


"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_			     =  logging.get_logger(__name__)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				['pixel_values']

		def __init__(  self							, _a = True							, _a = None							, _a = PILImageResampling.BICUBIC							, _a = True							, _a = True							, _a = 1 / 255							, _a = None							, _a = True							, _a = None							, _a = None							, **_a							, ):
				super().__init__(**_a      )
				__a           =			size if size is not None else {'''height''': 224, '''width''': 224}
				__a           =			get_size_dict(_a      )
				__a           =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
				__a           =			get_size_dict(_a							, default_to_square=_a							, param_name='''crop_size'''      )

				__a           =			do_resize
				__a           =			do_rescale
				__a           =			do_normalize
				__a           =			do_center_crop
				__a           =			crop_size
				__a           =			size
				__a           =			resample
				__a           =			rescale_factor
				__a           =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				__a           =			image_std if image_std is not None else IMAGENET_DEFAULT_STD

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = PILImageResampling.BILINEAR							, _a = None							, **_a							, ):
				__a           =			get_size_dict(_a      )

				if "shortest_edge" in size:
						__a           =			get_resize_output_image_size(_a							, size=size['''shortest_edge''']							, default_to_square=_a      )
						# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
				elif "height" in size and "width" in size:
						__a           =			(size['''height'''], size['''width'''])
				else:
						raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''      )
				return resize(_a							, size=_a							, resample=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = None							, **_a							, ):
				__a           =			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 			__UpperCAmelCase       (  self							, _a							, _a							, _a = None							, **_a      ):
				return rescale(_a							, scale=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a = None							, **_a							, ):
				return normalize(_a							, mean=_a							, std=_a							, data_format=_a							, **_a      )





		def 			__UpperCAmelCase       (  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 = ChannelDimension.FIRST							, **_a							, ):
				__a           =			do_resize if do_resize is not None else self.do_resize
				__a           =			do_rescale if do_rescale is not None else self.do_rescale
				__a           =			do_normalize if do_normalize is not None else self.do_normalize
				__a           =			do_center_crop if do_center_crop is not None else self.do_center_crop
				__a           =			crop_size if crop_size is not None else self.crop_size
				__a           =			get_size_dict(_a							, param_name='''crop_size'''							, default_to_square=_a      )
				__a           =			resample if resample is not None else self.resample
				__a           =			rescale_factor if rescale_factor is not None else self.rescale_factor
				__a           =			image_mean if image_mean is not None else self.image_mean
				__a           =			image_std if image_std is not None else self.image_std

				__a           =			size if size is not None else self.size
				__a           =			get_size_dict(_a      )

				if not is_batched(_a      ):
						__a           =			[images]

				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.'''      )

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

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

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

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

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

				__a           =			[to_channel_dimension_format(_a							, _a      ) for image in images]

				__a           =			{'''pixel_values''': images}
				return BatchFeature(data=_a							, tensor_type=_a      )










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     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(lowerCAmelCase__			) == 26

def        lowercase							(		lowerCAmelCase__ :     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(lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     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        lowercase							(		) ->      None:
		from timeit import timeit

		__a           =			'''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()






