





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       A__(		__lowerCAmelCase	,			__lowerCAmelCase=False ):
				try:
								_snake_case :			int  						=   os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								_snake_case :			str  						=   default
				else:
								# KEY is set, convert it to True or False.
								try:
												_snake_case :			Optional[int]  						=   strtobool(__lowerCAmelCase )
								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


lowercase_		:				str				=						parse_flag_from_env('''RUN_SLOW''', default=False)

def       A__(		__lowerCAmelCase ):
				return unittest.skip('Test was skipped' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(_run_slow_tests	,			'test is slow' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(not torch.cuda.is_available()	,			'test requires only a CPU' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(torch.cuda.is_available()	,			'test requires a GPU' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_xpu_available()	,			'test requires a XPU' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_mps_available()	,			'test requires a `mps` backend support in `torch`' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(
				    is_transformers_available() and is_datasets_available()	,			'test requires the Hugging Face suite' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_bnb_available()	,			'test requires the bitsandbytes library' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_tpu_available()	,			'test requires TPU' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(torch.cuda.device_count() == 1	,			'test requires a GPU' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(torch.xpu.device_count() == 1	,			'test requires a XPU' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(torch.cuda.device_count() > 1	,			'test requires multiple GPUs' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(torch.xpu.device_count() > 1	,			'test requires multiple XPUs' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_safetensors_available()	,			'test requires safetensors' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_deepspeed_available()	,			'test requires DeepSpeed' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_torch_version('>='	,			'1.12.0' )	,			'test requires torch version >= 1.12.0' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase=None	,			__lowerCAmelCase=None ):
				if test_case is None:
								return partial(__lowerCAmelCase	,			version=__lowerCAmelCase )
				return unittest.skipUnless(is_torch_version('>='	,			__lowerCAmelCase )	,			F'''test requires torch version >= {version}''' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_tensorboard_available()	,			'test requires Tensorboard' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_wandb_available()	,			'test requires wandb' )(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(is_comet_ml_available()	,			'test requires comet_ml' )(__lowerCAmelCase )


lowercase_		:				Optional[Any]				=						(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)

def       A__(		__lowerCAmelCase ):
				return unittest.skipUnless(
				    _atleast_one_tracker_available	,			'test requires at least one tracker to be available and for `comet_ml` to not be installed'	,			)(__lowerCAmelCase )


class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[Any]								=	True







						@classmethod
						def 		__UpperCAmelCase       (    cls			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			str  						=   tempfile.mkdtemp()







						@classmethod
						def 		__UpperCAmelCase       (    cls			:		int					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    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(lowerCamelCase_					)



class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







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



class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		Union[mock.Mock, List[mock.Mock]]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   mocks if isinstance(lowerCamelCase_ ,				(tuple, list)					) else [mocks]
										for m in self.mocks:
														m.start()
														self.addCleanup(m.stop					)



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


class 			lowercase				:



						"""simple docstring"""

						def __init__(    self			:		str ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   returncode
										_snake_case :			Optional[Any]  						=   stdout
										_snake_case :			str  						=   stderr



async def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				while True:
								_snake_case :			Optional[Any]  						=   await stream.readline()
								if line:
												callback(__lowerCAmelCase )
								else:
												break

async def       A__(		__lowerCAmelCase	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=False	,			__lowerCAmelCase=False ):
				if echo:
								print('\nRunning: '	,			' '.join(__lowerCAmelCase ) )

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

				# 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 :			List[str]  						=   []
				_snake_case :			Optional[int]  						=   []

				def tee(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase="" ):
								_snake_case :			Tuple  						=   line.decode('utf-8' ).rstrip()
								sink.append(__lowerCAmelCase )
								if not quiet:
												print(__lowerCAmelCase	,			__lowerCAmelCase	,			file=__lowerCAmelCase )

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

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=1_80	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True ):
				_snake_case :			Optional[int]  						=   asyncio.get_event_loop()
				_snake_case :			Tuple  						=   loop.run_until_complete(
				    _stream_subprocess(__lowerCAmelCase	,			env=__lowerCAmelCase	,			stdin=__lowerCAmelCase	,			timeout=__lowerCAmelCase	,			quiet=__lowerCAmelCase	,			echo=__lowerCAmelCase ) )

				_snake_case :			Optional[int]  						=   ' '.join(__lowerCAmelCase )
				if result.returncode > 0:
								_snake_case :			Optional[int]  						=   '\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 			lowercase				(							a_     ):



						"""simple docstring"""

						pass



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













from math import factorial

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if successes > trials:
								raise ValueError('successes must be lower or equal to trials' )
				if trials < 0 or successes < 0:
								raise ValueError('the function is defined for non-negative integers' )
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise ValueError('the function is defined for non-negative integers' )
				if not 0 < prob < 1:
								raise ValueError('prob has to be in range of 1 - 0' )
				_snake_case :			Optional[int]  						=   (prob**successes) * ((1 - prob) ** (trials - successes))
				# Calculate the binomial coefficient: n! / k!(n-k)!
				_snake_case :			List[Any]  						=   float(factorial(__lowerCAmelCase ) )
				coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
				return probability * coefficient


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print('''Probability of 2 successes out of 4 trails''')
		print('''with probability of 0.75 is:''', end=''' ''')
		print(binomial_distribution(2, 4, 0.75))






