



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


__UpperCAmelCase	       =	logging.getLogger(__name__)

__UpperCAmelCase	       =	'''pytorch_model.bin'''



@dataclasses.dataclass
class     lowerCAmelCase_		:
							UpperCAmelCase__    : str       					=  dataclasses.field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}							)
							UpperCAmelCase__    : Optional[str]       					=  dataclasses.field(
							    default=a__	,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}	,   )






@dataclasses.dataclass
class     lowerCAmelCase_		:
							UpperCAmelCase__    : str       					=  dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}							)
							UpperCAmelCase__    : str       					=  dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}							)
							UpperCAmelCase__    : Optional[str]       					=  dataclasses.field(
							    default=a__	,   metadata={"help": "A csv or a json file containing the validation data."}							)
							UpperCAmelCase__    : Optional[str]       					=  dataclasses.field(
							    default=a__	,   metadata={"help": "The name of the task to train on."}	,   )
							UpperCAmelCase__    : Optional[List[str]]       					=  dataclasses.field(
							    default=a__	,   metadata={"help": "The list of labels for the task."}							)






@dataclasses.dataclass
class     lowerCAmelCase_		:
							UpperCAmelCase__    : str       					=  dataclasses.field(
							    metadata={"help": "The output directory where the model predictions and checkpoints will be written."}							)
							UpperCAmelCase__    : Optional[str]       					=  dataclasses.field(
							    default="accuracy"	,   metadata={"help": "The evaluation metric used for the task."}							)
							UpperCAmelCase__    : Optional[str]       					=  dataclasses.field(
							    default="no"	,   metadata={
							        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
							    }	,   )
							UpperCAmelCase__    : Optional[int]       					=  dataclasses.field(
							    default=10	,   metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}	,   )
							UpperCAmelCase__    : Optional[float]       					=  dataclasses.field(
							    default=0.0	,   metadata={
							        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
							    }	,   )
							UpperCAmelCase__    : Optional[bool]       					=  dataclasses.field(
							    default=a__	,   metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}	,   )
							UpperCAmelCase__    : Optional[bool]       					=  dataclasses.field(
							    default=a__	,   metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}	,   )
							UpperCAmelCase__    : Optional[bool]       					=  dataclasses.field(
							    default=a__	,   metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}	,   )
							UpperCAmelCase__    : Optional[float]       					=  dataclasses.field(
							    default=0.0	,   metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}	,   )
							UpperCAmelCase__    : Optional[int]       					=  dataclasses.field(
							    default=100	,   metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}	,   )
							UpperCAmelCase__    : Optional[int]       					=  dataclasses.field(
							    default=a__	,   metadata={"help": "Random seed for initialization."}	,   )






def 		UpperCamelCase     (       snake_case__	:   Optional[int]	,					snake_case__	:   Dict	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   int	,					snake_case__	:   Tuple	,					snake_case__	:   Dict     )     ->					Optional[Any]:
			UpperCamelCase   :  int						     =     datasets.concatenate_datasets([infer_input, infer_output]	,					axis=1     )

			if args.do_filter_by_confidence:
						UpperCamelCase   :  Optional[int]						     =     dataset.filter(lambda snake_case__     : example["probability"] > args.confidence_threshold     )

			if args.do_filter_by_val_performance:
						assert eval_result >= 0.0 and eval_result <= 1.0
						UpperCamelCase   :  List[str]						     =     int(eval_result * len(snake_case__     )     )
						print(snake_case__     )
						UpperCamelCase   :  Union[str, Any]						     =     dataset.sort('probability'	,					reverse=snake_case__     )
						UpperCamelCase   :  int						     =     dataset.select(range(snake_case__     )     )

			UpperCamelCase   :  Any						     =     dataset.remove_columns(['label', 'probability']     )
			UpperCamelCase   :  int						     =     dataset.rename_column('prediction'	,					'label'     )
			UpperCamelCase   :  Any						     =     dataset.map(lambda snake_case__     : {"label": idalabel[example["label"]]}     )
			UpperCamelCase   :  List[Any]						     =     dataset.shuffle(seed=args.seed     )

