






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


A_						:						str						      =					logging.get_logger(__name__)

def     snake_case   (UpperCAmelCase__    )			->							List[List[ImageInput]]:
  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 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						int										=['''pixel_values''']





       def __init__(   self    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = PILImageResampling.BILINEAR    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = True    ,     _lowerCamelCase = 1 / 2_5_5    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         super().__init__(**_lowerCamelCase				)
         UpperCamelCase_:   Dict        =							size if size is not None else {'shortest_edge': 2_2_4}
         UpperCamelCase_:   List[str]        =							get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_:   Optional[int]        =							get_size_dict(_lowerCamelCase    ,     param_name='crop_size'				)

         UpperCamelCase_:   List[Any]        =							do_resize
         UpperCamelCase_:   Dict        =							size
         UpperCamelCase_:   List[Any]        =							do_center_crop
         UpperCamelCase_:   Tuple        =							crop_size
         UpperCamelCase_:   Dict        =							resample
         UpperCamelCase_:   List[Any]        =							do_rescale
         UpperCamelCase_:   Any        =							rescale_factor
         UpperCamelCase_:   Dict        =							do_normalize
         UpperCamelCase_:   List[str]        =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCamelCase_:   List[str]        =							image_std if image_std is not None else IMAGENET_STANDARD_STD





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = PILImageResampling.BILINEAR    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         UpperCamelCase_:   str        =							get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase				)
         if "shortest_edge" in size:
           UpperCamelCase_:   Dict        =							get_resize_output_image_size(_lowerCamelCase    ,     size['shortest_edge']    ,     default_to_square=_lowerCamelCase				)
         elif "height" in size and "width" in size:
           UpperCamelCase_:   Tuple        =							(size['height'], size['width'])
         else:
           raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''				)
         return resize(_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         UpperCamelCase_:   str        =							get_size_dict(_lowerCamelCase				)
         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(_lowerCamelCase    ,     size=(size['height'], size['width'])    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         return rescale(_lowerCamelCase    ,     scale=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
         return normalize(_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = 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.'				)

         # All transformations expect numpy arrays.
         UpperCamelCase_:   List[Any]        =							to_numpy_array(_lowerCamelCase				)

         if do_resize:
           UpperCamelCase_:   Dict        =							self.resize(image=_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase				)

         if do_center_crop:
           UpperCamelCase_:   Optional[int]        =							self.center_crop(_lowerCamelCase    ,     size=_lowerCamelCase				)

         if do_rescale:
           UpperCamelCase_:   Any        =							self.rescale(image=_lowerCamelCase    ,     scale=_lowerCamelCase				)

         if do_normalize:
           UpperCamelCase_:   Optional[int]        =							self.normalize(image=_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase				)

         UpperCamelCase_:   int        =							to_channel_dimension_format(_lowerCamelCase    ,     _lowerCamelCase				)
         return image





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = ChannelDimension.FIRST    ,     **_lowerCamelCase    ,     ):
         UpperCamelCase_:   List[Any]        =							do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_:   int        =							resample if resample is not None else self.resample
         UpperCamelCase_:   Union[str, Any]        =							do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_:   List[str]        =							do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_:   str        =							rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_:   Dict        =							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_:   int        =							image_std if image_std is not None else self.image_std

         UpperCamelCase_:   str        =							size if size is not None else self.size
         UpperCamelCase_:   List[str]        =							get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_:   Optional[int]        =							get_size_dict(_lowerCamelCase    ,     param_name='crop_size'				)

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

         UpperCamelCase_:   Optional[int]        =							make_batched(_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							[
             [
                 self._preprocess_image(
                     image=_lowerCamelCase    ,     do_resize=_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase    ,     do_center_crop=_lowerCamelCase    ,     crop_size=_lowerCamelCase    ,     do_rescale=_lowerCamelCase    ,     rescale_factor=_lowerCamelCase    ,     do_normalize=_lowerCamelCase    ,     image_mean=_lowerCamelCase    ,     image_std=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     )
                 for img in video
             ]
             for video in videos
         ]

         UpperCamelCase_:   Union[str, Any]        =							{'pixel_values': videos}
         return BatchFeature(data=_lowerCamelCase    ,     tensor_type=_lowerCamelCase				)






import enum
import shutil
import sys


__UpperCamelCase,	__UpperCamelCase:	Optional[int] = shutil.get_terminal_size()

__UpperCamelCase:	Optional[Any] = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}


class       __lowerCAmelCase					(       enum.Enum      ):


	'''simple docstring'''





	_A													=				0
	_A													=				1


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]   ,    _lowercase     : Union[str, Any]=""			)   ->							int:




		'''simple docstring'''
		sys.stdout.write(str(_lowercase			) + end			)
		sys.stdout.flush()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]=""			)   ->							str:




		'''simple docstring'''
		forceWrite(f"""\u001b[{color}m{content}\u001b[0m"""   ,    _lowercase			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		forceWrite('\r'			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : str			)   ->							List[Any]:




		'''simple docstring'''
		forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							Dict:




		'''simple docstring'''
		forceWrite(' ' * TERMINAL_WIDTH			)
		reset_cursor()





def       SCREAMING_SNAKE_CASE__  (       )   ->							List[str]:




		'''simple docstring'''
		reset_cursor()
		forceWrite('-' * TERMINAL_WIDTH			)


