






'''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


a_											=			logging.get_logger(__name__)





def 			_a(	UpperCamelCase__					:    str,						UpperCamelCase__					:    Union[str, Any]				):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					Optional[int]						=b.T
      SCREAMING_SNAKE_CASE__  :					Tuple						=np.sum(np.square(lowerCAmelCase_				),						axis=1				)
      SCREAMING_SNAKE_CASE__  :					Any						=np.sum(np.square(lowerCAmelCase_				),						axis=0				)
      SCREAMING_SNAKE_CASE__  :					List[str]						=np.matmul(lowerCAmelCase_,						lowerCAmelCase_				)
      SCREAMING_SNAKE_CASE__  :					Tuple						=aa[:, None] - 2 * ab + ba[None, :]
      return d





def 			_a(	UpperCamelCase__					:    Any,						UpperCamelCase__					:    Optional[int]				):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					List[str]						=x.reshape(-1,						3				)
      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=squared_euclidean_distance(lowerCAmelCase_,						lowerCAmelCase_				)
      return np.argmin(lowerCAmelCase_,						axis=1				)







class   __SCREAMING_SNAKE_CASE   (      _UpperCamelCase					):
 snake_case_					=				['pixel_values']




 def __init__(   self						:				Optional[Any]       ,   __lowercase						:				Optional[Union[List[List[int]], np.ndarray]] = None       ,   __lowercase						:				bool = True       ,   __lowercase						:				Dict[str, int] = None       ,   __lowercase						:				PILImageResampling = PILImageResampling.BILINEAR       ,   __lowercase						:				bool = True       ,   __lowercase						:				bool = True       ,   **__lowercase						:				Optional[int]       ,   )						->    Union[str, Any]:
       super().__init__(**_UpperCAmelCase   )
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=size if size is not None else {'height': 2_56, 'width': 2_56}
       SCREAMING_SNAKE_CASE__  :					Dict						=get_size_dict(_UpperCAmelCase   )
       SCREAMING_SNAKE_CASE__  :					Any						=np.array(_UpperCAmelCase   ) if clusters is not None else None
       SCREAMING_SNAKE_CASE__  :					List[str]						=do_resize
       SCREAMING_SNAKE_CASE__  :					List[Any]						=size
       SCREAMING_SNAKE_CASE__  :					str						=resample
       SCREAMING_SNAKE_CASE__  :					str						=do_normalize
       SCREAMING_SNAKE_CASE__  :					Tuple						=do_color_quantize




 def   __magic_name__						(   self						:				int       ,   __lowercase						:				np.ndarray       ,   __lowercase						:				Dict[str, int]       ,   __lowercase						:				PILImageResampling = PILImageResampling.BILINEAR       ,   __lowercase						:				Optional[Union[str, ChannelDimension]] = None       ,   **__lowercase						:				Tuple       ,   )						->    Tuple:
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=get_size_dict(_UpperCAmelCase   )
       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(
           _UpperCAmelCase       ,   size=(size['''height'''], size['''width'''])       ,   resample=_UpperCAmelCase       ,   data_format=_UpperCAmelCase       ,   **_UpperCAmelCase   )




 def   __magic_name__						(   self						:				Dict       ,   __lowercase						:				np.ndarray       ,   __lowercase						:				Optional[Union[str, ChannelDimension]] = None       ,   )						->    int:
       SCREAMING_SNAKE_CASE__  :					str						=rescale(image=_UpperCAmelCase       ,   scale=1 / 1_27.5       ,   data_format=_UpperCAmelCase   )
       SCREAMING_SNAKE_CASE__  :					Dict						=image - 1
       return image




 def   __magic_name__						(   self						:				Union[str, Any]       ,   __lowercase						:				ImageInput       ,   __lowercase						:				bool = None       ,   __lowercase						:				Dict[str, int] = None       ,   __lowercase						:				PILImageResampling = None       ,   __lowercase						:				bool = None       ,   __lowercase						:				Optional[bool] = None       ,   __lowercase						:				Optional[Union[List[List[int]], np.ndarray]] = None       ,   __lowercase						:				Optional[Union[str, TensorType]] = None       ,   __lowercase						:				Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,   **__lowercase						:				List[str]       ,   )						->    Any:
       SCREAMING_SNAKE_CASE__  :					Tuple						=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__  :					Any						=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__  :					Dict						=get_size_dict(_UpperCAmelCase   )
       SCREAMING_SNAKE_CASE__  :					Dict						=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=clusters if clusters is not None else self.clusters
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=np.array(_UpperCAmelCase   )

       SCREAMING_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 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.
       SCREAMING_SNAKE_CASE__  :					int						=[to_numpy_array(_UpperCAmelCase   ) for image in images]

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

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

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

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

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

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




'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Tuple      ,*_UpperCAmelCase   :			str      ,**_UpperCAmelCase   :			int						):
									warnings.warn(
									    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use DeiTImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




