




'''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 center_crop, 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, is_vision_available, logging


if is_vision_available():
       import PIL


A			   =logging.get_logger(__name__)


class       _a  (     A__			):
       __a    :					List[str]           = ["""pixel_values"""]
       def __init__(     self						:     Union[str, Any]       ,    lowercase						:     bool = True       ,    lowercase						:     Dict[str, int] = None       ,    lowercase						:     PILImageResampling = PIL.Image.NEAREST       ,    lowercase						:     bool = False       ,    lowercase						:     Dict[str, int] = None       ,    lowercase						:     Union[int, float] = 1 / 255       ,    lowercase						:     bool = False       ,    lowercase						:     bool = True       ,    lowercase						:     bool = True       ,    lowercase						:     Optional[Union[float, List[float]]] = None       ,    lowercase						:     Optional[Union[float, List[float]]] = None       ,    lowercase						:     bool = True       ,    **lowercase						:     Optional[Any]       ,    ):



           '''simple docstring'''

           super().__init__(**lowercase    )
           UpperCAmelCase      	=		size if size is not None else {'''height''': 346, '''width''': 346}
           UpperCAmelCase      	=		get_size_dict(lowercase    )
           UpperCAmelCase      	=		crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
           UpperCAmelCase      	=		get_size_dict(lowercase       ,    param_name='''crop_size'''    )

           UpperCAmelCase      	=		do_resize
           UpperCAmelCase      	=		size
           UpperCAmelCase      	=		resample
           UpperCAmelCase      	=		do_center_crop
           UpperCAmelCase      	=		crop_size
           UpperCAmelCase      	=		do_rescale
           UpperCAmelCase      	=		rescale_factor
           UpperCAmelCase      	=		rescale_offset
           UpperCAmelCase      	=		do_normalize
           UpperCAmelCase      	=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCAmelCase      	=		image_std if image_std is not None else IMAGENET_STANDARD_STD
           UpperCAmelCase      	=		include_top
       def    A				(     self						:     Optional[Any]       ,    lowercase						:     np.ndarray       ,    lowercase						:     Dict[str, int]       ,    lowercase						:     PILImageResampling = PIL.Image.NEAREST       ,    lowercase						:     Optional[Union[str, ChannelDimension]] = None       ,    **lowercase						:     Any       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		get_size_dict(lowercase    )
           if "height" not in size or "width" not in size:
               raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"    )
           return resize(
               lowercase       ,    size=(size['''height'''], size['''width'''])       ,    resample=lowercase       ,    data_format=lowercase       ,    **lowercase    )
       def    A				(     self						:     int       ,    lowercase						:     np.ndarray       ,    lowercase						:     Dict[str, int]       ,    lowercase						:     Optional[Union[str, ChannelDimension]] = None       ,    **lowercase						:     Optional[int]       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		get_size_dict(lowercase    )
           if "height" not in size or "width" not in size:
               raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"    )
           return center_crop(lowercase       ,    size=(size['''height'''], size['''width'''])       ,    data_format=lowercase       ,    **lowercase    )
       def    A				(     self						:     List[str]       ,    lowercase						:     np.ndarray       ,    lowercase						:     Union[int, float]       ,    lowercase						:     bool = True       ,    lowercase						:     Optional[Union[str, ChannelDimension]] = None       ,    **lowercase						:     Tuple       ,    ):



           '''simple docstring'''

           if offset:
               UpperCAmelCase      	=		(image - 127.5) * scale
               if data_format is not None:
                   UpperCAmelCase      	=		to_channel_dimension_format(lowercase       ,    lowercase    )
               UpperCAmelCase      	=		rescaled_image.astype(np.floataa    )
           else:
               UpperCAmelCase      	=		rescale(lowercase       ,    scale=lowercase       ,    data_format=lowercase       ,    **lowercase    )
           return rescaled_image
       def    A				(     self						:     Any       ,    lowercase						:     np.ndarray       ,    lowercase						:     Union[float, List[float]]       ,    lowercase						:     Union[float, List[float]]       ,    lowercase						:     Optional[Union[str, ChannelDimension]] = None       ,    **lowercase						:     Any       ,    ):



           '''simple docstring'''

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







       def    A				(     self						:     int       ,    lowercase						:     ImageInput       ,    lowercase						:     bool = None       ,    lowercase						:     Dict[str, int] = None       ,    lowercase						:     List[str]=None       ,    lowercase						:     bool = None       ,    lowercase						:     Dict[str, int] = None       ,    lowercase						:     bool = None       ,    lowercase						:     float = None       ,    lowercase						:     bool = None       ,    lowercase						:     bool = None       ,    lowercase						:     Optional[Union[float, List[float]]] = None       ,    lowercase						:     Optional[Union[float, List[float]]] = None       ,    lowercase						:     bool = None       ,    lowercase						:     Optional[Union[str, TensorType]] = None       ,    lowercase						:     ChannelDimension = ChannelDimension.FIRST       ,    **lowercase						:     Optional[Any]       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		do_resize if do_resize is not None else self.do_resize
           UpperCAmelCase      	=		resample if resample is not None else self.resample
           UpperCAmelCase      	=		do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCAmelCase      	=		do_rescale if do_rescale is not None else self.do_rescale
           UpperCAmelCase      	=		rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCAmelCase      	=		rescale_offset if rescale_offset is not None else self.rescale_offset
           UpperCAmelCase      	=		do_normalize if do_normalize is not None else self.do_normalize
           UpperCAmelCase      	=		image_mean if image_mean is not None else self.image_mean
           UpperCAmelCase      	=		image_std if image_std is not None else self.image_std
           UpperCAmelCase      	=		include_top if include_top is not None else self.include_top

           UpperCAmelCase      	=		size if size is not None else self.size
           UpperCAmelCase      	=		get_size_dict(lowercase    )
           UpperCAmelCase      	=		crop_size if crop_size is not None else self.crop_size
           UpperCAmelCase      	=		get_size_dict(lowercase       ,    param_name='''crop_size'''    )

           UpperCAmelCase      	=		make_list_of_images(lowercase    )

           if not valid_images(lowercase    ):
               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 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.
           UpperCAmelCase      	=		[to_numpy_array(lowercase    ) for image in images]

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

           if do_center_crop:
               UpperCAmelCase      	=		[self.center_crop(image=lowercase       ,    size=lowercase    ) for image in images]

           if do_rescale:
               UpperCAmelCase      	=		[self.rescale(image=lowercase       ,    scale=lowercase       ,    offset=lowercase    ) for image in images]

           if do_normalize:
               UpperCAmelCase      	=		[self.normalize(image=lowercase       ,    mean=lowercase       ,    std=lowercase    ) for image in images]

           if include_top:
               UpperCAmelCase      	=		[self.normalize(image=lowercase       ,    mean=[0, 0, 0]       ,    std=lowercase    ) for image in images]

           UpperCAmelCase      	=		[to_channel_dimension_format(lowercase       ,    lowercase    ) for image in images]

           UpperCAmelCase      	=		{'''pixel_values''': images}
           return BatchFeature(data=lowercase       ,    tensor_type=lowercase    )










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A			   ={'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A			   =[
       '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			   =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




