





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				:	Union[str, Any]      						=  logging.get_logger(__name__)



def A_     (		A__    )  ->		List[List[ImageInput]]:
       if isinstance(A__		,				(list, tuple)    ) and isinstance(videos[0]		,				(list, tuple)    ) and is_valid_image(videos[0][0]    ):
              return videos

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

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

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





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Optional[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 ,  )      ->						None:







          '''simple docstring'''

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

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

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase = PILImageResampling.BILINEAR ,  lowercase = None ,  **lowercase ,  )      ->						np.ndarray:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    get_size_dict(lowercase ,  default_to_square=lowercase)
          if "shortest_edge" in size:
                 a__						:     Any			=    get_resize_output_image_size(lowercase ,  size['shortest_edge'] ,  default_to_square=lowercase)
          elif "height" in size and "width" in size:
                 a__						:     List[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(lowercase ,  size=lowercase ,  resample=lowercase ,  data_format=lowercase ,  **lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase = None ,  **lowercase ,  )      ->						np.ndarray:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    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 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase = True ,  lowercase = None ,  **lowercase ,  )      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Tuple			=    image.astype(np.floataa)
          if offset:
                 a__						:     Union[str, Any]			=    image - (scale / 2)
          return rescale(lowercase ,  scale=lowercase ,  data_format=lowercase ,  **lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase = None ,  **lowercase ,  )      ->						np.ndarray:







          '''simple docstring'''

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

   def 							__lowercase		( 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 ,  )      ->						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.
          a__						:     int			=    to_numpy_array(lowercase)

          if do_resize:
                 a__						:     Any			=    self.resize(image=lowercase ,  size=lowercase ,  resample=lowercase)

          if do_center_crop:
                 a__						:     Any			=    self.center_crop(lowercase ,  size=lowercase)

          if do_rescale:
                 a__						:     Union[str, Any]			=    self.rescale(image=lowercase ,  scale=lowercase ,  offset=lowercase)

          if do_normalize:
                 a__						:     Tuple			=    self.normalize(image=lowercase ,  mean=lowercase ,  std=lowercase)

          a__						:     List[Any]			=    to_channel_dimension_format(lowercase ,  lowercase)
          return image

   def 							__lowercase		( 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 ,  )      ->						PIL.Image.Image:







          '''simple docstring'''

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

          a__						:     Optional[int]			=    size if size is not None else self.size
          a__						:     Any			=    get_size_dict(lowercase ,  default_to_square=lowercase)
          a__						:     Optional[int]			=    crop_size if crop_size is not None else self.crop_size
          a__						:     Optional[int]			=    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.')

          a__						:     List[Any]			=    make_batched(lowercase)

          a__						:     int			=    [
              [
                  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
          ]

          a__						:     int			=    {'pixel_values': videos}
          return BatchFeature(data=lowercase ,  tensor_type=lowercase)









import enum
import shutil
import sys


lowercase						, lowercase				:	List[Any]      						=  shutil.get_terminal_size()

lowercase				:	Union[str, Any]      						=  {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}





class 			A__   (							enum.Enum     ):




   """simple docstring"""



   __A       :    List[str]	  =  0
   __A       :    str	  =  1



def A_     (		A__		,				A__=""    )  ->		int:
       sys.stdout.write(str(A__    ) + end    )
       sys.stdout.flush()



def A_     (		A__		,				A__		,				A__=""    )  ->		int:
       forceWrite(F'\u001b[{color}m{content}\u001b[0m'		,				A__    )



def A_     (		)  ->		Any:
       forceWrite('\r'    )



def A_     (		A__		,				A__    )  ->		List[str]:
       forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'    )



def A_     (		)  ->		Any:
       forceWrite(' ' * TERMINAL_WIDTH    )
       reset_cursor()



def A_     (		)  ->		Any:
       reset_cursor()
       forceWrite('-' * TERMINAL_WIDTH    )


