






'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class UpperCAmelCase_       (     __A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      ShapEImgaImgPipeline
      UpperCamelCase_             =      ['''image''']
      UpperCamelCase_             =      ['''image''']
      UpperCamelCase_             =      [
          '''num_images_per_prompt''',
          '''num_inference_steps''',
          '''generator''',
          '''latents''',
          '''guidance_scale''',
          '''frame_size''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCamelCase_             =      False






      @property
      def 					A__   (			self      :		Any		)	->	List[Any]:


       '''simple docstring'''

       return 32






      @property
      def 					A__   (			self      :		Optional[int]		)	->	Any:


       '''simple docstring'''

       return 32






      @property
      def 					A__   (			self      :		str		)	->	Tuple:


       '''simple docstring'''

       return self.time_input_dim * 4






      @property
      def 					A__   (			self      :		Any		)	->	List[str]:


       '''simple docstring'''

       return 8






      @property
      def 					A__   (			self      :		Union[str, Any]		)	->	Tuple:


       '''simple docstring'''

       torch.manual_seed(0		)
       lowercase       : List[Any]						=CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size  ,   image_size=64  ,   projection_dim=self.text_embedder_hidden_size  ,   intermediate_size=37  ,   num_attention_heads=4  ,   num_channels=3  ,   num_hidden_layers=5  ,   patch_size=1  ,   )

       lowercase       : Optional[int]						=CLIPVisionModel(UpperCAmelCase		)
       return model






      @property
      def 					A__   (			self      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : Dict						=CLIPImageProcessor(
           crop_size=224  ,   do_center_crop=UpperCAmelCase  ,   do_normalize=UpperCAmelCase  ,   do_resize=UpperCAmelCase  ,   image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]  ,   image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]  ,   resample=3  ,   size=224  ,   )

       return image_processor






      @property
      def 					A__   (			self      :		str		)	->	List[str]:


       '''simple docstring'''

       torch.manual_seed(0		)

       lowercase       : Union[str, Any]						={
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 16,
           '''embedding_dim''': self.time_input_dim,
           '''num_embeddings''': 32,
           '''embedding_proj_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''num_layers''': 1,
           '''clip_embed_dim''': self.time_input_dim * 2,
           '''additional_embeddings''': 0,
           '''time_embed_act_fn''': '''gelu''',
           '''norm_in_type''': '''layer''',
           '''embedding_proj_norm_type''': '''layer''',
           '''encoder_hid_proj_type''': None,
           '''added_emb_type''': None,
       }

       lowercase       : Any						=PriorTransformer(**UpperCAmelCase		)
       return model






      @property
      def 					A__   (			self      :		Optional[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       torch.manual_seed(0		)

       lowercase       : List[str]						={
           '''param_shapes''': (
               (self.renderer_dim, 93),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           '''d_latent''': self.time_input_dim,
           '''d_hidden''': self.renderer_dim,
           '''n_output''': 12,
           '''background''': (
               0.1,
               0.1,
               0.1,
           ),
       }
       lowercase       : Optional[int]						=ShapERenderer(**UpperCAmelCase		)
       return model






      def 					A__   (			self      :		Union[str, Any]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : str						=self.dummy_prior
       lowercase       : List[str]						=self.dummy_image_encoder
       lowercase       : str						=self.dummy_image_processor
       lowercase       : Dict						=self.dummy_renderer

       lowercase       : Optional[Any]						=HeunDiscreteScheduler(
           beta_schedule='''exp'''  ,   num_train_timesteps=1024  ,   prediction_type='''sample'''  ,   use_karras_sigmas=UpperCAmelCase  ,   clip_sample=UpperCAmelCase  ,   clip_sample_range=1.0  ,   )
       lowercase       : Any						={
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''image_processor''': image_processor,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components






      def 					A__   (			self      :		Optional[Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		List[str]=0		)	->	List[str]:


       '''simple docstring'''

       lowercase       : List[str]						=floats_tensor((1, 3, 64, 64)  ,   rng=random.Random(UpperCAmelCase		)		).to(UpperCAmelCase		)

       if str(UpperCAmelCase		).startswith('''mps'''		):
        lowercase       : str						=torch.manual_seed(UpperCAmelCase		)
       else:
        lowercase       : Tuple						=torch.Generator(device=UpperCAmelCase		).manual_seed(UpperCAmelCase		)
       lowercase       : List[str]						={
           '''image''': input_image,
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs






      def 					A__   (			self      :		Dict		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : List[str]						='''cpu'''

       lowercase       : Optional[int]						=self.get_dummy_components()

       lowercase       : List[Any]						=self.pipeline_class(**UpperCAmelCase		)
       lowercase       : Tuple						=pipe.to(UpperCAmelCase		)

       pipe.set_progress_bar_config(disable=UpperCAmelCase		)

       lowercase       : Tuple						=pipe(**self.get_dummy_inputs(UpperCAmelCase		)		)
       lowercase       : Tuple						=output.images[0]
       lowercase       : int						=image[0, -3:, -3:, -1]

       assert image.shape == (20, 32, 32, 3)

       lowercase       : Tuple						=np.array(
           [
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
           ]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2






      def 					A__   (			self      :		Tuple		)	->	List[Any]:


       '''simple docstring'''

       self._test_inference_batch_consistent(batch_sizes=[1, 2]		)






      def 					A__   (			self      :		Dict		)	->	str:


       '''simple docstring'''

       lowercase       : Tuple						=torch_device == '''cpu'''
       lowercase       : List[str]						=True
       self._test_inference_batch_single_identical(
           batch_size=2  ,   test_max_difference=UpperCAmelCase  ,   relax_max_difference=UpperCAmelCase  ,   )






      def 					A__   (			self      :		List[str]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : List[Any]						=self.get_dummy_components()
       lowercase       : Optional[int]						=self.pipeline_class(**UpperCAmelCase		)
       lowercase       : Optional[Any]						=pipe.to(UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=UpperCAmelCase		)

       lowercase       : Optional[Any]						=1
       lowercase       : Optional[int]						=2

       lowercase       : Tuple						=self.get_dummy_inputs(UpperCAmelCase		)

       for key in inputs.keys():
        if key in self.batch_params:
         lowercase       : Any						=batch_size * [inputs[key]]

       lowercase       : List[Any]						=pipe(**UpperCAmelCase  ,   num_images_per_prompt=UpperCAmelCase		)[0]

       assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      def 					A__   (			self      :		List[Any]		)	->	List[str]:


       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






      def 					A__   (			self      :		str		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Tuple						=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''		)
       lowercase       : List[Any]						=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/shap_e/test_shap_e_img2img_out.npy'''		)
       lowercase       : int						=ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''		)
       lowercase       : str						=pipe.to(UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=UpperCAmelCase		)

       lowercase       : Tuple						=torch.Generator(device=UpperCAmelCase		).manual_seed(0		)

       lowercase       : Optional[Any]						=pipe(
           UpperCAmelCase  ,   generator=UpperCAmelCase  ,   guidance_scale=3.0  ,   num_inference_steps=64  ,   frame_size=64  ,   output_type='''np'''  ,   ).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(UpperCAmelCase  ,   UpperCAmelCase		)





"""simple docstring"""

from collections import Counter

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

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).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]))



