
'''simple docstring'''


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__lowercase							:   Any					=     logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




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

					if "token" in model_name_or_path:
										return "rag_token"
					if "sequence" in model_name_or_path:
										return "rag_sequence"
					if "bart" in model_name_or_path:
										return "bart"
					return None




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

					return max(metric_fn(_lowercase			,       _lowercase				) for gt in ground_truths				)




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

					lowerCamelCase_					:      str     =		[line.strip() for line in open(_lowercase			,       '''r'''				).readlines()]
					lowerCamelCase_					:      Tuple     =		[]

					if args.gold_data_mode == "qa":
										lowerCamelCase_					:      int     =		pd.read_csv(_lowercase			,       sep='''\t'''			,       header=_lowercase				)
										for answer_list in data[1]:
															lowerCamelCase_					:      Union[str, Any]     =		ast.literal_eval(_lowercase				)
															answers.append(_lowercase				)
					else:
										lowerCamelCase_					:      Optional[int]     =		[line.strip() for line in open(_lowercase			,       '''r'''				).readlines()]
										lowerCamelCase_					:      Any     =		[[reference] for reference in references]

					lowerCamelCase_					:      List[str]     =		0
					for prediction, ground_truths in zip(_lowercase			,       _lowercase				):
										total += 1
										em += metric_max_over_ground_truths(_lowercase			,       _lowercase			,       _lowercase				)
										fa += metric_max_over_ground_truths(_lowercase			,       _lowercase			,       _lowercase				)

					lowerCamelCase_					:      Dict     =		1_00.0 * em / total
					lowerCamelCase_					:      Dict     =		1_00.0 * fa / total

					logger.info(F"""F1: {fa:.2f}"""				)
					logger.info(F"""EM: {em:.2f}"""				)




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

					lowerCamelCase_					:      List[Any]     =		args.k
					lowerCamelCase_					:      Any     =		[line.strip() for line in open(_lowercase			,       '''r'''				).readlines()]
					lowerCamelCase_					:      int     =		[line.strip() for line in open(_lowercase			,       '''r'''				).readlines()]

					lowerCamelCase_					:      List[str]     =		0
					for hypo, reference in zip(_lowercase			,       _lowercase				):
										lowerCamelCase_					:      List[Any]     =		set(hypo.split('''\t'''				)[:k]				)
										lowerCamelCase_					:      Optional[int]     =		set(reference.split('''\t'''				)				)
										total += 1
										em += len(hypo_provenance & ref_provenance				) / k

					lowerCamelCase_					:      int     =		1_00.0 * em / total
					logger.info(F"""Precision@{k}: {em: .2f}"""				)




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

					def strip_title(_lowercase				):
										if title.startswith('''"'''				):
															lowerCamelCase_					:      int     =		title[1:]
										if title.endswith('''"'''				):
															lowerCamelCase_					:      int     =		title[:-1]
										return title

					lowerCamelCase_					:      Optional[int]     =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    _lowercase			,       return_tensors='''pt'''			,       padding=_lowercase			,       truncation=_lowercase			,       )['''input_ids'''].to(args.device				)

					lowerCamelCase_					:      Optional[Any]     =		rag_model.rag.question_encoder(_lowercase				)
					lowerCamelCase_					:      Dict     =		question_enc_outputs[0]

					lowerCamelCase_					:      str     =		rag_model.retriever(
					    _lowercase			,       question_enc_pool_output.cpu().detach().to(torch.floataa				).numpy()			,       prefix=rag_model.rag.generator.config.prefix			,       n_docs=rag_model.config.n_docs			,       return_tensors='''pt'''			,       )
					lowerCamelCase_					:      Optional[int]     =		rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
					lowerCamelCase_					:      Dict     =		[]
					for docs in all_docs:
										lowerCamelCase_					:      List[str]     =		[strip_title(_lowercase				) for title in docs['''title''']]
										provenance_strings.append('''\t'''.join(_lowercase				)				)
					return provenance_strings