			UpperCamelCase   :  Dict						     =     os.path.join(snake_case__	,					F"""train_pseudo.{args.data_file_extension}"""     )
			if args.data_file_extension == "csv":
						dataset.to_csv(snake_case__	,					index=snake_case__     )
			else:
						dataset.to_json(snake_case__     )


def 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   List[Any]	,					snake_case__	:   List[str]	,					snake_case__	:   Tuple	,					**snake_case__	:   Union[str, Any]     )     ->					Any:
			UpperCamelCase   :  List[str]						     =     Accelerator()
			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	,					datefmt='%m/%d/%Y %H:%M:%S'	,					level=logging.INFO	,					)
			logger.info(accelerator.state     )

			# Setup logging, we only want one process per machine to log things on the
			# screen. accelerator.is_local_main_process is only True for one process per
			# machine.
			logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR     )

			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_info()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()

			UpperCamelCase   :  List[str]						     =     STModelArguments(model_name_or_path=snake_case__     )
			UpperCamelCase   :  int						     =     STDataArguments(train_file=snake_case__	,					infer_file=snake_case__     )
			UpperCamelCase   :  Dict						     =     STTrainingArguments(output_dir=snake_case__     )
			UpperCamelCase   :  Optional[Any]						     =     argparse.Namespace()

			for arg_class in (model_args, data_args, training_args):
						for key, value in vars(snake_case__     ).items():
									setattr(snake_case__	,					snake_case__	,					snake_case__     )

			for key, value in kwargs.items():
						if hasattr(snake_case__	,					snake_case__     ):
									setattr(snake_case__	,					snake_case__	,					snake_case__     )

    # Sanity checks
			UpperCamelCase   :  int						     =     {}
			UpperCamelCase   :  Optional[int]						     =     None

			# You need to provide the training data and the data to predict on
			assert args.train_file is not None
			assert args.infer_file is not None
			UpperCamelCase   :  int						     =     args.train_file
			UpperCamelCase   :  Optional[Any]						     =     args.infer_file

			if args.evaluation_strategy != IntervalStrategy.NO.value:
						assert args.eval_file is not None
						UpperCamelCase   :  int						     =     args.eval_file

			for key in data_files:
						UpperCamelCase   :  List[str]						     =     data_files[key].split('.'     )[-1]
						assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
						if args.data_file_extension is None:
									UpperCamelCase   :  List[Any]						     =     extension
						else:
									assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""

			assert (
			    args.eval_metric in datasets.list_metrics()
			), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

			# If passed along, set the training seed now.
			if args.seed is not None:
						set_seed(args.seed     )

			logger.info('Creating the initial data directory for self-training...'     )
			UpperCamelCase   :  Dict						     =     F"""{args.output_dir}/self-train_iter-{{}}""".format
			UpperCamelCase   :  Any						     =     data_dir_format(0     )

			if accelerator.is_main_process:
						if args.output_dir is not None:
									os.makedirs(args.output_dir	,					exist_ok=snake_case__     )
									os.makedirs(snake_case__	,					exist_ok=snake_case__     )
			accelerator.wait_for_everyone()

			UpperCamelCase   :  List[Any]						     =     None
			UpperCamelCase   :  Any						     =     None
			UpperCamelCase   :  Tuple						     =     0
			UpperCamelCase   :  str						     =     False
			# Show the progress bar
			UpperCamelCase   :  List[str]						     =     tqdm(range(args.max_selftrain_iterations     )	,					disable=not accelerator.is_local_main_process     )

			# Self-train
			for iteration in range(0	,					int(args.max_selftrain_iterations     )     ):
						UpperCamelCase   :  List[str]						     =     data_dir_format(snake_case__     )
						assert os.path.exists(snake_case__     )

						# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
						# iteration > 0
						UpperCamelCase   :  List[Any]						     =     os.path.join(snake_case__	,					'stage-1'     )
						UpperCamelCase   :  Optional[int]						     =     {
						    'accelerator': accelerator,
						    'model_name_or_path': args.model_name_or_path,
						    'cache_dir': args.cache_dir,
						    'do_train': True,
						    'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
						    'do_eval': True if args.eval_file is not None else False,
						    'eval_file': data_files['eval'],
						    'do_predict': True,
						    'infer_file': data_files['infer'],
						    'task_name': args.task_name,
						    'label_list': args.label_list,
						    'output_dir': current_output_dir,
						    'eval_metric': args.eval_metric,
						    'evaluation_strategy': args.evaluation_strategy,
						    'early_stopping_patience': args.early_stopping_patience,
						    'early_stopping_threshold': args.early_stopping_threshold,
						    'seed': args.seed,
						}
						# Add additional training arguments
						for key, value in kwargs.items():
									if key not in arguments_dict and not hasattr(snake_case__	,					snake_case__     ):
												arguments_dict.update({key: value}     )

