


import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class     snake_case__  (      _A						,       _A						,       unittest.TestCase  ):
				__lowerCAmelCase		=      IFImgaImgPipeline
				__lowerCAmelCase		=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				__lowerCAmelCase		=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__lowerCAmelCase		=      PipelineTesterMixin.required_optional_params - {'''latents'''}
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self._get_dummy_components()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=0      ):




										"""simple docstring"""




										if str(_UpperCAmelCase      ).startswith('mps'      ):
																__magic_name__				:							List[Any]				=      torch.manual_seed(_UpperCAmelCase      )
										else:
																__magic_name__				:							Dict				=      torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      floats_tensor((1, 3, 32, 32)     ,      rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )

										__magic_name__				:							str				=      {
										    'prompt': 'A painting of a squirrel eating a burger',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'output_type': 'numpy',
										}

										return inputs
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_save_load_optional_components()
				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available()     ,      reason='XFormers attention is only available with CUDA and `xformers` installed'     ,      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )
				@unittest.skipIf(torch_device != 'cuda'     ,      reason='float16 requires CUDA'      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										super().test_save_load_floataa(expected_max_diff=1E-1      )
				@unittest.skipIf(torch_device != 'cuda'     ,      reason='float16 requires CUDA'      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										super().test_floataa_inference(expected_max_diff=1E-1      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_save_load_local()





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,      )






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


