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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


lowerCAmelCase__     						=			logging.get_logger(__name__)






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
							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     a_			(						SCREAMING_SNAKE_CASE						):






	'''simple docstring'''






	UpperCAmelCase_     			= ['pixel_values']




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







								'''simple docstring'''



								super().__init__(**lowercase__)
								lowerCAmelCase__					     = size if size is not None else {'shortest_edge': 224}
								lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
								lowerCAmelCase__					     = crop_size if crop_size is not None else {'height': 224, 'width': 224}
								lowerCAmelCase__					     = get_size_dict(lowercase__      ,   param_name='crop_size')

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




	def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  PILImageResampling = PILImageResampling.BILINEAR      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







								'''simple docstring'''



								lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
								if "shortest_edge" in size:
															lowerCAmelCase__					     = get_resize_output_image_size(lowercase__      ,   size['shortest_edge']      ,   default_to_square=lowercase__)
								elif "height" in size and "width" in size:
															lowerCAmelCase__					     = (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 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







								'''simple docstring'''



								lowerCAmelCase__					     = 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 		__snake_case						(      self :  int      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[int, float]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







								'''simple docstring'''



								return rescale(lowercase__      ,   scale=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




	def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  List[str]      ,   ):







								'''simple docstring'''



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




	def 		__snake_case						(      self :  str      ,   lowercase__ :  ImageInput      ,   lowercase__ :  bool = None      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  float = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[ChannelDimension] = ChannelDimension.FIRST      ,   ):







								'''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.')

								# All transformations expect numpy arrays.
								lowerCAmelCase__					     = to_numpy_array(lowercase__)

								if do_resize:
															lowerCAmelCase__					     = self.resize(image=lowercase__      ,   size=lowercase__      ,   resample=lowercase__)

								if do_center_crop:
															lowerCAmelCase__					     = self.center_crop(lowercase__      ,   size=lowercase__)

								if do_rescale:
															lowerCAmelCase__					     = self.rescale(image=lowercase__      ,   scale=lowercase__)

								if do_normalize:
															lowerCAmelCase__					     = self.normalize(image=lowercase__      ,   mean=lowercase__      ,   std=lowercase__)

								lowerCAmelCase__					     = to_channel_dimension_format(lowercase__      ,   lowercase__)
								return image




	def 		__snake_case						(      self :  str      ,   lowercase__ :  ImageInput      ,   lowercase__ :  bool = None      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  float = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  ChannelDimension = ChannelDimension.FIRST      ,   **lowercase__ :  int      ,   ):







								'''simple docstring'''



								lowerCAmelCase__					     = do_resize if do_resize is not None else self.do_resize
								lowerCAmelCase__					     = resample if resample is not None else self.resample
								lowerCAmelCase__					     = do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCAmelCase__					     = do_rescale if do_rescale is not None else self.do_rescale
								lowerCAmelCase__					     = rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCAmelCase__					     = do_normalize if do_normalize is not None else self.do_normalize
								lowerCAmelCase__					     = image_mean if image_mean is not None else self.image_mean
								lowerCAmelCase__					     = image_std if image_std is not None else self.image_std

								lowerCAmelCase__					     = size if size is not None else self.size
								lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
								lowerCAmelCase__					     = crop_size if crop_size is not None else self.crop_size
								lowerCAmelCase__					     = 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.')

								lowerCAmelCase__					     = make_batched(lowercase__)

								lowerCAmelCase__					     = [
								    [
								        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__      ,   do_normalize=lowercase__      ,   image_mean=lowercase__      ,   image_std=lowercase__      ,   data_format=lowercase__      ,   )
								        for img in video
								    ]
								    for video in videos
								]

								lowerCAmelCase__					     = {'pixel_values': videos}
								return BatchFeature(data=lowercase__      ,   tensor_type=lowercase__)

import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= 1
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        return self.__class__(**{k: copy.deepcopy(lowercase__) for k, v in self.__dict__.items()})
