


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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A				:       Union[str, Any]      =  logging.get_logger(__name__)






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


					def __init__(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     SCREAMING_SNAKE_CASE						:		Optional[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						:		int  ,     )      ->						None:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE    )
											__snake_case				=		size if size is not None else {"shortest_edge": 2_5_6}
											__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    )
											__snake_case				=		do_resize
											__snake_case				=		size
											__snake_case				=		resample
											__snake_case				=		do_center_crop
											__snake_case				=		crop_size
											__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.BICUBIC  ,     SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None  ,     **SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     )      ->						np.ndarray:




											'''simple docstring'''

											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE  ,     default_to_square=SCREAMING_SNAKE_CASE    )
											if "shortest_edge" not in size:
																	raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
											__snake_case				=		get_resize_output_image_size(SCREAMING_SNAKE_CASE  ,     size=size["shortest_edge"]  ,     default_to_square=SCREAMING_SNAKE_CASE    )
											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						:		int  ,     SCREAMING_SNAKE_CASE						:		np.ndarray  ,     SCREAMING_SNAKE_CASE						:		Dict[str, int]  ,     SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None  ,     **SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     )      ->						np.ndarray:




											'''simple docstring'''

											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE    )
											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						:		float  ,     SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None  ,     **SCREAMING_SNAKE_CASE						:		Any    )      ->						np.ndarray:




											'''simple docstring'''

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


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




											'''simple docstring'''

											__snake_case				=		do_resize if do_resize is not None else self.do_resize
											__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				=		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				=		crop_size if crop_size is not None else self.crop_size
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE    )
											__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				=		make_list_of_images(SCREAMING_SNAKE_CASE    )

											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."    )

											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."    )

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

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

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

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

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

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

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









def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						assert column_title.isupper()
						__snake_case				=		0
						__snake_case				=		len(_lowerCAmelCase			) - 1
						__snake_case				=		0

						while index >= 0:
												__snake_case				=		(ord(column_title[index]			) - 64) * pow(26   ,						_lowerCAmelCase			)
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





