
'''simple docstring'''


import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


__lowercase							:   Dict					=     logging.getLogger(__name__)




def lowercase_	(   _lowercase=2			,       _lowercase=3			,       _lowercase=16			,       _lowercase = 10			,       _lowercase = 2				)     ->							Any:
					'''simple docstring'''

					def get_dataset(_lowercase				):
										lowerCamelCase_					:      Optional[Any]     =		torch.randn(batch_size * n_batches			,       1				)
										return TensorDataset(_lowercase			,       a * x + b + 0.1 * torch.randn(batch_size * n_batches			,       1				)				)

					lowerCamelCase_					:      List[str]     =		get_dataset(_lowercase				)
					lowerCamelCase_					:      str     =		get_dataset(_lowercase				)
					lowerCamelCase_					:      List[Any]     =		DataLoader(_lowercase			,       shuffle=_lowercase			,       batch_size=_lowercase			,       num_workers=4				)
					lowerCamelCase_					:      Any     =		DataLoader(_lowercase			,       shuffle=_lowercase			,       batch_size=_lowercase			,       num_workers=4				)
					return (train_dataloader, valid_dataloader)




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=None				)     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		[]
					for epoch in range(_lowercase				):
										# Train quickly
										model.train()
										for batch in dataloader:
															lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		batch
															lowerCamelCase_					:      Tuple     =		model(_lowercase				)
															lowerCamelCase_					:      str     =		torch.nn.functional.mse_loss(_lowercase			,       _lowercase				)
															accelerator.backward(_lowercase				)
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()				)  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands

class 	__lowercase				(  nn.Module    ):

			def __init__(self      ):
								super().__init__()
								lowerCamelCase_					:      Union[str, Any]     =		nn.Parameter(torch.randn(1      )      )
								lowerCamelCase_					:      Optional[int]     =		nn.Parameter(torch.randn(1      )      )






			def        UpperCAmelCase__ (self			,   A      ):
								return x * self.a + self.b

class 	__lowercase				(  unittest.TestCase    ):

			def        UpperCAmelCase__ (self      ):
								with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2      )
													lowerCamelCase_					:      List[Any]     =		DummyModel()
													lowerCamelCase_					:      List[str]     =		torch.optim.Adam(params=model.parameters()			,   lr=1E-3      )
													lowerCamelCase_,      lowerCamelCase_					:      int     =		dummy_dataloaders()
													lowerCamelCase_					:      Dict     =		ProjectConfiguration(total_limit=1			,   project_dir=A			,   automatic_checkpoint_naming=A      )
													# Train baseline
													lowerCamelCase_					:      str     =		Accelerator(project_config=A      )
													lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		accelerator.prepare(
													    A			,   A			,   A			,   A      )
													# Save initial
													accelerator.save_state()

													# Save second state
													accelerator.save_state()
													self.assertEqual(len(os.listdir(accelerator.project_dir      )      )			,   1      )

			def        UpperCAmelCase__ (self      ):
								with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2      )
													lowerCamelCase_					:      Tuple     =		DummyModel()
													lowerCamelCase_					:      List[str]     =		torch.optim.Adam(params=model.parameters()			,   lr=1E-3      )
													lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		dummy_dataloaders()
													# Train baseline
													lowerCamelCase_					:      str     =		Accelerator()
													lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		accelerator.prepare(
													    A			,   A			,   A			,   A      )
													# Save initial
													lowerCamelCase_					:      Dict     =		os.path.join(A			,   '''initial'''      )
													accelerator.save_state(A      )
													((lowerCamelCase_),      (lowerCamelCase_))					:      Optional[Any]     =		model.a.item(), model.b.item()
													lowerCamelCase_					:      List[str]     =		optimizer.state_dict()
													lowerCamelCase_					:      Optional[int]     =		train(3			,   A			,   A			,   A			,   A      )
													((lowerCamelCase_),      (lowerCamelCase_))					:      List[Any]     =		model.a.item(), model.b.item()
													lowerCamelCase_					:      List[str]     =		optimizer.state_dict()

													# Train partially
													set_seed(4_2      )
													lowerCamelCase_					:      Any     =		DummyModel()
													lowerCamelCase_					:      List[str]     =		torch.optim.Adam(params=model.parameters()			,   lr=1E-3      )
													lowerCamelCase_,      lowerCamelCase_					:      str     =		dummy_dataloaders()
													lowerCamelCase_					:      Tuple     =		Accelerator()
													lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		accelerator.prepare(
													    A			,   A			,   A			,   A      )
													accelerator.load_state(A      )
													((lowerCamelCase_),      (lowerCamelCase_))					:      List[str]     =		model.a.item(), model.b.item()
													lowerCamelCase_					:      Optional[Any]     =		optimizer.state_dict()
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )

													lowerCamelCase_					:      Optional[int]     =		train(2			,   A			,   A			,   A			,   A      )
													# Save everything
													lowerCamelCase_					:      Dict     =		os.path.join(A			,   '''checkpoint'''      )
													accelerator.save_state(A      )

