
"""simple docstring"""



import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Optional[Any]=False		)     ->					Any:
  try:
    _snake_case					     =       os.environ[key]
  except KeyError:
    # KEY isn't set, default to `default`.
    _snake_case					     =       default
  else:
    # KEY is set, convert it to True or False.
    try:
      _snake_case					     =       strtobool(lowercase__		)
    except ValueError:
      # More values are supported, but let's keep the message simple.
      raise ValueError(f'''If set, {key} must be yes or no.'''		)
  return _value


UpperCAmelCase__          = parse_flag_from_env('RUN_SLOW', default=False)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Any:
  return unittest.skip('''Test was skipped'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					Optional[int]:
  return unittest.skipUnless(_run_slow_tests ,					'''test is slow'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Tuple:
  return unittest.skipUnless(not torch.cuda.is_available() ,					'''test requires only a CPU'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any]		)     ->					Dict:
  return unittest.skipUnless(torch.cuda.is_available() ,					'''test requires a GPU'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  return unittest.skipUnless(is_xpu_available() ,					'''test requires a XPU'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str]		)     ->					Tuple:
  return unittest.skipUnless(is_mps_available() ,					'''test requires a `mps` backend support in `torch`'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					str:
  return unittest.skipUnless(
      is_transformers_available() and is_datasets_available() ,					'''test requires the Hugging Face suite'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					Tuple:
  return unittest.skipUnless(is_bnb_available() ,					'''test requires the bitsandbytes library'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any]		)     ->					Dict:
  return unittest.skipUnless(is_tpu_available() ,					'''test requires TPU'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int		)     ->					Optional[Any]:
  return unittest.skipUnless(torch.cuda.device_count() == 1 ,					'''test requires a GPU'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int		)     ->					Any:
  return unittest.skipUnless(torch.xpu.device_count() == 1 ,					'''test requires a XPU'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str]		)     ->					str:
  return unittest.skipUnless(torch.cuda.device_count() > 1 ,					'''test requires multiple GPUs'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str]		)     ->					Any:
  return unittest.skipUnless(torch.xpu.device_count() > 1 ,					'''test requires multiple XPUs'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int		)     ->					Union[str, Any]:
  return unittest.skipUnless(is_safetensors_available() ,					'''test requires safetensors'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  return unittest.skipUnless(is_deepspeed_available() ,					'''test requires DeepSpeed'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any]		)     ->					Optional[Any]:
  return unittest.skipUnless(is_torch_version('''>=''' ,					'''1.12.0'''		) ,					'''test requires torch version >= 1.12.0'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]=None ,					__lowerCamelCase   :       Tuple=None		)     ->					List[Any]:
  if test_case is None:
    return partial(lowercase__ ,					version=lowercase__		)
  return unittest.skipUnless(is_torch_version('''>=''' ,					lowercase__		) ,					f'''test requires torch version >= {version}'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Union[str, Any]:
  return unittest.skipUnless(is_tensorboard_available() ,					'''test requires Tensorboard'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Optional[int]:
  return unittest.skipUnless(is_wandb_available() ,					'''test requires wandb'''		)(lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any]		)     ->					Tuple:
  return unittest.skipUnless(is_comet_ml_available() ,					'''test requires comet_ml'''		)(lowercase__		)


UpperCAmelCase__          = (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any		)     ->					Optional[Any]:
  return unittest.skipUnless(
      _atleast_one_tracker_available ,					'''test requires at least one tracker to be available and for `comet_ml` to not be installed''' ,					)(lowercase__		)

class       lowerCAmelCase__				(						unittest.TestCase	):
     __a						=					True







     @classmethod
     def      lowercase	(							cls			:			Optional[Any]			):
       _snake_case					     =       tempfile.mkdtemp()







