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

import black


SCREAMING_SNAKE_CASE							:str										=    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.
SCREAMING_SNAKE_CASE							:Dict										=    '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			int			):
					__A             =			tempfile.mkdtemp()
					os.makedirs(os.path.join(self.diffusers_dir							,"schedulers/"			)			)
					__A             =			self.diffusers_dir
					shutil.copy(
					    os.path.join(_a							,"src/diffusers/schedulers/scheduling_ddpm.py"			)							,os.path.join(self.diffusers_dir							,"schedulers/scheduling_ddpm.py"			)							,)






	def     UpperCamelCase_  (	self :			str			):
					__A             =			"src/diffusers"
					shutil.rmtree(self.diffusers_dir			)






	def     UpperCamelCase_  (	self :			List[str]							,A :			Optional[Any]							,A :			int							,A :			Optional[int]							,A :			str=None			):
					__A             =			comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
					if overwrite_result is not None:
									__A             =			comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
					__A             =			black.Mode(target_versions={black.TargetVersion.PYaa}							,line_length=1_19			)
					__A             =			black.format_str(_a							,mode=_a			)
					__A             =			os.path.join(self.diffusers_dir							,"new_code.py"			)
					with open(_a							,"w"							,newline="\n"			) as f:
									f.write(_a			)
					if overwrite_result is None:
									self.assertTrue(len(check_copies.is_copy_consistent(_a			)			) == 0			)
					else:
									check_copies.is_copy_consistent(f.name							,overwrite=_a			)
									with open(_a							,"r"			) as f:
													self.assertTrue(f.read()							,_a			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"			)
					self.assertEqual(_a							,_a			)







	def     UpperCamelCase_  (	self :			str			):
					# 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"							,_a							,)

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

					# Copy consistency with a really long name
					__A             =			"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"							,_a							,_a			)							,)

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


from collections import Counter

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

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



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