


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_					:    Any          =    logging.get_logger(__name__)






def 	_A				(lowerCAmelCase__  :Tuple		)   ->  List[List[ImageInput]]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__				,						(list, tuple)		) and isinstance(videos[0]				,						(list, tuple)		) and is_valid_image(videos[0][0]		):
          return videos

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

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

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






class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""pixel_values"""]
 def __init__( self      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = PILImageResampling.BILINEAR      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = True      ,				__magic_name__ = 1 / 2_55      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							None:
      super().__init__(**__magic_name__						)
      _a			      =							size if size is not None else {'shortest_edge': 2_24}
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      _a			      =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      _a			      =							get_size_dict(__magic_name__      ,				param_name='crop_size'						)

      _a			      =							do_resize
      _a			      =							size
      _a			      =							do_center_crop
      _a			      =							crop_size
      _a			      =							resample
      _a			      =							do_rescale
      _a			      =							rescale_factor
      _a			      =							do_normalize
      _a			      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _a			      =							image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = PILImageResampling.BILINEAR      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      if "shortest_edge" in size:
           _a			      =							get_resize_output_image_size(__magic_name__      ,				size['shortest_edge']      ,				default_to_square=__magic_name__						)
      elif "height" in size and "width" in size:
           _a			      =							(size['height'], size['width'])
      else:
           raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
      return resize(__magic_name__      ,				size=__magic_name__      ,				resample=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      _a			      =							get_size_dict(__magic_name__						)
      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(__magic_name__      ,				size=(size['height'], size['width'])      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							Tuple:
      return rescale(__magic_name__      ,				scale=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      return normalize(__magic_name__      ,				mean=__magic_name__      ,				std=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = ChannelDimension.FIRST      ,				)					->							np.ndarray:
      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.
      _a			      =							to_numpy_array(__magic_name__						)

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

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

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

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

      _a			      =							to_channel_dimension_format(__magic_name__      ,				__magic_name__						)
      return image




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = ChannelDimension.FIRST      ,				**__magic_name__      ,				)					->							PIL.Image.Image:
      _a			      =							do_resize if do_resize is not None else self.do_resize
      _a			      =							resample if resample is not None else self.resample
      _a			      =							do_center_crop if do_center_crop is not None else self.do_center_crop
      _a			      =							do_rescale if do_rescale is not None else self.do_rescale
      _a			      =							rescale_factor if rescale_factor is not None else self.rescale_factor
      _a			      =							do_normalize if do_normalize is not None else self.do_normalize
      _a			      =							image_mean if image_mean is not None else self.image_mean
      _a			      =							image_std if image_std is not None else self.image_std

      _a			      =							size if size is not None else self.size
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      _a			      =							crop_size if crop_size is not None else self.crop_size
      _a			      =							get_size_dict(__magic_name__      ,				param_name='crop_size'						)

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

      _a			      =							make_batched(__magic_name__						)

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

      _a			      =							{'pixel_values': videos}
      return BatchFeature(data=__magic_name__      ,				tensor_type=__magic_name__						)





'''simple docstring'''






from typing import TYPE_CHECKING

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


a_					:    int          =    {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[Any]          =    ["ViTFeatureExtractor"]
 a_					:    Optional[int]          =    ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Optional[int]          =    [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

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

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Dict          =    [
     "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

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