													# Load everything back in and make sure all states work
													accelerator.load_state(A      )
													test_rands += train(1			,   A			,   A			,   A			,   A      )
													((lowerCamelCase_),      (lowerCamelCase_))					:      Optional[Any]     =		model.a.item(), model.b.item()
													lowerCamelCase_					:      Tuple     =		optimizer.state_dict()
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )

			def        UpperCAmelCase__ (self      ):
								with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2      )
													lowerCamelCase_					:      Any     =		DummyModel()
													lowerCamelCase_					:      List[Any]     =		torch.optim.Adam(params=model.parameters()			,   lr=1E-3      )
													lowerCamelCase_,      lowerCamelCase_					:      Optional[Any]     =		dummy_dataloaders()
													lowerCamelCase_					:      Dict     =		ProjectConfiguration(automatic_checkpoint_naming=A      )

													# Train baseline
													lowerCamelCase_					:      Any     =		Accelerator(project_dir=A			,   project_config=A      )
													lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      int     =		accelerator.prepare(
													    A			,   A			,   A			,   A      )
													# Save initial
													accelerator.save_state()
													((lowerCamelCase_),      (lowerCamelCase_))					:      int     =		model.a.item(), model.b.item()
													lowerCamelCase_					:      Optional[int]     =		optimizer.state_dict()
													lowerCamelCase_					:      Optional[int]     =		train(3			,   A			,   A			,   A			,   A      )
													((lowerCamelCase_),      (lowerCamelCase_))					:      Tuple     =		model.a.item(), model.b.item()
													lowerCamelCase_					:      Optional[int]     =		optimizer.state_dict()

													# Train partially
													set_seed(4_2      )
													lowerCamelCase_					:      List[str]     =		DummyModel()
													lowerCamelCase_					:      int     =		torch.optim.Adam(params=model.parameters()			,   lr=1E-3      )
													lowerCamelCase_,      lowerCamelCase_					:      int     =		dummy_dataloaders()
													lowerCamelCase_					:      List[str]     =		ProjectConfiguration(iteration=1			,   automatic_checkpoint_naming=A      )
													lowerCamelCase_					:      str     =		Accelerator(project_dir=A			,   project_config=A      )
													lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		accelerator.prepare(
													    A			,   A			,   A			,   A      )
													accelerator.load_state(os.path.join(A			,   '''checkpoints'''			,   '''checkpoint_0'''      )      )
													((lowerCamelCase_),      (lowerCamelCase_))					:      Optional[int]     =		model.a.item(), model.b.item()
													lowerCamelCase_					:      str     =		optimizer.state_dict()
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )

													lowerCamelCase_					:      List[str]     =		train(2			,   A			,   A			,   A			,   A      )
													# Save everything
													accelerator.save_state()

													# Load everything back in and make sure all states work
													accelerator.load_state(os.path.join(A			,   '''checkpoints'''			,   '''checkpoint_1'''      )      )
													test_rands += train(1			,   A			,   A			,   A			,   A      )
													((lowerCamelCase_),      (lowerCamelCase_))					:      str     =		model.a.item(), model.b.item()
													lowerCamelCase_					:      Tuple     =		optimizer.state_dict()
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )
													self.assertEqual(A			,   A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		torch.tensor([1, 2, 3]      )
								lowerCamelCase_					:      str     =		torch.tensor([2, 3, 4]      )
								lowerCamelCase_					:      str     =		DummyModel()
								lowerCamelCase_					:      Optional[Any]     =		torch.optim.Adam(net.parameters()      )
								lowerCamelCase_					:      Any     =		Accelerator()
								with self.assertRaises(A      ) as ve:
													accelerator.register_for_checkpointing(A			,   A			,   A			,   A      )
								lowerCamelCase_					:      List[str]     =		str(ve.exception      )
								self.assertTrue('''Item at index 0''' in message      )
								self.assertTrue('''Item at index 1''' in message      )
								self.assertFalse('''Item at index 2''' in message      )
								self.assertFalse('''Item at index 3''' in message      )

