

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

__lowercase										= logging.get_logger(__name__)



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''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      lowerCamelCase_					(       lowerCamelCase__			):






    '''simple docstring'''






    a__     :						Any      =				["""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			,			)	->     Union[str, Any]:
        super().__init__(**__lowercase)
        __UpperCamelCase					:str        =		size if size is not None else {"shortest_edge": 256}
        __UpperCamelCase					:str        =		get_size_dict(__lowercase			,			default_to_square=__lowercase)
        __UpperCamelCase					:Any        =		crop_size if crop_size is not None else {"height": 224, "width": 224}
        __UpperCamelCase					:Union[str, Any]        =		get_size_dict(__lowercase			,			param_name='''crop_size''')

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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = PILImageResampling.BILINEAR			,			__lowercase = None			,			**__lowercase			,			)	->     Tuple:
        __UpperCamelCase					:Optional[Any]        =		get_size_dict(__lowercase			,			default_to_square=__lowercase)
        if "shortest_edge" in size:
            __UpperCamelCase					:Any        =		get_resize_output_image_size(__lowercase			,			size['''shortest_edge''']			,			default_to_square=__lowercase)
        elif "height" in size and "width" in size:
            __UpperCamelCase					:Optional[int]        =		(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			,			)	->     Union[str, Any]:
        __UpperCamelCase					:str        =		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			,			)	->     int:
        __UpperCamelCase					:List[Any]        =		image.astype(np.floataa)
        if offset:
            __UpperCamelCase					:Optional[Any]        =		image - (scale / 2)
        return rescale(__lowercase			,			scale=__lowercase			,			data_format=__lowercase			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = None			,			**__lowercase			,			)	->     Dict:
        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			,			)	->     List[str]:




        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.
        __UpperCamelCase					:Dict        =		to_numpy_array(__lowercase)

        if do_resize:
            __UpperCamelCase					:Any        =		self.resize(image=__lowercase			,			size=__lowercase			,			resample=__lowercase)

        if do_center_crop:
            __UpperCamelCase					:Tuple        =		self.center_crop(__lowercase			,			size=__lowercase)

        if do_rescale:
            __UpperCamelCase					:Optional[Any]        =		self.rescale(image=__lowercase			,			scale=__lowercase			,			offset=__lowercase)

        if do_normalize:
            __UpperCamelCase					:List[str]        =		self.normalize(image=__lowercase			,			mean=__lowercase			,			std=__lowercase)

        __UpperCamelCase					:str        =		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			,			)	->     int:
        __UpperCamelCase					:str        =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase					:int        =		resample if resample is not None else self.resample
        __UpperCamelCase					:Union[str, Any]        =		do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCamelCase					:Dict        =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase					:int        =		rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase					:Any        =		offset if offset is not None else self.offset
        __UpperCamelCase					:Any        =		do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase					:str        =		image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase					:str        =		image_std if image_std is not None else self.image_std

        __UpperCamelCase					:Optional[Any]        =		size if size is not None else self.size
        __UpperCamelCase					:List[Any]        =		get_size_dict(__lowercase			,			default_to_square=__lowercase)
        __UpperCamelCase					:Any        =		crop_size if crop_size is not None else self.crop_size
        __UpperCamelCase					: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.''')

        __UpperCamelCase					:str        =		make_batched(__lowercase)

        __UpperCamelCase					:str        =		[
            [
                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
        ]

        __UpperCamelCase					:Tuple        =		{"pixel_values": videos}
        return BatchFeature(data=__lowercase			,			tensor_type=__lowercase)






def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if not grid or not grid[0]:
        raise TypeError('''The grid does not contain the appropriate information'''     )

    for cell_n in range(1  ,      len(grid[0]     )     ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    __UpperCamelCase					:Union[str, Any]        =		grid[0]

    for row_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        __UpperCamelCase					:Optional[int]        =		grid[row_n]
        __UpperCamelCase					:Dict        =		fill_row(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Any        =		grid[row_n]

    return grid[-1][-1]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    current_row[0] += row_above[0]
    for cell_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        current_row[cell_n] += min(current_row[cell_n - 1]  ,      row_above[cell_n]     )

    return current_row


if __name__ == "__main__":
    import doctest

    doctest.testmod()





