


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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


A							:					int     						=     logging.get_logger(__name__)





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[List[ImageInput]]:


						'''simple docstring'''






						if isinstance(_lowerCAmelCase      ,	(list, tuple) ) and isinstance(videos[0]      ,	(list, tuple) ) and is_valid_image(videos[0][0] ):
												return videos

						elif isinstance(_lowerCAmelCase      ,	(list, tuple) ) and is_valid_image(videos[0] ):
												return [videos]

						elif is_valid_image(_lowerCAmelCase ):
												return [[videos]]

						raise ValueError(F'''Could not make batched video from {videos}''' )






class     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	["""pixel_values"""]


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = PILImageResampling.BILINEAR     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Union[int, float] = 1 / 2_5_5     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[float, List[float]]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[float, List[float]]] = None     ,    **SCREAMING_SNAKE_CASE	:		str     ,    )			->	None:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)
											__snake_case				=		size if size is not None else {"shortest_edge": 2_2_4}
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE     ,    default_to_square=SCREAMING_SNAKE_CASE						)
											__snake_case				=		crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE     ,    param_name="crop_size"						)

											__snake_case				=		do_resize
											__snake_case				=		size
											__snake_case				=		do_center_crop
											__snake_case				=		crop_size
											__snake_case				=		resample
											__snake_case				=		do_rescale
											__snake_case				=		rescale_factor
											__snake_case				=		do_normalize
											__snake_case				=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__snake_case				=		image_std if image_std is not None else IMAGENET_STANDARD_STD


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		np.ndarray     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int]     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = PILImageResampling.BILINEAR     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = None     ,    **SCREAMING_SNAKE_CASE	:		List[str]     ,    )			->	np.ndarray:




											'''simple docstring'''

											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE     ,    default_to_square=SCREAMING_SNAKE_CASE						)
											if "shortest_edge" in size:
																	__snake_case				=		get_resize_output_image_size(SCREAMING_SNAKE_CASE     ,    size["shortest_edge"]     ,    default_to_square=SCREAMING_SNAKE_CASE						)
											elif "height" in size and "width" in size:
																	__snake_case				=		(size["height"], size["width"])
											else:
																	raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''						)
											return resize(SCREAMING_SNAKE_CASE     ,    size=SCREAMING_SNAKE_CASE     ,    resample=SCREAMING_SNAKE_CASE     ,    data_format=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		np.ndarray     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int]     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = None     ,    **SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    )			->	np.ndarray:




											'''simple docstring'''

											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE						)
											if "height" not in size or "width" not in size:
																	raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''						)
											return center_crop(SCREAMING_SNAKE_CASE     ,    size=(size["height"], size["width"])     ,    data_format=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		np.ndarray     ,    SCREAMING_SNAKE_CASE	:		Union[int, float]     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = None     ,    **SCREAMING_SNAKE_CASE	:		List[str]     ,    )			->	Dict:




											'''simple docstring'''

											return rescale(SCREAMING_SNAKE_CASE     ,    scale=SCREAMING_SNAKE_CASE     ,    data_format=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		np.ndarray     ,    SCREAMING_SNAKE_CASE	:		Union[float, List[float]]     ,    SCREAMING_SNAKE_CASE	:		Union[float, List[float]]     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = None     ,    **SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    )			->	np.ndarray:




											'''simple docstring'''

											return normalize(SCREAMING_SNAKE_CASE     ,    mean=SCREAMING_SNAKE_CASE     ,    std=SCREAMING_SNAKE_CASE     ,    data_format=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		ImageInput     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = None     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		float = None     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[float, List[float]]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[float, List[float]]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[ChannelDimension] = ChannelDimension.FIRST     ,    )			->	np.ndarray:




											'''simple docstring'''

											if do_resize and size is None or resample is None:
																	raise ValueError("Size and resample 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."						)

											# All transformations expect numpy arrays.
											__snake_case				=		to_numpy_array(SCREAMING_SNAKE_CASE						)

											if do_resize:
																	__snake_case				=		self.resize(image=SCREAMING_SNAKE_CASE     ,    size=SCREAMING_SNAKE_CASE     ,    resample=SCREAMING_SNAKE_CASE						)

											if do_center_crop:
																	__snake_case				=		self.center_crop(SCREAMING_SNAKE_CASE     ,    size=SCREAMING_SNAKE_CASE						)

											if do_rescale:
																	__snake_case				=		self.rescale(image=SCREAMING_SNAKE_CASE     ,    scale=SCREAMING_SNAKE_CASE						)

											if do_normalize:
																	__snake_case				=		self.normalize(image=SCREAMING_SNAKE_CASE     ,    mean=SCREAMING_SNAKE_CASE     ,    std=SCREAMING_SNAKE_CASE						)

											__snake_case				=		to_channel_dimension_format(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											return image






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		ImageInput     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = None     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		float = None     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[float, List[float]]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[float, List[float]]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, TensorType]] = None     ,    SCREAMING_SNAKE_CASE	:		ChannelDimension = ChannelDimension.FIRST     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	PIL.Image.Image:




											'''simple docstring'''

											__snake_case				=		do_resize if do_resize is not None else self.do_resize
											__snake_case				=		resample if resample is not None else self.resample
											__snake_case				=		do_center_crop if do_center_crop is not None else self.do_center_crop
											__snake_case				=		do_rescale if do_rescale is not None else self.do_rescale
											__snake_case				=		rescale_factor if rescale_factor is not None else self.rescale_factor
											__snake_case				=		do_normalize if do_normalize is not None else self.do_normalize
											__snake_case				=		image_mean if image_mean is not None else self.image_mean
											__snake_case				=		image_std if image_std is not None else self.image_std

											__snake_case				=		size if size is not None else self.size
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE     ,    default_to_square=SCREAMING_SNAKE_CASE						)
											__snake_case				=		crop_size if crop_size is not None else self.crop_size
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE     ,    param_name="crop_size"						)

											if not valid_images(SCREAMING_SNAKE_CASE						):
																	raise ValueError(
																	    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																	    "torch.Tensor, tf.Tensor or jax.ndarray."						)

											__snake_case				=		make_batched(SCREAMING_SNAKE_CASE						)

											__snake_case				=		[
											    [
											        self._preprocess_image(
											            image=SCREAMING_SNAKE_CASE     ,    do_resize=SCREAMING_SNAKE_CASE     ,    size=SCREAMING_SNAKE_CASE     ,    resample=SCREAMING_SNAKE_CASE     ,    do_center_crop=SCREAMING_SNAKE_CASE     ,    crop_size=SCREAMING_SNAKE_CASE     ,    do_rescale=SCREAMING_SNAKE_CASE     ,    rescale_factor=SCREAMING_SNAKE_CASE     ,    do_normalize=SCREAMING_SNAKE_CASE     ,    image_mean=SCREAMING_SNAKE_CASE     ,    image_std=SCREAMING_SNAKE_CASE     ,    data_format=SCREAMING_SNAKE_CASE     ,    )
											        for img in video
											    ]
											    for video in videos
											]

											__snake_case				=		{"pixel_values": videos}
											return BatchFeature(data=SCREAMING_SNAKE_CASE     ,    tensor_type=SCREAMING_SNAKE_CASE						)









def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		set()

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





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		[False] * 26
						for char in input_str:
												if char.islower():
																		__snake_case				=		True
												elif char.isupper():
																		__snake_case				=		True
						return all(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from timeit import timeit

						__snake_case				=		"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()





