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


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



class        lowercase_   (					__snake_case		):
       _lowerCamelCase						=		['pixel_values']



       def __init__(						self				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = PILImageResampling.BILINEAR				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = True				,    lowercase_ = 1 / 255				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = None				,    **lowercase_				,    ):
             super().__init__(**__lowerCAmelCase    )
             _snake_case						:					List[Any]    =   size if size is not None else {"shortest_edge": 256}
             _snake_case						:					List[str]    =   get_size_dict(__lowerCAmelCase				,    default_to_square=__lowerCAmelCase    )
             _snake_case						:					Dict    =   crop_size if crop_size is not None else {"height": 224, "width": 224}
             _snake_case						:					int    =   get_size_dict(__lowerCAmelCase    )
             _snake_case						:					str    =   do_resize
             _snake_case						:					str    =   size
             _snake_case						:					List[Any]    =   resample
             _snake_case						:					int    =   do_center_crop
             _snake_case						:					Any    =   crop_size
             _snake_case						:					Optional[Any]    =   do_rescale
             _snake_case						:					Any    =   rescale_factor
             _snake_case						:					Tuple    =   do_normalize
             _snake_case						:					Optional[int]    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             _snake_case						:					Optional[int]    =   image_std if image_std is not None else IMAGENET_STANDARD_STD



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = PILImageResampling.BICUBIC				,    lowercase_ = None				,    **lowercase_				,    ):
             _snake_case						:					Dict    =   get_size_dict(__lowerCAmelCase				,    default_to_square=__lowerCAmelCase    )
             if "shortest_edge" not in size:
                   raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
             _snake_case						:					Dict    =   get_resize_output_image_size(__lowerCAmelCase				,    size=size["shortest_edge"]				,    default_to_square=__lowerCAmelCase    )
             return resize(__lowerCAmelCase				,    size=__lowerCAmelCase				,    resample=__lowerCAmelCase				,    data_format=__lowerCAmelCase				,    **__lowerCAmelCase    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_				,    ):
             _snake_case						:					Any    =   get_size_dict(__lowerCAmelCase    )
             return center_crop(__lowerCAmelCase				,    size=(size["height"], size["width"])				,    data_format=__lowerCAmelCase				,    **__lowerCAmelCase    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_    ):
             return rescale(__lowerCAmelCase				,    scale=__lowerCAmelCase				,    data_format=__lowerCAmelCase				,    **__lowerCAmelCase    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_				,    ):
             return normalize(__lowerCAmelCase				,    mean=__lowerCAmelCase				,    std=__lowerCAmelCase				,    data_format=__lowerCAmelCase				,    **__lowerCAmelCase    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = ChannelDimension.FIRST				,    **lowercase_				,    ):
             _snake_case						:					str    =   do_resize if do_resize is not None else self.do_resize
             _snake_case						:					Union[str, Any]    =   size if size is not None else self.size
             _snake_case						:					str    =   get_size_dict(__lowerCAmelCase				,    default_to_square=__lowerCAmelCase    )
             _snake_case						:					int    =   resample if resample is not None else self.resample
             _snake_case						:					str    =   do_center_crop if do_center_crop is not None else self.do_center_crop
             _snake_case						:					Union[str, Any]    =   crop_size if crop_size is not None else self.crop_size
             _snake_case						:					Dict    =   get_size_dict(__lowerCAmelCase    )
             _snake_case						:					int    =   do_rescale if do_rescale is not None else self.do_rescale
             _snake_case						:					str    =   rescale_factor if rescale_factor is not None else self.rescale_factor
             _snake_case						:					Union[str, Any]    =   do_normalize if do_normalize is not None else self.do_normalize
             _snake_case						:					Dict    =   image_mean if image_mean is not None else self.image_mean
             _snake_case						:					Tuple    =   image_std if image_std is not None else self.image_std

             _snake_case						:					str    =   make_list_of_images(__lowerCAmelCase    )

             if not valid_images(__lowerCAmelCase    ):
                   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						:					Optional[Any]    =   [to_numpy_array(__lowerCAmelCase    ) for image in images]

             if do_resize:
                   _snake_case						:					List[str]    =   [self.resize(image=__lowerCAmelCase				,    size=__lowerCAmelCase				,    resample=__lowerCAmelCase    ) for image in images]

             if do_center_crop:
                   _snake_case						:					Optional[Any]    =   [self.center_crop(image=__lowerCAmelCase				,    size=__lowerCAmelCase    ) for image in images]

             if do_rescale:
                   _snake_case						:					Optional[Any]    =   [self.rescale(image=__lowerCAmelCase				,    scale=__lowerCAmelCase    ) for image in images]

             if do_normalize:
                   _snake_case						:					Dict    =   [self.normalize(image=__lowerCAmelCase				,    mean=__lowerCAmelCase				,    std=__lowerCAmelCase    ) for image in images]

             _snake_case						:					int    =   [to_channel_dimension_format(__lowerCAmelCase				,    __lowerCAmelCase    ) for image in images]

             _snake_case						:					Tuple    =   {"pixel_values": images}
             return BatchFeature(data=__lowerCAmelCase				,    tensor_type=__lowerCAmelCase    )
def  snake_case						(__lowercase						)	->	list:

						'''simple docstring'''



						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__lowercase						)						)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
				__import__('doctest').testmod()