						UpperCamelCase   :  Any						     =     os.path.join(snake_case__	,					'best-checkpoint'	,					snake_case__     )
						if os.path.exists(snake_case__     ):
									logger.info(
									    'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'	,					snake_case__	,					snake_case__	,					)
						else:
									logger.info('***** Running self-training: iteration: %d, stage: 1 *****'	,					snake_case__     )
									finetune(**snake_case__     )
									accelerator.wait_for_everyone()
									assert os.path.exists(snake_case__     )
									logger.info('Self-training job completed: iteration: %d, stage: 1.'	,					snake_case__     )

						if iteration > 0 and args.finetune_on_labeled_data:
									# Stage 2 (optional): fine-tuning on the original labeled data
									UpperCamelCase   :  Tuple						     =     os.path.join(snake_case__	,					'best-checkpoint'     )
									UpperCamelCase   :  str						     =     os.path.join(snake_case__	,					'stage-2'     )
									# Update arguments_dict
									UpperCamelCase   :  str						     =     model_path
									UpperCamelCase   :  Tuple						     =     data_files['train']
									UpperCamelCase   :  int						     =     current_output_dir

									UpperCamelCase   :  Dict						     =     os.path.join(snake_case__	,					'best-checkpoint'	,					snake_case__     )
									if os.path.exists(snake_case__     ):
												logger.info(
												    'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'	,					snake_case__	,					snake_case__	,					)
									else:
												logger.info('***** Running self-training: iteration: %d, stage: 2 *****'	,					snake_case__     )
												finetune(**snake_case__     )
												accelerator.wait_for_everyone()
												assert os.path.exists(snake_case__     )
												logger.info('Self-training job completed: iteration: %d, stage: 2.'	,					snake_case__     )

						UpperCamelCase   :  int						     =     iteration
						UpperCamelCase   :  Any						     =     data_dir_format(iteration + 1     )

						UpperCamelCase   :  Optional[int]						     =     AutoConfig.from_pretrained(os.path.join(snake_case__	,					'best-checkpoint'     )     )
						UpperCamelCase   :  Union[str, Any]						     =     config.idalabel
						UpperCamelCase   :  str						     =     os.path.join(snake_case__	,					'eval_results_best-checkpoint.json'     )
						UpperCamelCase   :  List[Any]						     =     os.path.join(snake_case__	,					'test_results_best-checkpoint.json'     )
						assert os.path.exists(snake_case__     )

						with open(snake_case__	,					'r'     ) as f:
									UpperCamelCase   :  Tuple						     =     float(json.load(snake_case__     )[args.eval_metric]     )
						UpperCamelCase   :  Union[str, Any]						     =     os.path.join(snake_case__	,					'infer_output_best-checkpoint.csv'     )
						assert os.path.exists(snake_case__     )
						# Loading the dataset from local csv or json files.
						UpperCamelCase   :  Tuple						     =     load_dataset(args.data_file_extension	,					data_files={'data': data_files['infer']}     )['data']
						UpperCamelCase   :  Optional[Any]						     =     load_dataset('csv'	,					data_files={'data': infer_output_file}     )['data']

						if accelerator.is_main_process:
									os.makedirs(snake_case__	,					exist_ok=snake_case__     )
									shutil.copy(snake_case__	,					os.path.join(snake_case__	,					F"""eval_results_iter-{iteration}.json"""     )     )
									if os.path.exists(snake_case__     ):
												shutil.copy(snake_case__	,					os.path.join(snake_case__	,					F"""test_results_iter-{iteration}.json"""     )     )
									create_pseudo_labeled_data(snake_case__	,					snake_case__	,					snake_case__	,					snake_case__	,					snake_case__	,					snake_case__     )
						accelerator.wait_for_everyone()

