


'''simple docstring'''






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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a_       :     List[str] 							=     logging.get_logger(__name__)




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__ = 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 {'height': 2_24, 'width': 2_24}
      _a         =				get_size_dict(__magic_name__    )
      _a         =				do_resize
      _a         =				do_rescale
      _a         =				do_normalize
      _a         =				size
      _a         =				resample
      _a         =				rescale_factor
      _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__    )
      if "height" not in size or "width" not in size:
           raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'    )
      return resize(
          __magic_name__   ,					size=(size['height'], size['width'])   ,					resample=__magic_name__   ,					data_format=__magic_name__   ,					**__magic_name__    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__ = None   ,					**__magic_name__    )       ->				np.ndarray:
      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__ = ChannelDimension.FIRST   ,					**__magic_name__   ,					)       ->				Tuple:
      _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         =				do_normalize if do_normalize is not None else self.do_normalize
      _a         =				resample if resample is not None else self.resample
      _a         =				rescale_factor if rescale_factor is not None else self.rescale_factor
      _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__    )

      _a         =				make_list_of_images(__magic_name__    )

      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.'    )

      if do_resize and size is None:
           raise ValueError('Size must be specified if do_resize 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.
      _a         =				[to_numpy_array(__magic_name__    ) for image in images]

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

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

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


a_       :     Any 							=     {"configuration_bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig", "BitOnnxConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     int 							=     [
     "BIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BitForImageClassification",
     "BitModel",
     "BitPreTrainedModel",
     "BitBackbone",
 ]


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     Tuple 							=     ["BitImageProcessor"]


if TYPE_CHECKING:
 from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bit import (
      BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BitBackbone,
      BitForImageClassification,
      BitModel,
      BitPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bit import BitImageProcessor

else:
 import sys

 a_       :     Union[str, Any] 							=     _LazyModule(__name__, globals()["__file__"], _import_structure)

