






'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=False							):


   """simple docstring"""




   try:
      lowercase_	:    List[str]           =     os.environ[key]
   except KeyError:
      # KEY isn't set, default to `default`.
      lowercase_	:    Union[str, Any]           =     default
   else:
      # KEY is set, convert it to True or False.
      try:
         lowercase_	:    Tuple           =     strtobool(_UpperCamelCase							)
      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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skip("Test was skipped"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(_run_slow_tests						,		"test is slow"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(not torch.cuda.is_available()						,		"test requires only a CPU"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(torch.cuda.is_available()						,		"test requires a GPU"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_xpu_available()						,		"test requires a XPU"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_mps_available()						,		"test requires a `mps` backend support in `torch`"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(
       is_transformers_available() and is_datasets_available()						,		"test requires the Hugging Face suite"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_bnb_available()						,		"test requires the bitsandbytes library"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_tpu_available()						,		"test requires TPU"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(torch.cuda.device_count() == 1						,		"test requires a GPU"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(torch.xpu.device_count() == 1						,		"test requires a XPU"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(torch.cuda.device_count() > 1						,		"test requires multiple GPUs"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(torch.xpu.device_count() > 1						,		"test requires multiple XPUs"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_safetensors_available()						,		"test requires safetensors"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_deepspeed_available()						,		"test requires DeepSpeed"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_torch_version(">="						,		"1.12.0"							)						,		"test requires torch version >= 1.12.0"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase=None						,		_UpperCamelCase=None							):


   """simple docstring"""




   if test_case is None:
      return partial(_UpperCamelCase						,		version=_UpperCamelCase							)
   return unittest.skipUnless(is_torch_version(">="						,		_UpperCamelCase							)						,		F"""test requires torch version >= {version}"""							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_tensorboard_available()						,		"test requires Tensorboard"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_wandb_available()						,		"test requires wandb"							)(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(is_comet_ml_available()						,		"test requires comet_ml"							)(_UpperCamelCase							)


UpperCamelCase__ =			(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return unittest.skipUnless(
       _atleast_one_tracker_available						,		"test requires at least one tracker to be available and for `comet_ml` to not be installed"						,		)(_UpperCamelCase							)


class        _UpperCAmelCase						(		unittest.TestCase			):
     __lowerCamelCase:   Union[str, Any]		       =    True




     @classmethod
     def 				lowerCAmelCase__				(			cls  :       Union[str, Any]			):






        '''simple docstring'''






        lowercase_	:    List[Any]           =     tempfile.mkdtemp()




     @classmethod
     def 				lowerCAmelCase__				(			cls  :       List[str]			):






        '''simple docstring'''






        if os.path.exists(cls.tmpdir			):
           shutil.rmtree(cls.tmpdir			)





     def 				lowerCAmelCase__				(			self  :       Any			):






        '''simple docstring'''






        if self.clear_on_setup:
           for path in Path(self.tmpdir			).glob("**/*"			):
              if path.is_file():
                 path.unlink()
              elif path.is_dir():
                 shutil.rmtree(a			)


class        _UpperCAmelCase						(		unittest.TestCase			):




     def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






        '''simple docstring'''






        super().tearDown()
        # Reset the state of the AcceleratorState singleton.
        AcceleratorState._reset_state()
        PartialState._reset_state()


class        _UpperCAmelCase						(		unittest.TestCase			):




     def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Union[mock.Mock, List[mock.Mock]]			):






        '''simple docstring'''






        lowercase_	:    int           =     mocks if isinstance(a  ,     (tuple, list)			) else [mocks]
        for m in self.mocks:
           m.start()
           self.addCleanup(m.stop			)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[Any]           =     AcceleratorState()
   lowercase_	:    List[Any]           =     tensor[None].clone().to(state.device							)
   lowercase_	:    Any           =     gather(_UpperCamelCase							).cpu()
   lowercase_	:    Union[str, Any]           =     tensor[0].cpu()
   for i in range(tensors.shape[0]							):
      if not torch.equal(tensors[i]						,		_UpperCamelCase							):
         return False
   return True


class        _UpperCAmelCase						:




     def __init__(			self  :       List[Any]  ,     a  :       str  ,     a  :       Any  ,     a  :       List[Any]			):






