

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


snake_case_			  = logging.get_logger(__name__)


if is_vision_available():
       import PIL


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       List[Any]														=     ["""pixel_values"""]





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

         lowercase__    :   Optional[Any]      =       do_resize
         lowercase__    :   List[Any]      =       size
         lowercase__    :   Dict      =       resample
         lowercase__    :   Tuple      =       do_center_crop
         lowercase__    :   Any      =       crop_size
         lowercase__    :   Tuple      =       do_rescale
         lowercase__    :   Optional[int]      =       rescale_factor
         lowercase__    :   List[Any]      =       do_normalize
         lowercase__    :   Any      =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase__    :   Optional[int]      =       image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase__    :   Optional[int]      =       do_convert_rgb





    def       snake_case_				(      self						,   a						,   a						,   a = PILImageResampling.BICUBIC						,   a = None						,   **a						,   ):
         lowercase__    :   Dict      =       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()}""")
         lowercase__    :   List[str]      =       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						,   a						,   a						,   a = None						,   **a						,   ):
         lowercase__    :   List[Any]      =       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						,   a						,   a						,   a = None						,   **a						,   ):
         return rescale(a						,   scale=a						,   data_format=a						,   **a)





    def       snake_case_				(      self						,   a						,   a						,   a						,   a = None						,   **a						,   ):
         return normalize(a						,   mean=a						,   std=a						,   data_format=a						,   **a)





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

         lowercase__    :   str      =       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:
              lowercase__    :   str      =       [convert_to_rgb(a) for image in images]

         # All transformations expect numpy arrays.
         lowercase__    :   Dict      =       [to_numpy_array(a) for image in images]

         if do_resize:
              lowercase__    :   Tuple      =       [self.resize(image=a						,   size=a						,   resample=a) for image in images]

         if do_center_crop:
              lowercase__    :   Dict      =       [self.center_crop(image=a						,   size=a) for image in images]

         if do_rescale:
              lowercase__    :   Optional[int]      =       [self.rescale(image=a						,   scale=a) for image in images]

         if do_normalize:
              lowercase__    :   int      =       [self.normalize(image=a						,   mean=a						,   std=a) for image in images]

         lowercase__    :   List[Any]      =       [to_channel_dimension_format(a						,   a) for image in images]

         lowercase__    :   List[str]      =       {'pixel_values': images}
         return BatchFeature(data=a						,   tensor_type=a)




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



     '''simple docstring'''





     lowercase__    :   str      =       set()

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

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



     '''simple docstring'''





     lowercase__    :   Dict      =       [False] * 26
     for char in input_str:
          if char.islower():
               lowercase__    :   List[Any]      =       True
          elif char.isupper():
               lowercase__    :   Optional[Any]      =       True
     return all(SCREAMING_SNAKE_CASE_			)

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



     '''simple docstring'''





     return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def 	snake_case__			(    ):



     '''simple docstring'''





     from timeit import timeit

     lowercase__    :   Union[str, Any]      =       'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
     print(timeit('is_pangram()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_faster()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_fastest()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()

