel_dimension_format(a__					, a__     ) for image in images]

      __magic_name__		  =							{'pixel_values': images}
      return BatchFeature(data=a__					, tensor_type=a__     )










'''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()

