






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

lowerCamelCase       :       Optional[Any]        =				logging.get_logger(__name__)
def 					snake_case_						(   lowerCAmelCase_				:  str					):
							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   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					['''pixel_values''']



					def __init__(     self :		int						,   __a :		bool = True						,   __a :		Dict[str, int] = None						,   __a :		PILImageResampling = PILImageResampling.BILINEAR						,   __a :		bool = True						,   __a :		Dict[str, int] = None						,   __a :		bool = True						,   __a :		Union[int, float] = 1 / 255						,   __a :		bool = True						,   __a :		bool = True						,   __a :		Optional[Union[float, List[float]]] = None						,   __a :		Optional[Union[float, List[float]]] = None						,   **__a :		Optional[int]						,   )	->     None:


												"""simple docstring"""

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

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



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


												"""simple docstring"""

												__lowercase					:							int			=   get_size_dict(__a						,   default_to_square=__a							)
												if "shortest_edge" in size:
																			__lowercase					:							Optional[Any]			=   get_resize_output_image_size(__a						,   size["""shortest_edge"""]						,   default_to_square=__a							)
												elif "height" in size and "width" in size:
																			__lowercase					:							Union[str, 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(__a						,   size=__a						,   resample=__a						,   data_format=__a						,   **__a							)



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


												"""simple docstring"""

												__lowercase					:							Tuple			=   get_size_dict(__a							)
												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(__a						,   size=(size["""height"""], size["""width"""])						,   data_format=__a						,   **__a							)



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		np.ndarray						,   __a :		Union[int, float]						,   __a :		bool = True						,   __a :		Optional[Union[str, ChannelDimension]] = None						,   **__a :		Dict						,   )	->     str:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   image.astype(np.floataa							)
												if offset:
																			__lowercase					:							List[str]			=   image - (scale / 2)
												return rescale(__a						,   scale=__a						,   data_format=__a						,   **__a							)



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		np.ndarray						,   __a :		Union[float, List[float]]						,   __a :		Union[float, List[float]]						,   __a :		Optional[Union[str, ChannelDimension]] = None						,   **__a :		List[Any]						,   )	->     np.ndarray:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		ImageInput						,   __a :		bool = None						,   __a :		Dict[str, int] = None						,   __a :		PILImageResampling = None						,   __a :		bool = None						,   __a :		Dict[str, int] = None						,   __a :		bool = None						,   __a :		float = None						,   __a :		bool = None						,   __a :		bool = None						,   __a :		Optional[Union[float, List[float]]] = None						,   __a :		Optional[Union[float, List[float]]] = None						,   __a :		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."""							)

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

												# All transformations expect numpy arrays.
												__lowercase					:							Union[str, Any]			=   to_numpy_array(__a							)

												if do_resize:
																			__lowercase					:							Optional[Any]			=   self.resize(image=__a						,   size=__a						,   resample=__a							)

												if do_center_crop:
																			__lowercase					:							Optional[int]			=   self.center_crop(__a						,   size=__a							)

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

												if do_normalize:
																			__lowercase					:							Optional[Any]			=   self.normalize(image=__a						,   mean=__a						,   std=__a							)

												__lowercase					:							List[Any]			=   to_channel_dimension_format(__a						,   __a							)
												return image



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


												"""simple docstring"""

												__lowercase					:							int			=   do_resize if do_resize is not None else self.do_resize
												__lowercase					:							str			=   resample if resample is not None else self.resample
												__lowercase					:							Tuple			=   do_center_crop if do_center_crop is not None else self.do_center_crop
												__lowercase					:							str			=   do_rescale if do_rescale is not None else self.do_rescale
												__lowercase					:							str			=   rescale_factor if rescale_factor is not None else self.rescale_factor
												__lowercase					:							List[Any]			=   offset if offset is not None else self.offset
												__lowercase					:							Optional[Any]			=   do_normalize if do_normalize is not None else self.do_normalize
												__lowercase					:							List[str]			=   image_mean if image_mean is not None else self.image_mean
												__lowercase					:							Optional[int]			=   image_std if image_std is not None else self.image_std

												__lowercase					:							List[Any]			=   size if size is not None else self.size
												__lowercase					:							Optional[Any]			=   get_size_dict(__a						,   default_to_square=__a							)
												__lowercase					:							Optional[int]			=   crop_size if crop_size is not None else self.crop_size
												__lowercase					:							int			=   get_size_dict(__a						,   param_name="""crop_size"""							)

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

												__lowercase					:							int			=   make_batched(__a							)

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

												__lowercase					:							str			=   {"""pixel_values""": videos}
												return BatchFeature(data=__a						,   tensor_type=__a							)







from __future__ import annotations
def 					snake_case_						(   lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  int					):
							if partitions <= 0:
														raise ValueError("""partitions must be a positive number!"""					)
							if partitions > number_of_bytes:
														raise ValueError("""partitions can not > number_of_bytes!"""					)
							__lowercase					:							Dict			=   number_of_bytes // partitions
							__lowercase					:							Union[str, Any]			=   []
							for i in range(lowerCAmelCase_					):
														__lowercase					:							str			=   i * bytes_per_partition + 1
														__lowercase					:							List[Any]			=   (
														    number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
														)
														allocation_list.append(F"{start_bytes}-{end_bytes}"					)
							return allocation_list


if __name__ == "__main__":
	import doctest

	doctest.testmod()