			def        UpperCAmelCase__ (self      ):
								with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2      )
													lowerCamelCase_					:      Union[str, Any]     =		DummyModel()
													lowerCamelCase_					:      str     =		torch.optim.Adam(params=model.parameters()			,   lr=1E-3      )
													lowerCamelCase_					:      Optional[Any]     =		torch.optim.lr_scheduler.StepLR(A			,   step_size=1			,   gamma=0.99      )
													lowerCamelCase_,      lowerCamelCase_					:      Any     =		dummy_dataloaders()
													lowerCamelCase_					:      List[Any]     =		ProjectConfiguration(automatic_checkpoint_naming=A      )
													# Train baseline
													lowerCamelCase_					:      Dict     =		Accelerator(project_dir=A			,   project_config=A      )
													lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		accelerator.prepare(
													    A			,   A			,   A			,   A			,   A      )
													# Save initial
													accelerator.save_state()
													lowerCamelCase_					:      Union[str, Any]     =		scheduler.state_dict()
													train(3			,   A			,   A			,   A			,   A			,   A      )
													self.assertNotEqual(A			,   scheduler.state_dict()      )

													# Load everything back in and make sure all states work
													accelerator.load_state(os.path.join(A			,   '''checkpoints'''			,   '''checkpoint_0'''      )      )
													self.assertEqual(A			,   scheduler.state_dict()      )

			def        UpperCAmelCase__ (self      ):
								with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(4_2      )
													lowerCamelCase_					:      Tuple     =		DummyModel()
													lowerCamelCase_					:      Optional[Any]     =		ProjectConfiguration(automatic_checkpoint_naming=A			,   total_limit=2      )
													# Train baseline
													lowerCamelCase_					:      Optional[Any]     =		Accelerator(project_dir=A			,   project_config=A      )
													lowerCamelCase_					:      Optional[Any]     =		accelerator.prepare(A      )
													# Save 3 states:
													for _ in range(1_1      ):
																		accelerator.save_state()
													self.assertTrue(not os.path.exists(os.path.join(A			,   '''checkpoints'''			,   '''checkpoint_0'''      )      )      )
													self.assertTrue(os.path.exists(os.path.join(A			,   '''checkpoints'''			,   '''checkpoint_9'''      )      )      )
													self.assertTrue(os.path.exists(os.path.join(A			,   '''checkpoints'''			,   '''checkpoint_10'''      )      )      )






			@require_cuda
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
								execute_subprocess_async(A			,   env=os.environ.copy()      )


if __name__ == "__main__":
					__lowercase							:   Optional[int]					=     '''/tmp/accelerate/state_checkpointing'''
					__lowercase							:   Dict					=     DummyModel()
					__lowercase							:   str					=     torch.optim.Adam(params=model.parameters(), lr=1E-3)
					__lowercase							:   int					=     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
					__lowercase				,		__lowercase							:   Optional[int]					=     dummy_dataloaders()
					__lowercase							:   List[Any]					=     ProjectConfiguration(automatic_checkpoint_naming=True)
					# Train baseline
					__lowercase							:   int					=     Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
					if accelerator.process_index == 0:
										if os.path.exists(savedir):
															shutil.rmtree(savedir)
										os.makedirs(savedir)
					__lowercase				,		__lowercase				,		__lowercase				,		__lowercase				,		__lowercase							:   Dict					=     accelerator.prepare(
					    model, optimizer, train_dataloader, valid_dataloader, scheduler
					)
					__lowercase				,		__lowercase							:   str					=     accelerator.prepare(model, optimizer)
					train(3, model, train_dataloader, optimizer, accelerator, scheduler)
					# Check that the intial optimizer is loaded on the GPU
					for group in optimizer.param_groups:
										__lowercase							:   Optional[Any]					=     group['''params'''][0].device
										break
					assert param_device.type == accelerator.device.type
					__lowercase							:   Optional[Any]					=     model.cpu()
					accelerator.wait_for_everyone()
					accelerator.save_state()
					accelerator.wait_for_everyone()

					# Check CPU state
					accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
					for group in optimizer.param_groups:
										__lowercase							:   Tuple					=     group['''params'''][0].device
										break
					assert (
					    param_device.type == torch.device('''cpu''').type
					), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

					# Check device state
					model.to(accelerator.device)
					accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
					for group in optimizer.param_groups:
										__lowercase							:   List[Any]					=     group['''params'''][0].device
										break
					assert (
					    param_device.type == accelerator.device.type
					), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

					# Check error
					with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
										accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
					accelerator.wait_for_everyone()
					if accelerator.process_index == 0:
										shutil.rmtree(savedir)
					accelerator.wait_for_everyone()




'''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 lowercase_	(   _lowercase			,       _lowercase=False				)     ->							int:
					'''simple docstring'''