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

					with torch.no_grad():
										lowerCamelCase_					:      Optional[Any]     =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    _lowercase			,       return_tensors='''pt'''			,       padding=_lowercase			,       truncation=_lowercase				)

										lowerCamelCase_					:      Union[str, Any]     =		inputs_dict.input_ids.to(args.device				)
										lowerCamelCase_					:      int     =		inputs_dict.attention_mask.to(args.device				)
										lowerCamelCase_					:      Optional[int]     =		rag_model.generate(  # rag_model overwrites generate
										    _lowercase			,       attention_mask=_lowercase			,       num_beams=args.num_beams			,       min_length=args.min_length			,       max_length=args.max_length			,       early_stopping=_lowercase			,       num_return_sequences=1			,       bad_words_ids=[[0, 0]]			,       )
										lowerCamelCase_					:      int     =		rag_model.retriever.generator_tokenizer.batch_decode(_lowercase			,       skip_special_tokens=_lowercase				)

										if args.print_predictions:
															for q, a in zip(_lowercase			,       _lowercase				):
																				logger.info('''Q: {} - A: {}'''.format(_lowercase			,       _lowercase				)				)

										return answers




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

					lowerCamelCase_					:      Dict     =		argparse.ArgumentParser()
					parser.add_argument(
					    '''--model_type'''			,       choices=['''rag_sequence''', '''rag_token''', '''bart''']			,       type=_lowercase			,       help=(
					        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
					        ''' model_name_or_path'''
					    )			,       )
					parser.add_argument(
					    '''--index_name'''			,       default=_lowercase			,       choices=['''exact''', '''compressed''', '''legacy''']			,       type=_lowercase			,       help='''RAG model retriever type'''			,       )
					parser.add_argument(
					    '''--index_path'''			,       default=_lowercase			,       type=_lowercase			,       help='''Path to the retrieval index'''			,       )
					parser.add_argument('''--n_docs'''			,       default=5			,       type=_lowercase			,       help='''Number of retrieved docs'''				)
					parser.add_argument(
					    '''--model_name_or_path'''			,       default=_lowercase			,       type=_lowercase			,       required=_lowercase			,       help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''			,       )
					parser.add_argument(
					    '''--eval_mode'''			,       choices=['''e2e''', '''retrieval''']			,       default='''e2e'''			,       type=_lowercase			,       help=(
					        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
					        ''' precision@k.'''
					    )			,       )
					parser.add_argument('''--k'''			,       default=1			,       type=_lowercase			,       help='''k for the precision@k calculation'''				)
					parser.add_argument(
					    '''--evaluation_set'''			,       default=_lowercase			,       type=_lowercase			,       required=_lowercase			,       help='''Path to a file containing evaluation samples'''			,       )
					parser.add_argument(
					    '''--gold_data_path'''			,       default=_lowercase			,       type=_lowercase			,       required=_lowercase			,       help='''Path to a tab-separated file with gold samples'''			,       )
					parser.add_argument(
					    '''--gold_data_mode'''			,       default='''qa'''			,       type=_lowercase			,       choices=['''qa''', '''ans''']			,       help=(
					        '''Format of the gold data file'''
					        '''qa - a single line in the following format: question [tab] answer_list'''
					        '''ans - a single line of the gold file contains the expected answer string'''
					    )			,       )
					parser.add_argument(
					    '''--predictions_path'''			,       type=_lowercase			,       default='''predictions.txt'''			,       help='''Name of the predictions file, to be stored in the checkpoints directory'''			,       )
					parser.add_argument(
					    '''--eval_all_checkpoints'''			,       action='''store_true'''			,       help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''			,       )
					parser.add_argument(
					    '''--eval_batch_size'''			,       default=8			,       type=_lowercase			,       help='''Batch size per GPU/CPU for evaluation.'''			,       )
					parser.add_argument(
					    '''--recalculate'''			,       help='''Recalculate predictions even if the prediction file exists'''			,       action='''store_true'''			,       )
					parser.add_argument(
					    '''--num_beams'''			,       default=4			,       type=_lowercase			,       help='''Number of beams to be used when generating answers'''			,       )
					parser.add_argument('''--min_length'''			,       default=1			,       type=_lowercase			,       help='''Min length of the generated answers'''				)
					parser.add_argument('''--max_length'''			,       default=50			,       type=_lowercase			,       help='''Max length of the generated answers'''				)