        '''simple docstring'''






        lowercase_	:    Dict           =     returncode
        lowercase_	:    str           =     stdout
        lowercase_	:    List[Any]           =     stderr




async def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   while True:
      lowercase_	:    Optional[int]           =     await stream.readline()
      if line:
         callback(_UpperCamelCase							)
      else:
         break




async def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=False						,		_UpperCamelCase=False							):


   """simple docstring"""




   if echo:
      print("\nRunning: "						,		" ".join(_UpperCamelCase							)							)

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

   # 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)

   lowercase_	:    Tuple           =     []
   lowercase_	:    Any           =     []

   def tee(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=""							):
      lowercase_	:    List[Any]           =     line.decode("utf-8"							).rstrip()
      sink.append(_UpperCamelCase							)
      if not quiet:
         print(_UpperCamelCase						,		_UpperCamelCase						,		file=_UpperCamelCase							)

    # XXX: the timeout doesn't seem to make any difference here
   await asyncio.wait(
       [
           asyncio.create_task(_read_stream(p.stdout						,		lambda _UpperCamelCase							: tee(_UpperCamelCase						,		_UpperCamelCase						,		sys.stdout						,		label="stdout:"							)							)							),
           asyncio.create_task(_read_stream(p.stderr						,		lambda _UpperCamelCase							: tee(_UpperCamelCase						,		_UpperCamelCase						,		sys.stderr						,		label="stderr:"							)							)							),
       ]						,		timeout=_UpperCamelCase						,		)
   return _RunOutput(await p.wait()						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=180						,		_UpperCamelCase=False						,		_UpperCamelCase=True							):


   """simple docstring"""




   lowercase_	:    Dict           =     asyncio.get_event_loop()
   lowercase_	:    List[str]           =     loop.run_until_complete(
       _stream_subprocess(_UpperCamelCase						,		env=_UpperCamelCase						,		stdin=_UpperCamelCase						,		timeout=_UpperCamelCase						,		quiet=_UpperCamelCase						,		echo=_UpperCamelCase							)							)

   lowercase_	:    Optional[int]           =     " ".join(_UpperCamelCase							)
   if result.returncode > 0:
      lowercase_	:    str           =     "\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        _UpperCAmelCase						(		snake_case			):
     pass




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=False							):


   """simple docstring"""




   try:
      lowercase_	:    List[Any]           =     subprocess.check_output(_UpperCamelCase						,		stderr=subprocess.STDOUT							)
      if return_stdout:
         if hasattr(_UpperCamelCase						,		"decode"							):
            lowercase_	:    List[str]           =     output.decode("utf-8"							)
         return output
   except subprocess.CalledProcessError as e:
      raise SubprocessCallException(
          F"""Command `{" ".join(_UpperCamelCase							)}` failed with the following error:\n\n{e.output.decode()}"""							) from e












'''simple docstring'''
import heapq




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    list[list]           =     []

   # for each node and his adjacency list add them and the rank of the node to queue
   # using heapq module the queue will be filled like a Priority Queue
   # heapq works with a min priority queue, so I used -1*len(v) to build it
   for key, value in graph.items():
      # O(log(n))
      heapq.heappush(_UpperCamelCase						,		[-1 * len(_UpperCamelCase							), (key, value)]							)

   # chosen_vertices = set of chosen vertices
   lowercase_	:    Optional[Any]           =     set()

   # while queue isn't empty and there are still edges
   #   (queue[0][0] is the rank of the node with max rank)
   while queue and queue[0][0] != 0:
      # extract vertex with max rank from queue and add it to chosen_vertices
      lowercase_	:    Any           =     heapq.heappop(_UpperCamelCase							)[1][0]
      chosen_vertices.add(_UpperCamelCase							)

      # Remove all arcs adjacent to argmax
      for elem in queue:
         # if v haven't adjacent node, skip
         if elem[0] == 0:
            continue
         # if argmax is reachable from elem
         # remove argmax from elem's adjacent list and update his rank
         if argmax in elem[1][1]:
            lowercase_	:    str           =     elem[1][1].index(_UpperCamelCase							)
            del elem[1][1][index]
            elem[0] += 1
        # re-order the queue
      heapq.heapify(_UpperCamelCase							)
   return chosen_vertices


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 UpperCamelCase__ =			{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




