
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    DDIMPipeline
				snake_case_						   =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				snake_case_						   =    PipelineTesterMixin.required_optional_params - {
				    '''num_images_per_prompt''',
				    '''latents''',
				    '''callback''',
				    '''callback_steps''',
				}
				snake_case_						   =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
				snake_case_						   =    False




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								torch.manual_seed(0				)
								__a										=    UNetaDModel(
								    block_out_channels=(32, 64)		,     layers_per_block=2		,     sample_size=32		,     in_channels=3		,     out_channels=3		,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,     )
								__a										=    DDIMScheduler()
								__a										=    {'''unet''': unet, '''scheduler''': scheduler}
								return components




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=0				)    ->      List[str]:


								'''simple docstring'''




								if str(_snake_case				).startswith('''mps'''				):
												__a										=    torch.manual_seed(_snake_case				)
								else:
												__a										=    torch.Generator(device=_snake_case				).manual_seed(_snake_case				)
								__a										=    {
								    '''batch_size''': 1,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}
								return inputs




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    '''cpu'''

								__a										=    self.get_dummy_components()
								__a										=    self.pipeline_class(**_snake_case				)
								pipe.to(_snake_case				)
								pipe.set_progress_bar_config(disable=_snake_case				)

								__a										=    self.get_dummy_inputs(_snake_case				)
								__a										=    pipe(**_snake_case				).images
								__a										=    image[0, -3:, -3:, -1]

								self.assertEqual(image.shape		,     (1, 32, 32, 3)				)
								__a										=    np.array(
								    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]				)
								__a										=    np.abs(image_slice.flatten() - expected_slice				).max()
								self.assertLessEqual(_snake_case		,     1E-3				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								super().test_save_load_local(expected_max_difference=3E-3				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								super().test_save_load_optional_components(expected_max_difference=3E-3				)




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


								'''simple docstring'''




								super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    '''google/ddpm-cifar10-32'''

								__a										=    UNetaDModel.from_pretrained(_snake_case				)
								__a										=    DDIMScheduler()

								__a										=    DDIMPipeline(unet=_snake_case		,     scheduler=_snake_case				)
								ddim.to(_snake_case				)
								ddim.set_progress_bar_config(disable=_snake_case				)

								__a										=    torch.manual_seed(0				)
								__a										=    ddim(generator=_snake_case		,     eta=0.0		,     output_type='''numpy'''				).images

								__a										=    image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								__a										=    np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]				)

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




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


								'''simple docstring'''




								__a										=    '''google/ddpm-ema-bedroom-256'''

								__a										=    UNetaDModel.from_pretrained(_snake_case				)
								__a										=    DDIMScheduler.from_pretrained(_snake_case				)

								__a										=    DDIMPipeline(unet=_snake_case		,     scheduler=_snake_case				)
								ddpm.to(_snake_case				)
								ddpm.set_progress_bar_config(disable=_snake_case				)

								__a										=    torch.manual_seed(0				)
								__a										=    ddpm(generator=_snake_case		,     output_type='''numpy'''				).images

								__a										=    image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)
								__a										=    np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]				)

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



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]))
