

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
  import PIL







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			str      							=["""pixel_values"""]


  def __init__(      self     :			int					,					UpperCAmelCase__     :			bool = True					,					UpperCAmelCase__     :			Dict[str, int] = None					,					UpperCAmelCase__     :			PILImageResampling = PILImageResampling.BICUBIC					,					UpperCAmelCase__     :			bool = True					,					UpperCAmelCase__     :			Dict[str, int] = None					,					UpperCAmelCase__     :			bool = True					,					UpperCAmelCase__     :			Union[int, float] = 1 / 2_5_5					,					UpperCAmelCase__     :			bool = True					,					UpperCAmelCase__     :			Optional[Union[float, List[float]]] = None					,					UpperCAmelCase__     :			Optional[Union[float, List[float]]] = None					,					UpperCAmelCase__     :			bool = True					,					**UpperCAmelCase__     :			List[str]					,					)       ->None:
        """simple docstring"""







        super().__init__(**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Dict	  = size if size is not None else {"""shortest_edge""": 2_2_4}
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = get_size_dict(UpperCAmelCase__					,					default_to_square=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = get_size_dict(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   str	  = do_resize
        SCREAMING_SNAKE_CASE			:   Tuple	  = size
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = resample
        SCREAMING_SNAKE_CASE			:   Any	  = do_center_crop
        SCREAMING_SNAKE_CASE			:   Tuple	  = crop_size
        SCREAMING_SNAKE_CASE			:   List[Any]	  = do_rescale
        SCREAMING_SNAKE_CASE			:   List[Any]	  = rescale_factor
        SCREAMING_SNAKE_CASE			:   Any	  = do_normalize
        SCREAMING_SNAKE_CASE			:   List[str]	  = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        SCREAMING_SNAKE_CASE			:   Any	  = image_std if image_std is not None else OPENAI_CLIP_STD
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = do_convert_rgb


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			np.ndarray					,					UpperCAmelCase__     :			Dict[str, int]					,					UpperCAmelCase__     :			PILImageResampling = PILImageResampling.BICUBIC					,					UpperCAmelCase__     :			Optional[Union[str, ChannelDimension]] = None					,					**UpperCAmelCase__     :			Optional[int]					,					)       ->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = get_size_dict(UpperCAmelCase__					,					default_to_square=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Dict	  = get_resize_output_image_size(
            UpperCAmelCase__					,					size=(size["""height"""], size["""width"""])					,					default_to_square=UpperCAmelCase__		)
        return resize(UpperCAmelCase__					,					size=UpperCAmelCase__					,					resample=UpperCAmelCase__					,					data_format=UpperCAmelCase__					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			np.ndarray					,					UpperCAmelCase__     :			Dict[str, int]					,					UpperCAmelCase__     :			Optional[Union[str, ChannelDimension]] = None					,					**UpperCAmelCase__     :			Tuple					,					)       ->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = get_size_dict(UpperCAmelCase__		)
        return center_crop(UpperCAmelCase__					,					size=(size["""height"""], size["""width"""])					,					data_format=UpperCAmelCase__					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[str]					,					UpperCAmelCase__     :			np.ndarray					,					UpperCAmelCase__     :			Union[int, float]					,					UpperCAmelCase__     :			Optional[Union[str, ChannelDimension]] = None					,					**UpperCAmelCase__     :			Any					,					)       ->Dict:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			np.ndarray					,					UpperCAmelCase__     :			Union[float, List[float]]					,					UpperCAmelCase__     :			Union[float, List[float]]					,					UpperCAmelCase__     :			Optional[Union[str, ChannelDimension]] = None					,					**UpperCAmelCase__     :			List[Any]					,					)       ->np.ndarray:
        """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__     :			int = None					,					UpperCAmelCase__     :			bool = None					,					UpperCAmelCase__     :			float = None					,					UpperCAmelCase__     :			bool = None					,					UpperCAmelCase__     :			Optional[Union[float, List[float]]] = None					,					UpperCAmelCase__     :			Optional[Union[float, List[float]]] = None					,					UpperCAmelCase__     :			bool = None					,					UpperCAmelCase__     :			Optional[Union[str, TensorType]] = None					,					UpperCAmelCase__     :			Optional[ChannelDimension] = ChannelDimension.FIRST					,					**UpperCAmelCase__     :			Union[str, Any]					,					)       ->PIL.Image.Image:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE			:   List[Any]	  = size if size is not None else self.size
        SCREAMING_SNAKE_CASE			:   int	  = get_size_dict(UpperCAmelCase__					,					default_to_square=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   int	  = resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE			:   Dict	  = do_center_crop if do_center_crop is not None else self.do_center_crop
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = crop_size if crop_size is not None else self.crop_size
        SCREAMING_SNAKE_CASE			:   List[str]	  = get_size_dict(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Dict	  = do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE			:   int	  = rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE			:   List[str]	  = do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE			:   Any	  = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        SCREAMING_SNAKE_CASE			:   List[str]	  = make_list_of_images(UpperCAmelCase__		)

        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."""		)

        if do_resize and size is None:
              raise ValueError("""Size 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."""		)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = [convert_to_rgb(UpperCAmelCase__		) for image in images]

        # All transformations expect numpy arrays.
        SCREAMING_SNAKE_CASE			:   int	  = [to_numpy_array(UpperCAmelCase__		) for image in images]

        if do_resize:
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = [self.resize(image=UpperCAmelCase__					,					size=UpperCAmelCase__					,					resample=UpperCAmelCase__		) for image in images]

        if do_center_crop:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = [self.center_crop(image=UpperCAmelCase__					,					size=UpperCAmelCase__		) for image in images]

        if do_rescale:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = [self.rescale(image=UpperCAmelCase__					,					scale=UpperCAmelCase__		) for image in images]

        if do_normalize:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = [self.normalize(image=UpperCAmelCase__					,					mean=UpperCAmelCase__					,					std=UpperCAmelCase__		) for image in images]

        SCREAMING_SNAKE_CASE			:   List[Any]	  = [to_channel_dimension_format(UpperCAmelCase__					,					UpperCAmelCase__		) for image in images]

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = {"""pixel_values""": images}
        return BatchFeature(data=UpperCAmelCase__					,					tensor_type=UpperCAmelCase__		)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__			:  Dict					     =     {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Union[str, Any]					     =     [
      """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """HubertForCTC""",
      """HubertForSequenceClassification""",
      """HubertModel""",
      """HubertPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  List[Any]					     =     [
      """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFHubertForCTC""",
      """TFHubertModel""",
      """TFHubertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_hubert import (
        HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        HubertForCTC,
        HubertForSequenceClassification,
        HubertModel,
        HubertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_hubert import (
        TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFHubertForCTC,
        TFHubertModel,
        TFHubertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase__			:  str					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




