


'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a_					:    str          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""pixel_values"""]
 def __init__( self      ,				__magic_name__ = True      ,				__magic_name__ = 32      ,				__magic_name__=PILImageResampling.BILINEAR      ,				__magic_name__ = True      ,				**__magic_name__      ,				)					->							None:
      _a			      =							do_resize
      _a			      =							do_rescale
      _a			      =							size_divisor
      _a			      =							resample
      super().__init__(**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__						)					->							np.ndarray:
      _a      ,   _a			      =							get_image_size(__magic_name__						)
      # Rounds the height and width down to the closest multiple of size_divisor
      _a			      =							height // size_divisor * size_divisor
      _a			      =							width // size_divisor * size_divisor
      _a			      =							resize(__magic_name__      ,				(new_h, new_w)      ,				resample=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
      return image
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__						)					->							np.ndarray:
      return rescale(image=__magic_name__      ,				scale=__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__ = ChannelDimension.FIRST      ,				**__magic_name__      ,				)					->							BatchFeature:
      _a			      =							do_resize if do_resize is not None else self.do_resize
      _a			      =							do_rescale if do_rescale is not None else self.do_rescale
      _a			      =							size_divisor if size_divisor is not None else self.size_divisor
      _a			      =							resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
           raise ValueError('size_divisor is required for resizing'						)

      _a			      =							make_list_of_images(__magic_name__						)

      if not valid_images(__magic_name__						):
           raise ValueError('Invalid image(s)'						)

      # All transformations expect numpy arrays.
      _a			      =							[to_numpy_array(__magic_name__						) for img in images]

      if do_resize:
           _a			      =							[self.resize(__magic_name__      ,				size_divisor=__magic_name__      ,				resample=__magic_name__						) for image in images]

      if do_rescale:
           _a			      =							[self.rescale(__magic_name__      ,				scale=1 / 2_55						) for image in images]

      _a			      =							[to_channel_dimension_format(__magic_name__      ,				__magic_name__						) for image in images]

      _a			      =							{'pixel_values': images}
      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__)

