
"""simple docstring"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_a   :      Union[str, Any]       =				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
_a,      _a,      _a,      _a   :      Union[str, Any]       =				train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

_a   :      int       =				KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_a   :      Optional[int]       =				[[1, 2, 1, 4], [2, 3, 4, 5]]

_a   :      int       =				knn.predict(X_new)

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



"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    __lowercase				:			List[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowercase				:			Optional[Any]	=       PipelineTesterMixin.required_optional_params - {"""latents"""}
    def        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()    ,     reason="XFormers attention is only available with CUDA and `xformers` installed"    ,     )
    def        lowerCamelCase__						(     self  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     self  ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1  )
    def        lowerCamelCase__						(     self  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


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