						UpperCamelCase   :  Tuple						     =     os.path.join(snake_case__	,					F"""train_pseudo.{args.data_file_extension}"""     )

						if args.evaluation_strategy != IntervalStrategy.NO.value:
									UpperCamelCase   :  Optional[Any]						     =     eval_result

									if best_iteration is None:
												UpperCamelCase   :  Dict						     =     new_iteration
												UpperCamelCase   :  List[str]						     =     new_eval_result
									else:
												if new_eval_result - best_eval_result > args.early_stopping_threshold:
															UpperCamelCase   :  Tuple						     =     new_iteration
															UpperCamelCase   :  List[Any]						     =     new_eval_result
															UpperCamelCase   :  str						     =     0
												else:
															if new_eval_result == best_eval_result:
																		UpperCamelCase   :  Any						     =     new_iteration
																		UpperCamelCase   :  Tuple						     =     new_eval_result
															early_stopping_patience_counter += 1

												if early_stopping_patience_counter >= args.early_stopping_patience:
															UpperCamelCase   :  Tuple						     =     True

						progress_bar.update(1     )

						if should_training_stop:
									break

			if best_iteration is not None:
						# Save the best iteration
						logger.info('Best iteration: %d'	,					snake_case__     )
						logger.info('Best evaluation result: %s = %f'	,					args.eval_metric	,					snake_case__     )
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(snake_case__	,					F"""eval_results_iter-{iteration}.json"""     )	,					os.path.join(snake_case__	,					'eval_results_best-iteration.json'     )	,					)
			else:
						# Assume that the last iteration is the best
						logger.info('Best iteration: %d'	,					args.max_selftrain_iterations - 1     )
						logger.info('Best evaluation result: %s = %f'	,					args.eval_metric	,					snake_case__     )
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(snake_case__	,					F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""     )	,					os.path.join(snake_case__	,					'eval_results_best-iteration.json'     )	,					)








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 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[str]=False     )     ->					Optional[Any]:
   try:
      UpperCamelCase   :  List[Any]						     =     os.environ[key]
   except KeyError:
      # KEY isn't set, default to `default`.
      UpperCamelCase   :  List[Any]						     =     default
   else:
      # KEY is set, convert it to True or False.
      try:
         UpperCamelCase   :  Optional[Any]						     =     strtobool(snake_case__     )
      except ValueError:
         # More values are supported, but let's keep the message simple.
         raise ValueError(F"""If set, {key} must be yes or no."""     )
   return _value


__UpperCAmelCase	       =	parse_flag_from_env('''RUN_SLOW''', default=False)
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					str:
   return unittest.skip('Test was skipped'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Optional[Any]:
   return unittest.skipUnless(_run_slow_tests	,					'test is slow'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   return unittest.skipUnless(not torch.cuda.is_available()	,					'test requires only a CPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   return unittest.skipUnless(torch.cuda.is_available()	,					'test requires a GPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					List[Any]:
   return unittest.skipUnless(is_xpu_available()	,					'test requires a XPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return unittest.skipUnless(is_mps_available()	,					'test requires a `mps` backend support in `torch`'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[str]     )     ->					Tuple:
   return unittest.skipUnless(
       is_transformers_available() and is_datasets_available()	,					'test requires the Hugging Face suite'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return unittest.skipUnless(is_bnb_available()	,					'test requires the bitsandbytes library'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					List[str]:
   return unittest.skipUnless(is_tpu_available()	,					'test requires TPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Optional[int]:
   return unittest.skipUnless(torch.cuda.device_count() == 1	,					'test requires a GPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Dict:
   return unittest.skipUnless(torch.xpu.device_count() == 1	,					'test requires a XPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					Any:
   return unittest.skipUnless(torch.cuda.device_count() > 1	,					'test requires multiple GPUs'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					int:
   return unittest.skipUnless(torch.xpu.device_count() > 1	,					'test requires multiple XPUs'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Any:
   return unittest.skipUnless(is_safetensors_available()	,					'test requires safetensors'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Dict:
   return unittest.skipUnless(is_deepspeed_available()	,					'test requires DeepSpeed'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					Optional[Any]:
   return unittest.skipUnless(is_torch_version('>='	,					'1.12.0'     )	,					'test requires torch version >= 1.12.0'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict=None	,					snake_case__	:   Union[str, Any]=None     )     ->					Optional[Any]:
   if test_case is None:
      return partial(snake_case__	,					version=snake_case__     )
   return unittest.skipUnless(is_torch_version('>='	,					snake_case__     )	,					F"""test requires torch version >= {version}"""     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					Optional[Any]:
   return unittest.skipUnless(is_tensorboard_available()	,					'test requires Tensorboard'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   str     )     ->					Tuple:
   return unittest.skipUnless(is_wandb_available()	,					'test requires wandb'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Any     )     ->					List[Any]:
   return unittest.skipUnless(is_comet_ml_available()	,					'test requires comet_ml'     )(snake_case__     )


