





"""simple docstring"""
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

_snake_case     :		Optional[int]       						= logging.get_logger(__name__)






def 					A__				(			UpperCamelCase       ):
							if isinstance(UpperCamelCase					,  (list, tuple)       ) and isinstance(videos[0]					,  (list, tuple)       ) and is_valid_image(videos[0][0]       ):
														return videos

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

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

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






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							['''pixel_values''']


					def __init__(					self			:Optional[Any]     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Dict[str, int] = None     ,       __UpperCamelCase			:PILImageResampling = PILImageResampling.BILINEAR     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Dict[str, int] = None     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Union[int, float] = 1 / 2_55     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       **__UpperCamelCase			:str     ,       ):
												super().__init__(**__UpperCamelCase		)
												A		=  size if size is not None else {"shortest_edge": 2_56}
												A		=  get_size_dict(__UpperCamelCase     ,       default_to_square=__UpperCamelCase		)
												A		=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
												A		=  get_size_dict(__UpperCamelCase     ,       param_name="crop_size"		)

												A		=  do_resize
												A		=  size
												A		=  do_center_crop
												A		=  crop_size
												A		=  resample
												A		=  do_rescale
												A		=  rescale_factor
												A		=  offset
												A		=  do_normalize
												A		=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												A		=  image_std if image_std is not None else IMAGENET_STANDARD_STD


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:PILImageResampling = PILImageResampling.BILINEAR     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:Dict     ,       ):
												A		=  get_size_dict(__UpperCamelCase     ,       default_to_square=__UpperCamelCase		)
												if "shortest_edge" in size:
																			A		=  get_resize_output_image_size(__UpperCamelCase     ,       size["shortest_edge"]     ,       default_to_square=__UpperCamelCase		)
												elif "height" in size and "width" in size:
																			A		=  (size["height"], size["width"])
												else:
																			raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"		)
												return resize(__UpperCamelCase     ,       size=__UpperCamelCase     ,       resample=__UpperCamelCase     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:str     ,       ):
												A		=  get_size_dict(__UpperCamelCase		)
												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(__UpperCamelCase     ,       size=(size["height"], size["width"])     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Union[int, float]     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:List[str]     ,       ):
												A		=  image.astype(np.floataa		)
												if offset:
																			A		=  image - (scale / 2)
												return rescale(__UpperCamelCase     ,       scale=__UpperCamelCase     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Union[float, List[float]]     ,       __UpperCamelCase			:Union[float, List[float]]     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:List[str]     ,       ):
												return normalize(__UpperCamelCase     ,       mean=__UpperCamelCase     ,       std=__UpperCamelCase     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:ImageInput     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:Dict[str, int] = None     ,       __UpperCamelCase			:PILImageResampling = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:Dict[str, int] = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:float = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       __UpperCamelCase			:Optional[ChannelDimension] = 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.
												A		=  to_numpy_array(__UpperCamelCase		)

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

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

												if do_rescale:
																			A		=  self.rescale(image=__UpperCamelCase     ,       scale=__UpperCamelCase     ,       offset=__UpperCamelCase		)

												if do_normalize:
																			A		=  self.normalize(image=__UpperCamelCase     ,       mean=__UpperCamelCase     ,       std=__UpperCamelCase		)

												A		=  to_channel_dimension_format(__UpperCamelCase     ,       __UpperCamelCase		)
												return image


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:ImageInput     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:Dict[str, int] = None     ,       __UpperCamelCase			:PILImageResampling = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:Dict[str, int] = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:float = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       __UpperCamelCase			:Optional[Union[str, TensorType]] = None     ,       __UpperCamelCase			:ChannelDimension = ChannelDimension.FIRST     ,       **__UpperCamelCase			:Tuple     ,       ):
												A		=  do_resize if do_resize is not None else self.do_resize
												A		=  resample if resample is not None else self.resample
												A		=  do_center_crop if do_center_crop is not None else self.do_center_crop
												A		=  do_rescale if do_rescale is not None else self.do_rescale
												A		=  rescale_factor if rescale_factor is not None else self.rescale_factor
												A		=  offset if offset is not None else self.offset
												A		=  do_normalize if do_normalize is not None else self.do_normalize
												A		=  image_mean if image_mean is not None else self.image_mean
												A		=  image_std if image_std is not None else self.image_std

												A		=  size if size is not None else self.size
												A		=  get_size_dict(__UpperCamelCase     ,       default_to_square=__UpperCamelCase		)
												A		=  crop_size if crop_size is not None else self.crop_size
												A		=  get_size_dict(__UpperCamelCase     ,       param_name="crop_size"		)

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

												A		=  make_batched(__UpperCamelCase		)

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

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









"""simple docstring"""
_snake_case     :		Optional[int]       						= [
    'DownloadConfig',
    'DownloadManager',
    'DownloadMode',
    'StreamingDownloadManager',
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager


