
"""simple docstring"""



from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


UpperCAmelCase__          = logging.get_logger(__name__)

class       lowerCAmelCase__				(						A__	):
     __a						=					['''pixel_values''']







     def __init__(							self			:			Optional[Any]    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Optional[Dict[str, int]] = None    ,      _lowerCamelCase			:			PILImageResampling = PILImageResampling.BILINEAR    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Dict[str, int] = None    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Union[int, float] = 1 / 255    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Optional[Union[float, List[float]]] = None    ,      _lowerCamelCase			:			Optional[Union[float, List[float]]] = None    ,      **_lowerCamelCase			:			Tuple    ,      ):
       super().__init__(**lowerCAmelCase__			)
       _snake_case					     =       size if size is not None else {'''shortest_edge''': 256}
       _snake_case					     =       get_size_dict(lowerCAmelCase__    ,      default_to_square=lowerCAmelCase__			)
       _snake_case					     =       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       _snake_case					     =       get_size_dict(lowerCAmelCase__    ,      param_name='''crop_size'''			)
       _snake_case					     =       do_resize
       _snake_case					     =       size
       _snake_case					     =       resample
       _snake_case					     =       do_center_crop
       _snake_case					     =       crop_size
       _snake_case					     =       do_rescale
       _snake_case					     =       rescale_factor
       _snake_case					     =       do_normalize
       _snake_case					     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _snake_case					     =       image_std if image_std is not None else IMAGENET_STANDARD_STD







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			np.ndarray    ,      _lowerCamelCase			:			Dict[str, int]    ,      _lowerCamelCase			:			PILImageResampling = PILImageResampling.BICUBIC    ,      _lowerCamelCase			:			Optional[Union[str, ChannelDimension]] = None    ,      **_lowerCamelCase			:			int    ,      ):
       _snake_case					     =       get_size_dict(lowerCAmelCase__    ,      default_to_square=lowerCAmelCase__			)
       if "shortest_edge" not in size:
         raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''			)
       _snake_case					     =       get_resize_output_image_size(lowerCAmelCase__    ,      size=size['''shortest_edge''']    ,      default_to_square=lowerCAmelCase__			)
       return resize(lowerCAmelCase__    ,      size=lowerCAmelCase__    ,      resample=lowerCAmelCase__    ,      data_format=lowerCAmelCase__    ,      **lowerCAmelCase__			)







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			np.ndarray    ,      _lowerCamelCase			:			Dict[str, int]    ,      _lowerCamelCase			:			Optional[Union[str, ChannelDimension]] = None    ,      **_lowerCamelCase			:			str    ,      ):
       _snake_case					     =       get_size_dict(lowerCAmelCase__			)
       if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''			)
       return center_crop(lowerCAmelCase__    ,      size=(size['''height'''], size['''width'''])    ,      data_format=lowerCAmelCase__    ,      **lowerCAmelCase__			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			np.ndarray    ,      _lowerCamelCase			:			float    ,      _lowerCamelCase			:			Optional[Union[str, ChannelDimension]] = None    ,      **_lowerCamelCase			:			int			):
       return rescale(lowerCAmelCase__    ,      scale=lowerCAmelCase__    ,      data_format=lowerCAmelCase__    ,      **lowerCAmelCase__			)







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			np.ndarray    ,      _lowerCamelCase			:			Union[float, List[float]]    ,      _lowerCamelCase			:			Union[float, List[float]]    ,      _lowerCamelCase			:			Optional[Union[str, ChannelDimension]] = None    ,      **_lowerCamelCase			:			str    ,      ):
       return normalize(lowerCAmelCase__    ,      mean=lowerCAmelCase__    ,      std=lowerCAmelCase__    ,      data_format=lowerCAmelCase__    ,      **lowerCAmelCase__			)







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			ImageInput    ,      _lowerCamelCase			:			Optional[bool] = None    ,      _lowerCamelCase			:			Dict[str, int] = None    ,      _lowerCamelCase			:			PILImageResampling = None    ,      _lowerCamelCase			:			bool = None    ,      _lowerCamelCase			:			Dict[str, int] = None    ,      _lowerCamelCase			:			Optional[bool] = None    ,      _lowerCamelCase			:			Optional[float] = None    ,      _lowerCamelCase			:			Optional[bool] = None    ,      _lowerCamelCase			:			Optional[Union[float, List[float]]] = None    ,      _lowerCamelCase			:			Optional[Union[float, List[float]]] = None    ,      _lowerCamelCase			:			Optional[Union[str, TensorType]] = None    ,      _lowerCamelCase			:			Union[str, ChannelDimension] = ChannelDimension.FIRST    ,      **_lowerCamelCase			:			Optional[int]    ,      ):
       _snake_case					     =       do_resize if do_resize is not None else self.do_resize
       _snake_case					     =       size if size is not None else self.size
       _snake_case					     =       get_size_dict(lowerCAmelCase__    ,      default_to_square=lowerCAmelCase__			)
       _snake_case					     =       resample if resample is not None else self.resample
       _snake_case					     =       do_center_crop if do_center_crop is not None else self.do_center_crop
       _snake_case					     =       crop_size if crop_size is not None else self.crop_size
       _snake_case					     =       get_size_dict(lowerCAmelCase__    ,      param_name='''crop_size'''			)
       _snake_case					     =       do_rescale if do_rescale is not None else self.do_rescale
       _snake_case					     =       rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case					     =       do_normalize if do_normalize is not None else self.do_normalize
       _snake_case					     =       image_mean if image_mean is not None else self.image_mean
       _snake_case					     =       image_std if image_std is not None else self.image_std

       _snake_case					     =       make_list_of_images(lowerCAmelCase__			)

       if not valid_images(lowerCAmelCase__			):
         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.
       _snake_case					     =       [to_numpy_array(lowerCAmelCase__			) for image in images]

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

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

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

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

       _snake_case					     =       [to_channel_dimension_format(lowerCAmelCase__    ,      lowerCAmelCase__			) for image in images]

       _snake_case					     =       {'''pixel_values''': images}
       return BatchFeature(data=lowerCAmelCase__    ,      tensor_type=lowerCAmelCase__			)







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			List[Tuple] = None			):
       _snake_case					     =       outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
         if len(lowerCAmelCase__			) != len(lowerCAmelCase__			):
           raise ValueError(
               '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''			)

         if is_torch_tensor(lowerCAmelCase__			):
           _snake_case					     =       target_sizes.numpy()

         _snake_case					     =       []

         for idx in range(len(lowerCAmelCase__			)			):
           _snake_case					     =       torch.nn.functional.interpolate(
               logits[idx].unsqueeze(dim=0			)    ,      size=target_sizes[idx]    ,      mode='''bilinear'''    ,      align_corners=lowerCAmelCase__			)
           _snake_case					     =       resized_logits[0].argmax(dim=0			)
           semantic_segmentation.append(lowerCAmelCase__			)
       else:
         _snake_case					     =       logits.argmax(dim=1			)
         _snake_case					     =       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

       return semantic_segmentation







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return np.linalg.norm(np.array(lowercase__      ) - np.array(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=5      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		zip(lowercase__    ,       lowercase__      )
						# List of distances of all points from the point to be classified
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						for data_point in data:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		euclidean_distance(data_point[0]    ,       lowercase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						__SCREAMING_SNAKE_CASE						:     int					=		[i[1] for i in sorted(lowercase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__SCREAMING_SNAKE_CASE						:     Any					=		Counter(lowercase__      ).most_common(1      )[0][0]
						return classes[result]


if __name__ == "__main__":
						print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
