



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCAmelCase			  =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__UpperCAmelCase			  =			'''    \"""
    Output class for the scheduler\'s step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"""

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
'''



class 							lowerCAmelCase_						(    unittest.TestCase				):
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    str		      =      tempfile.mkdtemp()
          os.makedirs(os.path.join(self.diffusers_dir,     'schedulers/'			)			)
          UpperCamelCase       :    int		      =      self.diffusers_dir
          shutil.copy(
              os.path.join(SCREAMING_SNAKE_CASE_,     'src/diffusers/schedulers/scheduling_ddpm.py'			),     os.path.join(self.diffusers_dir,     'schedulers/scheduling_ddpm.py'			),     )
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    Dict		      =      'src/diffusers'
          shutil.rmtree(self.diffusers_dir			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
          if overwrite_result is not None:
             UpperCamelCase       :    List[Any]		      =      comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
          UpperCamelCase       :    Union[str, Any]		      =      black.Mode(target_versions={black.TargetVersion.PYaa},     line_length=119			)
          UpperCamelCase       :    Any		      =      black.format_str(SCREAMING_SNAKE_CASE_,     mode=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      os.path.join(self.diffusers_dir,     'new_code.py'			)
          with open(SCREAMING_SNAKE_CASE_,     'w',     newline='\n'			) as f:
             f.write(SCREAMING_SNAKE_CASE_			)
          if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE_			)			) == 0			)
          else:
             check_copies.is_copy_consistent(f.name,     overwrite=SCREAMING_SNAKE_CASE_			)
             with open(SCREAMING_SNAKE_CASE_,     'r'			) as f:
                self.assertTrue(f.read(),     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Any		      =      check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'			)
          self.assertEqual(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self			)	->						Union[str, Any]:
          # Base copy consistency
          self.check_copy_consistency(
              '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput',     'DDPMSchedulerOutput',     REFERENCE_CODE + '\n',     )

          # With no empty line at the end
          self.check_copy_consistency(
              '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput',     'DDPMSchedulerOutput',     SCREAMING_SNAKE_CASE_,     )

          # Copy consistency with rename
          self.check_copy_consistency(
              '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test',     'TestSchedulerOutput',     re.sub('DDPM',     'Test',     SCREAMING_SNAKE_CASE_			),     )

          # Copy consistency with a really long name
          UpperCamelCase       :    Any		      =      'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
          self.check_copy_consistency(
              F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""",     F"""{long_class_name}SchedulerOutput""",     re.sub('Bert',     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			),     )

          # Copy consistency with overwrite
          self.check_copy_consistency(
              '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test',     'TestSchedulerOutput',     SCREAMING_SNAKE_CASE_,     overwrite_result=re.sub('DDPM',     'Test',     SCREAMING_SNAKE_CASE_			),     )








from collections import Counter

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

lowercase_													=      datasets.load_iris()

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

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =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]))