__UpperCAmelCase	       =	(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					Optional[Any]:
   return unittest.skipUnless(
       _atleast_one_tracker_available	,					'test requires at least one tracker to be available and for `comet_ml` to not be installed'	,					)(snake_case__     )



class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  True
       @classmethod
       def  snake_case_	(      cls	)					->		Any:
          UpperCamelCase   :  Optional[Any]						     =     tempfile.mkdtemp()
       @classmethod
       def  snake_case_	(      cls	)					->		Tuple:
          if os.path.exists(cls.tmpdir	):
             shutil.rmtree(cls.tmpdir	)






       def  snake_case_	(      self	)					->		str:
          if self.clear_on_setup:
             for path in Path(self.tmpdir	).glob('**/*'	):
                if path.is_file():
                   path.unlink()
                elif path.is_dir():
                   shutil.rmtree(SCREAMING_SNAKE_CASE_	)






class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		Optional[int]:
          super().tearDown()
          # Reset the state of the AcceleratorState singleton.
          AcceleratorState._reset_state()
          PartialState._reset_state()






class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Optional[Any]						     =     mocks if isinstance(SCREAMING_SNAKE_CASE_,      (tuple, list)	) else [mocks]
          for m in self.mocks:
             m.start()
             self.addCleanup(m.stop	)






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



class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		int:
          UpperCamelCase   :  List[Any]						     =     returncode
          UpperCamelCase   :  Tuple						     =     stdout
          UpperCamelCase   :  Any						     =     stderr






async def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[int]     )     ->					Union[str, Any]:
   while True:
      UpperCamelCase   :  List[str]						     =     await stream.readline()
      if line:
         callback(snake_case__     )
      else:
         break
async def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   int=None	,					snake_case__	:   Dict=None	,					snake_case__	:   Any=None	,					snake_case__	:   Optional[Any]=False	,					snake_case__	:   Union[str, Any]=False     )     ->					_RunOutput:
   if echo:
      print('\nRunning: '	,					' '.join(snake_case__     )     )

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

   # 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)

   UpperCamelCase   :  Tuple						     =     []
   UpperCamelCase   :  int						     =     []

   def tee(snake_case__	:   List[Any]	,					snake_case__	:   Optional[int]	,					snake_case__	:   Optional[int]	,					snake_case__	:   List[Any]=""     ):
      UpperCamelCase   :  Union[str, Any]						     =     line.decode('utf-8'     ).rstrip()
      sink.append(snake_case__     )
      if not quiet:
         print(snake_case__	,					snake_case__	,					file=snake_case__     )

    # XXX: the timeout doesn't seem to make any difference here
   await asyncio.wait(
       [
           asyncio.create_task(_read_stream(p.stdout	,					lambda snake_case__     : tee(snake_case__	,					snake_case__	,					sys.stdout	,					label='stdout:'     )     )     ),
           asyncio.create_task(_read_stream(p.stderr	,					lambda snake_case__     : tee(snake_case__	,					snake_case__	,					sys.stderr	,					label='stderr:'     )     )     ),
       ]	,					timeout=snake_case__	,					)
   return _RunOutput(await p.wait()	,					snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Any=None	,					snake_case__	:   Tuple=None	,					snake_case__	:   Any=180	,					snake_case__	:   Any=False	,					snake_case__	:   Optional[int]=True     )     ->					_RunOutput:
   UpperCamelCase   :  int						     =     asyncio.get_event_loop()
   UpperCamelCase   :  Tuple						     =     loop.run_until_complete(
       _stream_subprocess(snake_case__	,					env=snake_case__	,					stdin=snake_case__	,					timeout=snake_case__	,					quiet=snake_case__	,					echo=snake_case__     )     )

   UpperCamelCase   :  str						     =     ' '.join(snake_case__     )
   if result.returncode > 0:
      UpperCamelCase   :  Union[str, Any]						     =     '\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     lowerCAmelCase_		(  a__							):
       pass






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