					parser.add_argument(
					    '''--print_predictions'''			,       action='''store_true'''			,       help='''If True, prints predictions while evaluating.'''			,       )
					parser.add_argument(
					    '''--print_docs'''			,       action='''store_true'''			,       help='''If True, prints docs retried while generating.'''			,       )
					lowerCamelCase_					:      Dict     =		parser.parse_args()
					lowerCamelCase_					:      Optional[Any]     =		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)
					return args




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

					lowerCamelCase_					:      str     =		{}
					if args.model_type is None:
										lowerCamelCase_					:      List[Any]     =		infer_model_type(args.model_name_or_path				)
										assert args.model_type is not None
					if args.model_type.startswith('''rag'''				):
										lowerCamelCase_					:      Any     =		RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
										lowerCamelCase_					:      List[Any]     =		args.n_docs
										if args.index_name is not None:
															lowerCamelCase_					:      List[Any]     =		args.index_name
										if args.index_path is not None:
															lowerCamelCase_					:      Optional[Any]     =		args.index_path
					else:
										lowerCamelCase_					:      List[str]     =		BartForConditionalGeneration

					lowerCamelCase_					:      Dict     =		(
					    [f.path for f in os.scandir(args.model_name_or_path				) if f.is_dir()]
					    if args.eval_all_checkpoints
					    else [args.model_name_or_path]
					)

					logger.info('''Evaluate the following checkpoints: %s'''			,       _lowercase				)

					lowerCamelCase_					:      Optional[int]     =		get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
					lowerCamelCase_					:      Optional[Any]     =		evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

					for checkpoint in checkpoints:
										if os.path.exists(args.predictions_path				) and (not args.recalculate):
															logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path				)				)
															score_fn(_lowercase			,       args.predictions_path			,       args.gold_data_path				)
															continue

										logger.info('''***** Running evaluation for {} *****'''.format(_lowercase				)				)
										logger.info('''  Batch size = %d'''			,       args.eval_batch_size				)
										logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path				)				)

										if args.model_type.startswith('''rag'''				):
															lowerCamelCase_					:      Optional[int]     =		RagRetriever.from_pretrained(_lowercase			,       **_lowercase				)
															lowerCamelCase_					:      str     =		model_class.from_pretrained(_lowercase			,       retriever=_lowercase			,       **_lowercase				)
															model.retriever.init_retrieval()
										else:
															lowerCamelCase_					:      List[str]     =		model_class.from_pretrained(_lowercase			,       **_lowercase				)
										model.to(args.device				)

										with open(args.evaluation_set			,       '''r'''				) as eval_file, open(args.predictions_path			,       '''w'''				) as preds_file:
															lowerCamelCase_					:      List[str]     =		[]
															for line in tqdm(_lowercase				):
																				questions.append(line.strip()				)
																				if len(_lowercase				) == args.eval_batch_size:
																									lowerCamelCase_					:      Tuple     =		evaluate_batch_fn(_lowercase			,       _lowercase			,       _lowercase				)
																									preds_file.write('''\n'''.join(_lowercase				) + '''\n'''				)
																									preds_file.flush()
																									lowerCamelCase_					:      Optional[int]     =		[]
															if len(_lowercase				) > 0:
																				lowerCamelCase_					:      Optional[int]     =		evaluate_batch_fn(_lowercase			,       _lowercase			,       _lowercase				)
																				preds_file.write('''\n'''.join(_lowercase				)				)
																				preds_file.flush()

															score_fn(_lowercase			,       args.predictions_path			,       args.gold_data_path				)


if __name__ == "__main__":
					__lowercase							:   Dict					=     get_args()
					main(args)




'''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


