from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
				import PIL

__SCREAMING_SNAKE_CASE						:   int         =			logging.get_logger(__name__)
def  snake_case						(__lowercase						)	->	List[List[ImageInput]]:

						'''simple docstring'''



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

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

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

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



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_ = True				,    lowercase_ = None				,    lowercase_ = None				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )
													_snake_case						:					List[Any]    =   size if size is not None else {"shortest_edge": 256}
													_snake_case						:					str    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													_snake_case						:					List[Any]    =   crop_size if crop_size is not None else {"height": 224, "width": 224}
													_snake_case						:					int    =   get_size_dict(lowercase_				,    param_name="crop_size"    )

													_snake_case						:					Union[str, Any]    =   do_resize
													_snake_case						:					Optional[int]    =   size
													_snake_case						:					List[str]    =   do_center_crop
													_snake_case						:					Optional[Any]    =   crop_size
													_snake_case						:					List[Any]    =   resample
													_snake_case						:					str    =   do_rescale
													_snake_case						:					Tuple    =   rescale_factor
													_snake_case						:					Optional[int]    =   offset
													_snake_case						:					List[Any]    =   do_normalize
													_snake_case						:					Optional[int]    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													_snake_case						:					Union[str, Any]    =   image_std if image_std is not None else IMAGENET_STANDARD_STD



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = PILImageResampling.BILINEAR				,    lowercase_ = None				,    **lowercase_				,    ):
													_snake_case						:					List[Any]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													if "shortest_edge" in size:
																			_snake_case						:					Any    =   get_resize_output_image_size(lowercase_				,    size["shortest_edge"]				,    default_to_square=lowercase_    )
													elif "height" in size and "width" in size:
																			_snake_case						:					Optional[Any]    =   (size["height"], size["width"])
													else:
																			raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""    )
													return resize(lowercase_				,    size=lowercase_				,    resample=lowercase_				,    data_format=lowercase_				,    **lowercase_    )



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



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = True				,    lowercase_ = None				,    **lowercase_				,    ):
													_snake_case						:					int    =   image.astype(np.floataa    )
													if offset:
																			_snake_case						:					str    =   image - (scale / 2)
													return rescale(lowercase_				,    scale=lowercase_				,    data_format=lowercase_				,    **lowercase_    )



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



							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				,    ):
													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."    )

													if offset and not do_rescale:
																			raise ValueError("For offset, do_rescale must also be set to True."    )

													# All transformations expect numpy arrays.
													_snake_case						:					List[Any]    =   to_numpy_array(lowercase_    )

													if do_resize:
																			_snake_case						:					List[str]    =   self.resize(image=lowercase_				,    size=lowercase_				,    resample=lowercase_    )

													if do_center_crop:
																			_snake_case						:					Union[str, Any]    =   self.center_crop(lowercase_				,    size=lowercase_    )

													if do_rescale:
																			_snake_case						:					Union[str, Any]    =   self.rescale(image=lowercase_				,    scale=lowercase_				,    offset=lowercase_    )

													if do_normalize:
																			_snake_case						:					Union[str, Any]    =   self.normalize(image=lowercase_				,    mean=lowercase_				,    std=lowercase_    )

													_snake_case						:					Optional[int]    =   to_channel_dimension_format(lowercase_				,    lowercase_    )
													return image



							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_ = None				,    lowercase_ = ChannelDimension.FIRST				,    **lowercase_				,    ):
													_snake_case						:					List[Any]    =   do_resize if do_resize is not None else self.do_resize
													_snake_case						:					Union[str, Any]    =   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]    =   do_rescale if do_rescale is not None else self.do_rescale
													_snake_case						:					int    =   rescale_factor if rescale_factor is not None else self.rescale_factor
													_snake_case						:					List[str]    =   offset if offset is not None else self.offset
													_snake_case						:					Tuple    =   do_normalize if do_normalize is not None else self.do_normalize
													_snake_case						:					Union[str, Any]    =   image_mean if image_mean is not None else self.image_mean
													_snake_case						:					List[Any]    =   image_std if image_std is not None else self.image_std

													_snake_case						:					List[Any]    =   size if size is not None else self.size
													_snake_case						:					Union[str, Any]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													_snake_case						:					str    =   crop_size if crop_size is not None else self.crop_size
													_snake_case						:					Dict    =   get_size_dict(lowercase_				,    param_name="crop_size"    )

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

													_snake_case						:					Optional[int]    =   make_batched(lowercase_    )

													_snake_case						:					Dict    =   [
													    [
													        self._preprocess_image(
													            image=lowercase_				,    do_resize=lowercase_				,    size=lowercase_				,    resample=lowercase_				,    do_center_crop=lowercase_				,    crop_size=lowercase_				,    do_rescale=lowercase_				,    rescale_factor=lowercase_				,    offset=lowercase_				,    do_normalize=lowercase_				,    image_mean=lowercase_				,    image_std=lowercase_				,    data_format=lowercase_				,    )
													        for img in video
													    ]
													    for video in videos
													]

													_snake_case						:					Optional[Any]    =   {"pixel_values": videos}
													return BatchFeature(data=lowercase_				,    tensor_type=lowercase_    )
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()