     @classmethod
     def      lowercase	(							cls			:			List[Any]			):
       if os.path.exists(cls.tmpdir			):
         shutil.rmtree(cls.tmpdir			)







     def      lowercase	(							self			:			List[Any]			):
       if self.clear_on_setup:
         for path in Path(self.tmpdir			).glob('''**/*'''			):
           if path.is_file():
             path.unlink()
           elif path.is_dir():
             shutil.rmtree(lowerCAmelCase__			)






class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			List[str]			):
       super().tearDown()
       # Reset the state of the AcceleratorState singleton.
       AcceleratorState._reset_state()
       PartialState._reset_state()






class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			Union[mock.Mock, List[mock.Mock]]			):
       _snake_case					     =       mocks if isinstance(lowerCAmelCase__    ,      (tuple, list)			) else [mocks]
       for m in self.mocks:
         m.start()
         self.addCleanup(m.stop			)






def 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					List[str]:
  _snake_case					     =       AcceleratorState()
  _snake_case					     =       tensor[None].clone().to(state.device		)
  _snake_case					     =       gather(lowercase__		).cpu()
  _snake_case					     =       tensor[0].cpu()
  for i in range(tensors.shape[0]		):
    if not torch.equal(tensors[i] ,					lowercase__		):
      return False
  return True

class       lowerCAmelCase__				:







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			str			):
       _snake_case					     =       returncode
       _snake_case					     =       stdout
       _snake_case					     =       stderr






async def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Any		)     ->					Any:
  while True:
    _snake_case					     =       await stream.readline()
    if line:
      callback(lowercase__		)
    else:
      break
async def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Union[str, Any]=None ,					__lowerCamelCase   :       Tuple=None ,					__lowerCamelCase   :       str=None ,					__lowerCamelCase   :       Optional[Any]=False ,					__lowerCamelCase   :       Union[str, Any]=False		)     ->					str:
  if echo:
    print('''\nRunning: ''' ,					''' '''.join(lowercase__		)		)

  _snake_case					     =       await asyncio.create_subprocess_exec(
      cmd[0] ,					*cmd[1:] ,					stdin=lowercase__ ,					stdout=asyncio.subprocess.PIPE ,					stderr=asyncio.subprocess.PIPE ,					env=lowercase__ ,					)

  # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
  # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
  #
  # If it starts hanging, will need to switch to the following code. The problem is that no data
  # will be seen until it's done and if it hangs for example there will be no debug info.
  # out, err = await p.communicate()
  # return _RunOutput(p.returncode, out, err)

  _snake_case					     =       []
  _snake_case					     =       []

  def tee(__lowerCamelCase   :       int ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Union[str, Any]=""		):
    _snake_case					     =       line.decode('''utf-8'''		).rstrip()
    sink.append(lowercase__		)
    if not quiet:
      print(lowercase__ ,					lowercase__ ,					file=lowercase__		)

    # XXX: the timeout doesn't seem to make any difference here
  await asyncio.wait(
      [
          asyncio.create_task(_read_stream(p.stdout ,					lambda __lowerCamelCase		: tee(lowercase__ ,					lowercase__ ,					sys.stdout ,					label='''stdout:'''		)		)		),
          asyncio.create_task(_read_stream(p.stderr ,					lambda __lowerCamelCase		: tee(lowercase__ ,					lowercase__ ,					sys.stderr ,					label='''stderr:'''		)		)		),
      ] ,					timeout=lowercase__ ,					)
  return _RunOutput(await p.wait() ,					lowercase__ ,					lowercase__		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       List[str]=None ,					__lowerCamelCase   :       Optional[Any]=None ,					__lowerCamelCase   :       Optional[Any]=1_80 ,					__lowerCamelCase   :       Dict=False ,					__lowerCamelCase   :       Optional[int]=True		)     ->					int:
  _snake_case					     =       asyncio.get_event_loop()
  _snake_case					     =       loop.run_until_complete(
      _stream_subprocess(lowercase__ ,					env=lowercase__ ,					stdin=lowercase__ ,					timeout=lowercase__ ,					quiet=lowercase__ ,					echo=lowercase__		)		)

  _snake_case					     =       ''' '''.join(lowercase__		)
  if result.returncode > 0:
    _snake_case					     =       '''\n'''.join(result.stderr		)
    raise RuntimeError(
        f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
        f'''The combined stderr from workers follows:\n{stderr}'''		)

  return result

class       lowerCAmelCase__				(						A__	):
     pass






def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[Any]=False		)     ->					Any:
  try:
    _snake_case					     =       subprocess.check_output(lowercase__ ,					stderr=subprocess.STDOUT		)
    if return_stdout:
      if hasattr(lowercase__ ,					'''decode'''		):
        _snake_case					     =       output.decode('''utf-8'''		)
      return output
  except subprocess.CalledProcessError as e:
    raise SubprocessCallException(
        f'''Command `{' '.join(lowercase__		)}` failed with the following error:\n\n{e.output.decode()}'''		) from e







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]))
