



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

__UpperCAmelCase			  =			logging.get_logger(__name__)
def 						UpperCamelCase      (  snake_case__			:       Optional[int]      )      ->	List[List[ImageInput]]:
   if isinstance(snake_case__			,	(list, tuple)      ) and isinstance(videos[0]			,	(list, tuple)      ) and is_valid_image(videos[0][0]      ):
      return videos

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

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

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



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   Tuple  	=    ["pixel_values"]
       def __init__(     self,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = 1 / 255,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						None:
          super().__init__(**SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      size if size is not None else {'shortest_edge': 256}
          UpperCamelCase       :    Tuple		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      crop_size if crop_size is not None else {'height': 224, 'width': 224}
          UpperCamelCase       :    List[Any]		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     param_name='crop_size'			)

          UpperCamelCase       :    Optional[int]		      =      do_resize
          UpperCamelCase       :    Optional[Any]		      =      size
          UpperCamelCase       :    Any		      =      do_center_crop
          UpperCamelCase       :    Tuple		      =      crop_size
          UpperCamelCase       :    Dict		      =      resample
          UpperCamelCase       :    int		      =      do_rescale
          UpperCamelCase       :    Optional[Any]		      =      rescale_factor
          UpperCamelCase       :    List[str]		      =      offset
          UpperCamelCase       :    int		      =      do_normalize
          UpperCamelCase       :    str		      =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase       :    str		      =      image_std if image_std is not None else IMAGENET_STANDARD_STD
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						np.ndarray:
          UpperCamelCase       :    Union[str, Any]		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)
          if "shortest_edge" in size:
             UpperCamelCase       :    List[str]		      =      get_resize_output_image_size(SCREAMING_SNAKE_CASE_,     size['shortest_edge'],     default_to_square=SCREAMING_SNAKE_CASE_			)
          elif "height" in size and "width" in size:
             UpperCamelCase       :    Dict		      =      (size['height'], size['width'])
          else:
             raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""			)
          return resize(SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						np.ndarray:
          UpperCamelCase       :    List[Any]		      =      get_size_dict(SCREAMING_SNAKE_CASE_			)
          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(SCREAMING_SNAKE_CASE_,     size=(size['height'], size['width']),     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						Optional[int]:
          UpperCamelCase       :    Tuple		      =      image.astype(np.floataa			)
          if offset:
             UpperCamelCase       :    Tuple		      =      image - (scale / 2)
          return rescale(SCREAMING_SNAKE_CASE_,     scale=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						np.ndarray:
          return normalize(SCREAMING_SNAKE_CASE_,     mean=SCREAMING_SNAKE_CASE_,     std=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,     )	->						np.ndarray:
          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       :    Optional[int]		      =      to_numpy_array(SCREAMING_SNAKE_CASE_			)

          if do_resize:
             UpperCamelCase       :    Dict		      =      self.resize(image=SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_			)

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

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

          if do_normalize:
             UpperCamelCase       :    int		      =      self.normalize(image=SCREAMING_SNAKE_CASE_,     mean=SCREAMING_SNAKE_CASE_,     std=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      to_channel_dimension_format(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          return image






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,     **SCREAMING_SNAKE_CASE_,     )	->						PIL.Image.Image:
          UpperCamelCase       :    Union[str, Any]		      =      do_resize if do_resize is not None else self.do_resize
          UpperCamelCase       :    List[str]		      =      resample if resample is not None else self.resample
          UpperCamelCase       :    int		      =      do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase       :    Optional[Any]		      =      do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase       :    List[str]		      =      rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase       :    int		      =      offset if offset is not None else self.offset
          UpperCamelCase       :    Optional[int]		      =      do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase       :    Optional[Any]		      =      image_mean if image_mean is not None else self.image_mean
          UpperCamelCase       :    Dict		      =      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       :    Tuple		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      crop_size if crop_size is not None else self.crop_size
          UpperCamelCase       :    Dict		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     param_name='crop_size'			)

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

          UpperCamelCase       :    Tuple		      =      make_batched(SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase       :    str		      =      {'pixel_values': videos}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_,     tensor_type=SCREAMING_SNAKE_CASE_			)











from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						@staticmethod
						@abstractmethod
						def        UpperCAmelCase__					(__UpperCAmelCase:					ArgumentParser   )    ->     Tuple:




											'''simple docstring'''



											raise NotImplementedError()



						@abstractmethod
						def        UpperCAmelCase__					(self:					List[str]   )    ->     List[str]:




											'''simple docstring'''



											raise NotImplementedError()





