
from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case      = logging.get_logger(__name__)
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["pixel_values"]

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BICUBIC					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Union[int, float] = 1 / 2_55					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN					, SCREAMING_SNAKE_CASE__			:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD					, **SCREAMING_SNAKE_CASE__			:			Tuple					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					size if size is not None else {'shortest_edge': 2_24}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, default_to_square=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, 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						    =					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      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BICUBIC					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			int					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, default_to_square=SCREAMING_SNAKE_CASE__   )
      # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
      if "shortest_edge" in size:
           UpperCamelCase						    =					int((2_56 / 2_24) * size['shortest_edge']   )
           UpperCamelCase						    =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__					, size=SCREAMING_SNAKE_CASE__					, default_to_square=SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					{'height': output_size[0], 'width': output_size[1]}
      if "height" not in size_dict or "width" not in size_dict:
           raise ValueError(
               F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'   )
      return resize(
          SCREAMING_SNAKE_CASE__					, size=(size_dict['height'], size_dict['width'])					, resample=SCREAMING_SNAKE_CASE__					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      if "height" not in size or "width" not in size:
           raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'   )
      return center_crop(SCREAMING_SNAKE_CASE__					, size=(size['height'], size['width'])					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Union[int, float]					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			int					, ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Union[float, List[float]]					, SCREAMING_SNAKE_CASE__			:			Union[float, List[float]]					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			str					, ):






      """simple docstring"""



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






 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			ImageInput					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Dict[str, int]] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = None					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Dict[str, int]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, SCREAMING_SNAKE_CASE__			:			Optional[float] = None					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[float, Iterable[float]]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[float, Iterable[float]]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[TensorType] = None					, SCREAMING_SNAKE_CASE__			:			ChannelDimension = ChannelDimension.FIRST					, **SCREAMING_SNAKE_CASE__			:			int					, ):






      """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						    =					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						    =					size if size is not None else self.size
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, default_to_square=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					crop_size if crop_size is not None else self.crop_size
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, param_name='crop_size'   )

      UpperCamelCase						    =					make_list_of_images(SCREAMING_SNAKE_CASE__   )

      if not valid_images(SCREAMING_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:
           raise ValueError('Size 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(SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_resize:
           UpperCamelCase						    =					[self.resize(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_center_crop:
           UpperCamelCase						    =					[self.center_crop(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_rescale:
           UpperCamelCase						    =					[self.rescale(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_normalize:
           UpperCamelCase						    =					[self.normalize(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      UpperCamelCase						    =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      UpperCamelCase						    =					{'pixel_values': images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE__					, tensor_type=SCREAMING_SNAKE_CASE__   )




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


_snake_case      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Optional[Any]:
     UpperCamelCase						    =					b.T
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=1			)
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=0			)
     UpperCamelCase						    =					np.matmul(_lowercase						,     _lowercase			)
     UpperCamelCase						    =					aa[:, None] - 2 * ab + ba[None, :]
     return d

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      List[Any]:
     UpperCamelCase						    =					x.reshape(-1						,     3			)
     UpperCamelCase						    =					squared_euclidean_distance(_lowercase						,     _lowercase			)
     return np.argmin(_lowercase						,     axis=1			)







class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["pixel_values"]

 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[Union[List[List[int]], np.ndarray]] = None					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			bool = True					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					size if size is not None else {'height': 2_56, 'width': 2_56}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   ) if clusters is not None else None
      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					resample
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					do_color_quantize

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_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(
          SCREAMING_SNAKE_CASE__					, size=(size['height'], size['width'])					, resample=SCREAMING_SNAKE_CASE__					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, ):






      """simple docstring"""



      UpperCamelCase						    =					rescale(image=SCREAMING_SNAKE_CASE__					, scale=1 / 127.5					, data_format=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					image - 1
      return image






 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			ImageInput					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = None					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[List[List[int]], np.ndarray]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, TensorType]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					, **SCREAMING_SNAKE_CASE__			:			str					, ):






      """simple docstring"""



      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(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					resample if resample is not None else self.resample
      UpperCamelCase						    =					do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase						    =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCamelCase						    =					clusters if clusters is not None else self.clusters
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					make_list_of_images(SCREAMING_SNAKE_CASE__   )

      if not valid_images(SCREAMING_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.
      UpperCamelCase						    =					[to_numpy_array(SCREAMING_SNAKE_CASE__   ) for image in images]

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

      if do_normalize:
           UpperCamelCase						    =					[self.normalize(image=SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_color_quantize:
           UpperCamelCase						    =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					, ChannelDimension.LAST   ) for image in images]
           # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
           UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					color_quantize(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).reshape(images.shape[:-1]   )

           # flatten to (batch_size, height*width)
           UpperCamelCase						    =					images.shape[0]
           UpperCamelCase						    =					images.reshape(SCREAMING_SNAKE_CASE__					, -1   )

           # We need to convert back to a list of images to keep consistent behaviour across processors.
           UpperCamelCase						    =					list(SCREAMING_SNAKE_CASE__   )
      else:
           UpperCamelCase						    =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      UpperCamelCase						    =					{'input_ids': images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE__					, tensor_type=SCREAMING_SNAKE_CASE__   )


