
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

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

def a__    (       A_					):






  '''simple docstring'''





  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        UpperCAmelCase_			( lowercase_     ):


  '''simple docstring'''







  a__           =		["""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 / 255			,							UpperCamelCase__			: bool = True			,							UpperCamelCase__			: bool = True			,							UpperCamelCase__			: Optional[Union[float, List[float]]] = None			,							UpperCamelCase__			: Optional[Union[float, List[float]]] = None			,							**UpperCamelCase__			: str			,							)			->	Tuple:







    """simple docstring"""






    super().__init__(**__UpperCamelCase    )
    __magic_name__				     =       size if size is not None else {"""shortest_edge""": 256}
    __magic_name__				     =       get_size_dict(__UpperCamelCase			,							default_to_square=__UpperCamelCase    )
    __magic_name__				     =       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    __magic_name__				     =       get_size_dict(__UpperCamelCase			,							param_name="""crop_size"""    )

    __magic_name__				     =       do_resize
    __magic_name__				     =       size
    __magic_name__				     =       do_center_crop
    __magic_name__				     =       crop_size
    __magic_name__				     =       resample
    __magic_name__				     =       do_rescale
    __magic_name__				     =       rescale_factor
    __magic_name__				     =       offset
    __magic_name__				     =       do_normalize
    __magic_name__				     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __magic_name__				     =       image_std if image_std is not None else IMAGENET_STANDARD_STD
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: np.ndarray			,							UpperCamelCase__			: Dict[str, int]			,							UpperCamelCase__			: PILImageResampling = PILImageResampling.BILINEAR			,							UpperCamelCase__			: Optional[Union[str, ChannelDimension]] = None			,							**UpperCamelCase__			: Dict			,							)			->	int:







    """simple docstring"""






    __magic_name__				     =       get_size_dict(__UpperCamelCase			,							default_to_square=__UpperCamelCase    )
    if "shortest_edge" in size:
      __magic_name__				     =       get_resize_output_image_size(__UpperCamelCase			,							size["""shortest_edge"""]			,							default_to_square=__UpperCamelCase    )
    elif "height" in size and "width" in size:
      __magic_name__				     =       (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 							_lowercase							( self			: str			,							UpperCamelCase__			: np.ndarray			,							UpperCamelCase__			: Dict[str, int]			,							UpperCamelCase__			: Optional[Union[str, ChannelDimension]] = None			,							**UpperCamelCase__			: str			,							)			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       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 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: np.ndarray			,							UpperCamelCase__			: Union[int, float]			,							UpperCamelCase__			: bool = True			,							UpperCamelCase__			: Optional[Union[str, ChannelDimension]] = None			,							**UpperCamelCase__			: List[str]			,							)			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       image.astype(np.floataa    )
    if offset:
      __magic_name__				     =       image - (scale / 2)
    return rescale(__UpperCamelCase			,							scale=__UpperCamelCase			,							data_format=__UpperCamelCase			,							**__UpperCamelCase    )
  def 							_lowercase							( 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]			,							)			->	Optional[Any]:







    """simple docstring"""






    return normalize(__UpperCamelCase			,							mean=__UpperCamelCase			,							std=__UpperCamelCase			,							data_format=__UpperCamelCase			,							**__UpperCamelCase    )
  def 							_lowercase							( 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			,							)			->	int:







    """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.
    __magic_name__				     =       to_numpy_array(__UpperCamelCase    )

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

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

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

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

    __magic_name__				     =       to_channel_dimension_format(__UpperCamelCase			,							__UpperCamelCase    )
    return image





  def 							_lowercase							( 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			,							)			->	int:







    """simple docstring"""






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

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

    __magic_name__				     =       make_batched(__UpperCamelCase    )

    __magic_name__				     =       [
        [
            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
    ]

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








"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case     :		Union[str, Any]       						= {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case     :		int       						= [
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		_snake_case     :		str       						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


