'''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  :Dict  				=    logging.get_logger(__name__)


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	List[Any]:





     '''simple docstring'''





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


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





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






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						["pixel_values"]






    def __init__(       self    ,							lowercase__ = None    ,							lowercase__ = True    ,							lowercase__ = None    ,							lowercase__ = PILImageResampling.BILINEAR    ,							lowercase__ = True    ,							lowercase__ = True    ,							**lowercase__    ,							)							-> None:
         super().__init__(**lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       size if size is not None else {'height': 256, 'width': 256}
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_size_dict(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array(lowercase__ ) if clusters is not None else None
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       do_resize
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       size
         SCREAMING_SNAKE_CASE					:			str    	=       resample
         SCREAMING_SNAKE_CASE					:			str    	=       do_normalize
         SCREAMING_SNAKE_CASE					:			Any    	=       do_color_quantize






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = PILImageResampling.BILINEAR    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       get_size_dict(lowercase__ )
         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(
             lowercase__    ,							size=(size['height'], size['width'])    ,							resample=lowercase__    ,							data_format=lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							)							-> np.ndarray:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       rescale(image=lowercase__    ,							scale=1 / 1_2_7.5    ,							data_format=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       image - 1
         return image






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = ChannelDimension.FIRST    ,							**lowercase__    ,							)							-> PIL.Image.Image:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       do_resize if do_resize is not None else self.do_resize
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       size if size is not None else self.size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       get_size_dict(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       resample if resample is not None else self.resample
         SCREAMING_SNAKE_CASE					:			List[str]    	=       do_normalize if do_normalize is not None else self.do_normalize
         SCREAMING_SNAKE_CASE					:			str    	=       do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         SCREAMING_SNAKE_CASE					:			str    	=       clusters if clusters is not None else self.clusters
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       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_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					:			Optional[int]    	=       [to_numpy_array(lowercase__ ) for image in images]

         if do_resize:
              SCREAMING_SNAKE_CASE					:			Dict    	=       [self.resize(image=lowercase__    ,							size=lowercase__    ,							resample=lowercase__ ) for image in images]

         if do_normalize:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       [self.normalize(image=lowercase__ ) for image in images]

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

              # flatten to (batch_size, height*width)
              SCREAMING_SNAKE_CASE					:			Tuple    	=       images.shape[0]
              SCREAMING_SNAKE_CASE					:			Dict    	=       images.reshape(lowercase__    ,							-1 )

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

         SCREAMING_SNAKE_CASE					:			Dict    	=       {'input_ids': images}
         return BatchFeature(data=lowercase__    ,							tensor_type=lowercase__ )




'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         self.assertTrue(torch.allclose(gelu_python(lowercase__ )    ,							torch_builtin(lowercase__ ) ) )
         self.assertFalse(torch.allclose(gelu_python(lowercase__ )    ,							gelu_new(lowercase__ ) ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_activation('gelu_10' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch_builtin(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       geluaa(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.where(y_gelu_aa < 1_0.0    ,							1    ,							0 )

         self.assertTrue(torch.max(lowercase__ ).item() == 1_0.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,							y_gelu_aa * clipped_mask ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         get_activation('gelu' )
         get_activation('gelu_10' )
         get_activation('gelu_fast' )
         get_activation('gelu_new' )
         get_activation('gelu_python' )
         get_activation('gelu_pytorch_tanh' )
         get_activation('linear' )
         get_activation('mish' )
         get_activation('quick_gelu' )
         get_activation('relu' )
         get_activation('sigmoid' )
         get_activation('silu' )
         get_activation('swish' )
         get_activation('tanh' )
         with self.assertRaises(lowercase__ ):
              get_activation('bogus' )
         with self.assertRaises(lowercase__ ):
              get_activation(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			str    	=       1
         SCREAMING_SNAKE_CASE					:			List[str]    	=       get_activation('gelu' )
         self.assertEqual(acta.a    ,							1 )
         with self.assertRaises(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       acta.a