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


__lowercase							:   Union[str, Any]					=     parse_flag_from_env('''RUN_SLOW''', default=False)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skip('''Test was skipped'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					return unittest.skipUnless(_run_slow_tests			,       '''test is slow'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

					return unittest.skipUnless(torch.cuda.is_available()			,       '''test requires a GPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

					return unittest.skipUnless(is_xpu_available()			,       '''test requires a XPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

					return unittest.skipUnless(is_bnb_available()			,       '''test requires the bitsandbytes library'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skipUnless(is_tpu_available()			,       '''test requires TPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

					return unittest.skipUnless(is_safetensors_available()			,       '''test requires safetensors'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							int:
					'''simple docstring'''

					return unittest.skipUnless(is_deepspeed_available()			,       '''test requires DeepSpeed'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skipUnless(is_torch_version('''>='''			,       '''1.12.0'''				)			,       '''test requires torch version >= 1.12.0'''				)(_lowercase				)




def lowercase_	(   _lowercase=None			,       _lowercase=None				)     ->							Any:
					'''simple docstring'''

					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 lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

					return unittest.skipUnless(is_tensorboard_available()			,       '''test requires Tensorboard'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skipUnless(is_wandb_available()			,       '''test requires wandb'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							int:
					'''simple docstring'''

					return unittest.skipUnless(is_comet_ml_available()			,       '''test requires comet_ml'''				)(_lowercase				)


__lowercase							:   str					=     (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''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'''			,       )(_lowercase				)

class 	__lowercase				(  unittest.TestCase    ):
			lowerCamelCase			:		Optional[Any]	      =			True

			@classmethod
			def        UpperCAmelCase__ (cls      ):
								lowerCamelCase_					:      Optional[Any]     =		tempfile.mkdtemp()

			@classmethod
			def        UpperCAmelCase__ (cls      ):
								if os.path.exists(cls.tmpdir      ):
													shutil.rmtree(cls.tmpdir      )






			def        UpperCAmelCase__ (self      ):
								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 	__lowercase				(  unittest.TestCase    ):

			def        UpperCAmelCase__ (self      ):
								super().tearDown()
								# Reset the state of the AcceleratorState singleton.
								AcceleratorState._reset_state()
								PartialState._reset_state()

class 	__lowercase				(  unittest.TestCase    ):

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      List[str]     =		mocks if isinstance(A			,   (tuple, list)      ) else [mocks]
								for m in self.mocks:
													m.start()
													self.addCleanup(m.stop      )




def lowercase_	(   _lowercase				)     ->							Optional[int]:
					'''simple docstring'''

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

class 	__lowercase				:

			def __init__(self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      List[Any]     =		returncode
								lowerCamelCase_					:      Optional[int]     =		stdout
								lowerCamelCase_					:      List[str]     =		stderr




async def lowercase_	(   _lowercase			,       _lowercase				)     ->							Tuple:
					'''simple docstring'''

					while True:
										lowerCamelCase_					:      Any     =		await stream.readline()
										if line:
															callback(_lowercase				)
										else:
															break




async def lowercase_	(   _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=False			,       _lowercase=False				)     ->							_RunOutput:
					'''simple docstring'''

					if echo:
										print('''\nRunning: '''			,       ''' '''.join(_lowercase				)				)

					lowerCamelCase_					:      Any     =		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)

					lowerCamelCase_					:      List[Any]     =		[]
					lowerCamelCase_					:      Optional[int]     =		[]

					def tee(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase=""				):
										lowerCamelCase_					:      Union[str, Any]     =		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 _lowercase				: tee(_lowercase			,       _lowercase			,       sys.stdout			,       label='''stdout:'''				)				)				),
					        asyncio.create_task(_read_stream(p.stderr			,       lambda _lowercase				: tee(_lowercase			,       _lowercase			,       sys.stderr			,       label='''stderr:'''				)				)				),
					    ]			,       timeout=_lowercase			,       )
					return _RunOutput(await p.wait()			,       _lowercase			,       _lowercase				)




def lowercase_	(   _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=180			,       _lowercase=False			,       _lowercase=True				)     ->							_RunOutput:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		asyncio.get_event_loop()
					lowerCamelCase_					:      List[str]     =		loop.run_until_complete(
					    _stream_subprocess(_lowercase			,       env=_lowercase			,       stdin=_lowercase			,       timeout=_lowercase			,       quiet=_lowercase			,       echo=_lowercase				)				)

					lowerCamelCase_					:      List[Any]     =		''' '''.join(_lowercase				)
					if result.returncode > 0:
										lowerCamelCase_					:      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				(  _lowercase    ):
			pass




def lowercase_	(   _lowercase			,       _lowercase=False				)     ->							Dict:
					'''simple docstring'''

					try:
										lowerCamelCase_					:      List[str]     =		subprocess.check_output(_lowercase			,       stderr=subprocess.STDOUT				)
										if return_stdout:
															if hasattr(_lowercase			,       '''decode'''				):
																				lowerCamelCase_					:      str     =		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


