
'''simple docstring'''


from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
					from transformers import (
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelForTableQuestionAnswering,
					    TFAutoModelForTokenClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFFunnelBaseModel,
					    TFFunnelModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					    TFTapasForQuestionAnswering,
					)
					from transformers.models.auto.modeling_tf_auto import (
					    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_MASKED_LM_MAPPING,
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    TF_MODEL_MAPPING,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		str	      =			"new-model"


if is_tf_available():

					class 	__lowercase				(  _lowercase    ):
								lowerCamelCase			:		Optional[int]	      =			NewModelConfig

@require_tf
class 	__lowercase				(  unittest.TestCase    ):

			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		'''bert-base-cased'''
								lowerCamelCase_					:      str     =		AutoConfig.from_pretrained(A      )
								self.assertIsNotNone(A      )
								self.assertIsInstance(A			,   A      )

								lowerCamelCase_					:      Any     =		TFAutoModel.from_pretrained(A      )
								self.assertIsNotNone(A      )
								self.assertIsInstance(A			,   A      )

			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		'''bert-base-cased'''
								lowerCamelCase_					:      int     =		AutoConfig.from_pretrained(A      )
								self.assertIsNotNone(A      )
								self.assertIsInstance(A			,   A      )

								lowerCamelCase_					:      Any     =		TFAutoModelForPreTraining.from_pretrained(A      )
								self.assertIsNotNone(A      )
								self.assertIsInstance(A			,   A      )

			@slow
			def        UpperCAmelCase__ (self      ):
								for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase_					:      Any     =		AutoConfig.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

													lowerCamelCase_					:      Dict     =		TFAutoModelForCausalLM.from_pretrained(A      )
													lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		TFAutoModelForCausalLM.from_pretrained(A			,   output_loading_info=A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

			@slow
			def        UpperCAmelCase__ (self      ):
								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase_					:      List[Any]     =		AutoConfig.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

													lowerCamelCase_					:      Dict     =		TFAutoModelWithLMHead.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

			@slow
			def        UpperCAmelCase__ (self      ):
								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase_					:      Union[str, Any]     =		AutoConfig.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

													lowerCamelCase_					:      List[Any]     =		TFAutoModelForMaskedLM.from_pretrained(A      )
													lowerCamelCase_,      lowerCamelCase_					:      Any     =		TFAutoModelForMaskedLM.from_pretrained(A			,   output_loading_info=A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

			@slow
			def        UpperCAmelCase__ (self      ):
								for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase_					:      Optional[int]     =		AutoConfig.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

													lowerCamelCase_					:      Tuple     =		TFAutoModelForSeqaSeqLM.from_pretrained(A      )
													lowerCamelCase_,      lowerCamelCase_					:      int     =		TFAutoModelForSeqaSeqLM.from_pretrained(A			,   output_loading_info=A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

			@slow
			def        UpperCAmelCase__ (self      ):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
													lowerCamelCase_					:      List[str]     =		AutoConfig.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

													lowerCamelCase_					:      Optional[int]     =		TFAutoModelForSequenceClassification.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

			@slow
			def        UpperCAmelCase__ (self      ):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
													lowerCamelCase_					:      Union[str, Any]     =		AutoConfig.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

													lowerCamelCase_					:      Optional[int]     =		TFAutoModelForQuestionAnswering.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

			@slow
			@require_tensorflow_probability
			def        UpperCAmelCase__ (self      ):
								for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
													lowerCamelCase_					:      Tuple     =		AutoConfig.from_pretrained(A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

													lowerCamelCase_					:      Union[str, Any]     =		TFAutoModelForTableQuestionAnswering.from_pretrained(A      )
													lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		TFAutoModelForTableQuestionAnswering.from_pretrained(
													    A			,   output_loading_info=A      )
													self.assertIsNotNone(A      )
													self.assertIsInstance(A			,   A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		TFAutoModelWithLMHead.from_pretrained(A      )
								self.assertIsInstance(A			,   A      )
								self.assertEqual(model.num_parameters()			,   1_4_4_1_0      )
								self.assertEqual(model.num_parameters(only_trainable=A      )			,   1_4_4_1_0      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		TFAutoModelWithLMHead.from_pretrained(A      )
								self.assertIsInstance(A			,   A      )
								self.assertEqual(model.num_parameters()			,   1_4_4_1_0      )
								self.assertEqual(model.num_parameters(only_trainable=A      )			,   1_4_4_1_0      )

