





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				:							Any					=			logging.get_logger(__name__)

class      lowerCamelCase	(  _SCREAMING_SNAKE_CASE     ):
       _UpperCamelCase    		=      ['pixel_values']

       def __init__(			self	,						a__ = True	,						a__ = None	,						a__ = PILImageResampling.BILINEAR	,						a__ = True	,						a__ = 1 / 2_55	,						a__ = True	,						a__ = None	,						a__ = None	,						**a__	,						):






        '''simple docstring'''






        super().__init__(**UpperCamelCase__					)
        snake_case   : Optional[int]	  =      size if size is not None else {'''height''': 2_24, '''width''': 2_24}
        snake_case   : Optional[int]	  =      get_size_dict(UpperCamelCase__					)
        snake_case   : int	  =      do_resize
        snake_case   : Any	  =      do_rescale
        snake_case   : Dict	  =      do_normalize
        snake_case   : Optional[Any]	  =      size
        snake_case   : str	  =      resample
        snake_case   : Dict	  =      rescale_factor
        snake_case   : Optional[Any]	  =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        snake_case   : Union[str, Any]	  =      image_std if image_std is not None else IMAGENET_STANDARD_STD

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__ = PILImageResampling.BILINEAR	,						a__ = None	,						**a__	,						):






        '''simple docstring'''






        snake_case   : List[Any]	  =      get_size_dict(UpperCamelCase__					)
        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(
            UpperCamelCase__	,						size=(size['''height'''], size['''width'''])	,						resample=UpperCamelCase__	,						data_format=UpperCamelCase__	,						**UpperCamelCase__					)

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__ = None	,						**a__					):






        '''simple docstring'''






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

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__	,						a__ = None	,						**a__	,						):






        '''simple docstring'''






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



       def 				UpperCAmelCase				(			self	,						a__	,						a__ = None	,						a__ = None	,						a__ = None	,						a__ = None	,						a__ = None	,						a__ = None	,						a__ = None	,						a__ = None	,						a__ = None	,						a__ = ChannelDimension.FIRST	,						**a__	,						):






        '''simple docstring'''






        snake_case   : List[Any]	  =      do_resize if do_resize is not None else self.do_resize
        snake_case   : Union[str, Any]	  =      do_rescale if do_rescale is not None else self.do_rescale
        snake_case   : Any	  =      do_normalize if do_normalize is not None else self.do_normalize
        snake_case   : Optional[Any]	  =      resample if resample is not None else self.resample
        snake_case   : List[Any]	  =      rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case   : Optional[int]	  =      image_mean if image_mean is not None else self.image_mean
        snake_case   : List[Any]	  =      image_std if image_std is not None else self.image_std

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

        snake_case   : Any	  =      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_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

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

        if do_resize:
         snake_case   : int	  =      [self.resize(image=UpperCamelCase__	,						size=UpperCamelCase__	,						resample=UpperCamelCase__					) for image in images]

        if do_rescale:
         snake_case   : Any	  =      [self.rescale(image=UpperCamelCase__	,						scale=UpperCamelCase__					) for image in images]

        if do_normalize:
         snake_case   : Dict	  =      [self.normalize(image=UpperCamelCase__	,						mean=UpperCamelCase__	,						std=UpperCamelCase__					) for image in images]

        snake_case   : Optional[int]	  =      [to_channel_dimension_format(UpperCamelCase__	,						UpperCamelCase__					) for image in images]

        snake_case   : Dict	  =      {'''pixel_values''': images}
        return BatchFeature(data=UpperCamelCase__	,						tensor_type=UpperCamelCase__					)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				:							str					=			{'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Union[str, Any]					=			[
      'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DinatForImageClassification',
      'DinatModel',
      'DinatPreTrainedModel',
      'DinatBackbone',
  ]

if TYPE_CHECKING:
  from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dinat import (
        DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DinatBackbone,
        DinatForImageClassification,
        DinatModel,
        DinatPreTrainedModel,
    )

else:
  import sys

  a				:							List[Any]					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






