




'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    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  (     lowerCamelCase__			):
       __a    :					List[str]           = ['pixel_values']
       def __init__(     self						:     str       ,    lowercase						:     List[str] = True       ,    lowercase						:     int = None       ,    lowercase						:     Any = True       ,    lowercase						:     str = None       ,    lowercase						:     Optional[Any] = PILImageResampling.BICUBIC       ,    lowercase						:     Optional[Any] = True       ,    lowercase						:     str = 1 / 255       ,    lowercase						:     Dict = True       ,    lowercase						:     Optional[Any] = None       ,    lowercase						:     str = None       ,    **lowercase						:     Dict       ,    ):



           '''simple docstring'''

           super().__init__(**lowercase_    )
           UpperCAmelCase      	=		crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
           UpperCAmelCase      	=		get_size_dict(lowercase_       ,    param_name='''crop_size'''    )
           UpperCAmelCase      	=		size if size is not None else {'''height''': 224, '''width''': 224}
           UpperCAmelCase      	=		get_size_dict(lowercase_    )

           UpperCAmelCase      	=		do_center_crop
           UpperCAmelCase      	=		crop_size
           UpperCAmelCase      	=		do_resize
           UpperCAmelCase      	=		size
           UpperCAmelCase      	=		resample
           UpperCAmelCase      	=		do_rescale
           UpperCAmelCase      	=		rescale_factor
           UpperCAmelCase      	=		do_normalize
           UpperCAmelCase      	=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           UpperCAmelCase      	=		image_std if image_std is not None else IMAGENET_DEFAULT_STD
       def    A				(     self						:     Dict       ,    lowercase						:     int       ,    lowercase						:     Tuple       ,    lowercase						:     Optional[int] = None       ,    lowercase						:     Tuple = None       ,    **lowercase						:     Optional[int]       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.size if size is None else size
           UpperCAmelCase      	=		get_size_dict(lowercase_    )
           UpperCAmelCase      	=		get_size_dict(lowercase_       ,    param_name='''crop_size'''    )

           UpperCAmelCase	,					UpperCAmelCase      	=		get_image_size(lowercase_    )
           UpperCAmelCase      	=		min(lowercase_       ,    lowercase_    )
           UpperCAmelCase      	=		(size['''height'''] / crop_size['''height''']) * min_dim
           UpperCAmelCase      	=		(size['''width'''] / crop_size['''width''']) * min_dim
           return center_crop(lowercase_       ,    size=(cropped_height, cropped_width)       ,    data_format=lowercase_       ,    **lowercase_    )
       def    A				(     self						:     Optional[int]       ,    lowercase						:     str       ,    lowercase						:     int       ,    lowercase						:     Any = PIL.Image.BICUBIC       ,    lowercase						:     Any = None       ,    **lowercase						:     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 contain the keys 'height' and 'width'. Got {size.keys()}"    )
           return resize(
               lowercase_       ,    size=(size['''height'''], size['''width'''])       ,    resample=lowercase_       ,    data_format=lowercase_       ,    **lowercase_    )
       def    A				(     self						:     Union[str, Any]       ,    lowercase						:     List[str]       ,    lowercase						:     Union[str, Any]       ,    lowercase						:     str = None       ,    **lowercase						:     Optional[int]       ,    ):



           '''simple docstring'''

           return rescale(lowercase_       ,    scale=lowercase_       ,    data_format=lowercase_       ,    **lowercase_    )
       def    A				(     self						:     Union[str, Any]       ,    lowercase						:     Union[str, Any]       ,    lowercase						:     List[str]       ,    lowercase						:     Any       ,    lowercase						:     str = None       ,    **lowercase						:     str       ,    ):



           '''simple docstring'''

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







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



           '''simple docstring'''

           UpperCAmelCase      	=		do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCAmelCase      	=		crop_size if crop_size is not None else self.crop_size
           UpperCAmelCase      	=		get_size_dict(lowercase_       ,    param_name='''crop_size'''    )
           UpperCAmelCase      	=		do_resize if do_resize is not None else self.do_resize
           UpperCAmelCase      	=		size if size is not None else self.size
           UpperCAmelCase      	=		get_size_dict(lowercase_    )
           UpperCAmelCase      	=		resample if resample is not None else self.resample
           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      	=		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      	=		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_center_crop and crop_size is None:
               raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''    )

           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.'''    )

           if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''    )

           # All transformations expect numpy arrays.
           UpperCAmelCase      	=		[to_numpy_array(lowercase_    ) for image in images]

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

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

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

           if do_normalize:
               UpperCAmelCase      	=		[self.normalize(image=lowercase_       ,    mean=lowercase_       ,    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__)