			def        UpperCAmelCase__ (self      ):
								# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
								lowerCamelCase_					:      Optional[Any]     =		TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''      )
								self.assertIsInstance(A			,   A      )

								lowerCamelCase_					:      List[str]     =		copy.deepcopy(model.config      )
								lowerCamelCase_					:      Dict     =		['''FunnelBaseModel''']
								lowerCamelCase_					:      int     =		TFAutoModel.from_config(A      )
								self.assertIsInstance(A			,   A      )

								with tempfile.TemporaryDirectory() as tmp_dir:
													model.save_pretrained(A      )
													lowerCamelCase_					:      Any     =		TFAutoModel.from_pretrained(A      )
													self.assertIsInstance(A			,   A      )

			def        UpperCAmelCase__ (self      ):
								try:
													AutoConfig.register('''new-model'''			,   A      )

													lowerCamelCase_					:      Union[str, Any]     =		[
													    TFAutoModel,
													    TFAutoModelForCausalLM,
													    TFAutoModelForMaskedLM,
													    TFAutoModelForPreTraining,
													    TFAutoModelForQuestionAnswering,
													    TFAutoModelForSequenceClassification,
													    TFAutoModelForTokenClassification,
													]

													for auto_class in auto_classes:
																		with self.subTest(auto_class.__name__      ):
																							# Wrong config class will raise an error
																							with self.assertRaises(A      ):
																												auto_class.register(A			,   A      )
																							auto_class.register(A			,   A      )
																							# Trying to register something existing in the Transformers library will raise an error
																							with self.assertRaises(A      ):
																												auto_class.register(A			,   A      )

																							# Now that the config is registered, it can be used as any other config with the auto-API
																							lowerCamelCase_					:      Optional[Any]     =		BertModelTester(self      ).get_config()
																							lowerCamelCase_					:      List[Any]     =		NewModelConfig(**tiny_config.to_dict()      )
																							lowerCamelCase_					:      str     =		auto_class.from_config(A      )
																							self.assertIsInstance(A			,   A      )

																							with tempfile.TemporaryDirectory() as tmp_dir:
																												model.save_pretrained(A      )
																												lowerCamelCase_					:      str     =		auto_class.from_pretrained(A      )
																												self.assertIsInstance(A			,   A      )

								finally:
													if "new-model" in CONFIG_MAPPING._extra_content:
																		del CONFIG_MAPPING._extra_content["new-model"]
													for mapping in (
													    TF_MODEL_MAPPING,
													    TF_MODEL_FOR_PRETRAINING_MAPPING,
													    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
													    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
													    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
													    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
													    TF_MODEL_FOR_MASKED_LM_MAPPING,
													):
																		if NewModelConfig in mapping._extra_content:
																							del mapping._extra_content[NewModelConfig]

			def        UpperCAmelCase__ (self      ):
								with self.assertRaisesRegex(
								    A			,   '''bert-base is not a local folder and is not a valid model identifier'''      ):
													lowerCamelCase_					:      Any     =		TFAutoModel.from_pretrained('''bert-base'''      )

			def        UpperCAmelCase__ (self      ):
								with self.assertRaisesRegex(
								    A			,   R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
													lowerCamelCase_					:      List[Any]     =		TFAutoModel.from_pretrained(A			,   revision='''aaaaaa'''      )

			def        UpperCAmelCase__ (self      ):
								with self.assertRaisesRegex(
								    A			,   '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''			,   ):
													lowerCamelCase_					:      Union[str, Any]     =		TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''      )

			def        UpperCAmelCase__ (self      ):
								with self.assertRaisesRegex(A			,   '''Use `from_pt=True` to load this model'''      ):
													lowerCamelCase_					:      Union[str, Any]     =		TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''      )






			def        UpperCAmelCase__ (self      ):
								# Make sure we have cached the model.
								lowerCamelCase_					:      str     =		TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
								with RequestCounter() as counter:
													lowerCamelCase_					:      List[str]     =		TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
													self.assertEqual(counter.get_request_count			,   0      )
													self.assertEqual(counter.head_request_count			,   1      )
													self.assertEqual(counter.other_request_count			,   0      )

								# With a sharded checkpoint
								lowerCamelCase_					:      str     =		TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''      )
								with RequestCounter() as counter:
													lowerCamelCase_					:      Any     =		TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''      )
													self.assertEqual(counter.get_request_count			,   0      )
													self.assertEqual(counter.head_request_count			,   1      )
													self.assertEqual(counter.other_request_count			,   0      )




'''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


