


"""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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


lowerCAmelCase__				    =       logging.get_logger(__name__)
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							b.T
  lowerCAmelCase					:					Union[str, Any]								=							np.sum(np.square(SCREAMING_SNAKE_CASE    )    ,				axis=1    )
  lowerCAmelCase					:					Optional[int]								=							np.sum(np.square(SCREAMING_SNAKE_CASE    )    ,				axis=0    )
  lowerCAmelCase					:					Dict								=							np.matmul(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Optional[int]								=							aa[:, None] - 2 * ab + ba[None, :]
  return d
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							x.reshape(-1    ,				3    )
  lowerCAmelCase					:					Optional[Any]								=							squared_euclidean_distance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  return np.argmin(SCREAMING_SNAKE_CASE    ,				axis=1    )





class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    str       =["pixel_values"]

       def __init__(						self		,					snake_case__ = None		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = PILImageResampling.BILINEAR		,					snake_case__ = True		,					snake_case__ = True		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(**snake_case__						)
         lowerCAmelCase					:					List[Any]								=							size if size is not None else {"height": 256, "width": 256}
         lowerCAmelCase					:					Any								=							get_size_dict(snake_case__						)
         lowerCAmelCase					:					Tuple								=							np.array(snake_case__						) if clusters is not None else None
         lowerCAmelCase					:					Union[str, Any]								=							do_resize
         lowerCAmelCase					:					Optional[Any]								=							size
         lowerCAmelCase					:					Tuple								=							resample
         lowerCAmelCase					:					Dict								=							do_normalize
         lowerCAmelCase					:					Any								=							do_color_quantize

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = PILImageResampling.BILINEAR		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					str								=							get_size_dict(snake_case__						)
         if "height" not in size or "width" not in size:
           raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""						)
         return resize(
             snake_case__		,					size=(size["height"], size["width"])		,					resample=snake_case__		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							rescale(image=snake_case__		,					scale=1 / 127.5		,					data_format=snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							image - 1
         return image







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = ChannelDimension.FIRST		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase					:					int								=							size if size is not None else self.size
         lowerCAmelCase					:					Tuple								=							get_size_dict(snake_case__						)
         lowerCAmelCase					:					int								=							resample if resample is not None else self.resample
         lowerCAmelCase					:					Dict								=							do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase					:					Tuple								=							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         lowerCAmelCase					:					Optional[Any]								=							clusters if clusters is not None else self.clusters
         lowerCAmelCase					:					Any								=							np.array(snake_case__						)

         lowerCAmelCase					:					str								=							make_list_of_images(snake_case__						)

         if not valid_images(snake_case__						):
           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_color_quantize and clusters is None:
           raise ValueError("Clusters must be specified if do_color_quantize is True."						)

         # All transformations expect numpy arrays.
         lowerCAmelCase					:					Tuple								=							[to_numpy_array(snake_case__						) for image in images]

         if do_resize:
           lowerCAmelCase					:					str								=							[self.resize(image=snake_case__		,					size=snake_case__		,					resample=snake_case__						) for image in images]

         if do_normalize:
           lowerCAmelCase					:					Any								=							[self.normalize(image=snake_case__						) for image in images]

         if do_color_quantize:
           lowerCAmelCase					:					Dict								=							[to_channel_dimension_format(snake_case__		,					ChannelDimension.LAST						) for image in images]
           # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
           lowerCAmelCase					:					Union[str, Any]								=							np.array(snake_case__						)
           lowerCAmelCase					:					Optional[int]								=							color_quantize(snake_case__		,					snake_case__						).reshape(images.shape[:-1]						)

           # flatten to (batch_size, height*width)
           lowerCAmelCase					:					int								=							images.shape[0]
           lowerCAmelCase					:					Dict								=							images.reshape(snake_case__		,					-1						)

           # We need to convert back to a list of images to keep consistent behaviour across processors.
           lowerCAmelCase					:					Optional[int]								=							list(snake_case__						)
         else:
           lowerCAmelCase					:					Union[str, Any]								=							[to_channel_dimension_format(snake_case__		,					snake_case__						) for image in images]

         lowerCAmelCase					:					Optional[Any]								=							{"input_ids": images}
         return BatchFeature(data=snake_case__		,					tensor_type=snake_case__						)









"""simple docstring"""







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__				    =       {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__				    =       [
         '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''VanForImageClassification''',
         '''VanModel''',
         '''VanPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase__				    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)





