

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase__ :     List[Any]				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : int			       =["""pixel_values"""]


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







        super().__init__(**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
        SCREAMING_SNAKE_CASE :      List[Any]       =   get_size_dict(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
        SCREAMING_SNAKE_CASE :      List[str]       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__				,				param_name="""crop_size"""		)

        SCREAMING_SNAKE_CASE :      Dict       =   do_resize
        SCREAMING_SNAKE_CASE :      Optional[int]       =   do_rescale
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   do_normalize
        SCREAMING_SNAKE_CASE :      Dict       =   do_center_crop
        SCREAMING_SNAKE_CASE :      Tuple       =   crop_size
        SCREAMING_SNAKE_CASE :      str       =   size
        SCREAMING_SNAKE_CASE :      Dict       =   resample
        SCREAMING_SNAKE_CASE :      List[str]       =   rescale_factor
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        SCREAMING_SNAKE_CASE :      List[Any]       =   image_std if image_std is not None else IMAGENET_DEFAULT_STD


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







        SCREAMING_SNAKE_CASE :      Dict       =   get_size_dict(UpperCAmelCase__		)

        if "shortest_edge" in size:
              SCREAMING_SNAKE_CASE :      str       =   get_resize_output_image_size(UpperCAmelCase__				,				size=size["""shortest_edge"""]				,				default_to_square=UpperCAmelCase__		)
              # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
              SCREAMING_SNAKE_CASE :      int       =   (size["""height"""], size["""width"""])
        else:
              raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"		)
        return resize(UpperCAmelCase__				,				size=UpperCAmelCase__				,				resample=UpperCAmelCase__				,				data_format=UpperCAmelCase__				,				**UpperCAmelCase__		)


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







        SCREAMING_SNAKE_CASE :      Optional[int]       =   get_size_dict(UpperCAmelCase__		)
        if "height" not in size or "width" not in size:
              raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"		)
        return center_crop(UpperCAmelCase__				,				size=(size["""height"""], size["""width"""])				,				data_format=UpperCAmelCase__				,				**UpperCAmelCase__		)


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







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


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







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




  def  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : ImageInput				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Dict[str, int] = None				,				UpperCAmelCase__    : PILImageResampling = None				,				UpperCAmelCase__    : bool = None				,				UpperCAmelCase__    : int = None				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[float] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : Union[str, ChannelDimension] = ChannelDimension.FIRST				,				**UpperCAmelCase__    : Any				,				)					->BatchFeature:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      Optional[int]       =   size if size is not None else self.size
        SCREAMING_SNAKE_CASE :      Any       =   get_size_dict(UpperCAmelCase__		)

        if not is_batched(UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      List[Any]       =   [images]

        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."""		)

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

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

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

        if do_rescale:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [self.rescale(image=UpperCAmelCase__				,				scale=UpperCAmelCase__		) for image in images]

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

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

        SCREAMING_SNAKE_CASE :      Tuple       =   {"""pixel_values""": images}
        return BatchFeature(data=UpperCAmelCase__				,				tensor_type=UpperCAmelCase__		)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     List[Any]				    =    {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Union[str, Any]				    =    ["""ViTFeatureExtractor"""]
  UpperCAmelCase__ :     List[Any]				    =    ["""ViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[Any]				    =    [
      """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTForImageClassification""",
      """ViTForMaskedImageModeling""",
      """ViTModel""",
      """ViTPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Tuple				    =    [
      """TFViTForImageClassification""",
      """TFViTModel""",
      """TFViTPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     str				    =    [
      """FlaxViTForImageClassification""",
      """FlaxViTModel""",
      """FlaxViTPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vit import ViTFeatureExtractor
    from .image_processing_vit import ViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit import (
        VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTForImageClassification,
        ViTForMaskedImageModeling,
        ViTModel,
        ViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
  import sys

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




