



'''simple docstring'''



import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']



			def __init__(     self		:						Tuple    ,			__UpperCamelCase		:						Tuple			)      ->      Any:
							"""simple docstring"""





							assert Path(hparams.data_dir			).exists()
							snake_case__      :   List[str]      =    Path(hparams.output_dir			)
							self.output_dir.mkdir(exist_ok=__UpperCamelCase			)

							snake_case__      :   Optional[Any]      =    self.output_dir.joinpath('''student'''			)

							snake_case__      :   Optional[Any]      =    str(__UpperCamelCase			)  # Tell lightning we are training the student
							snake_case__      :   List[Any]      =    AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher			).eval()
							use_task_specific_params(__UpperCamelCase    ,			hparams.task			)  # We copy good generation parameters to student by default
							if hparams.student is not None:
											snake_case__      :   Optional[Any]      =    AutoModelForSeqaSeqLM.from_pretrained(hparams.student			)
											use_task_specific_params(__UpperCamelCase    ,			hparams.task			)
											snake_case__  ,      snake_case__      :   List[Any]      =    None, None
							else:
											snake_case__  ,      snake_case__  ,      snake_case__      :   List[Any]      =    create_student_by_copying_alternating_layers(
											    __UpperCamelCase    ,			e=hparams.student_encoder_layers    ,			d=hparams.student_decoder_layers    ,			save_path=__UpperCamelCase			)

							if hparams.length_penalty != -1:
											snake_case__      :   str      =    hparams.length_penalty
							snake_case__      :   Optional[Any]      =    hparams.teacher  # Use teacher's tokenizer
							super().__init__(__UpperCamelCase    ,			model=__UpperCamelCase    ,			config=student.config			)
							assert student.config.model_type == teacher.config.model_type, (
							    F'''teacher, student model types should be the same, got {student.config.model_type} !='''
							    F''' {teacher.config.model_type}'''
							)

							if student.config.model_type == "t5":
											snake_case__      :   Optional[Any]      =    len(student.get_encoder().block			)
											snake_case__      :   Union[str, Any]      =    len(student.get_decoder().block			)
											snake_case__      :   List[Any]      =    len(teacher.get_encoder().block			)
											snake_case__      :   Any      =    len(teacher.get_decoder().block			)
							else:
											snake_case__      :   int      =    student.config.encoder_layers
											snake_case__      :   str      =    student.config.decoder_layers
											snake_case__      :   Union[str, Any]      =    teacher.config.encoder_layers
											snake_case__      :   int      =    teacher.config.decoder_layers

							snake_case__      :   Optional[int]      =    not (hparams.student is None or hparams.teacher == hparams.student)
							snake_case__      :   Optional[Any]      =    (not self.different_base_models) and hparams.alpha_hid > 0
							snake_case__      :   Union[str, Any]      =    self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
							# self.different_encoder determines whether we need to run the teacher encoder
							snake_case__      :   Dict      =    teacher
							freeze_params(self.teacher			)

							if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
											try:
															del self.teacher.model.encoder
											except AttributeError:  # T5
															del self.teacher.encoder

							if e_layer_ids is None:
											snake_case__      :   str      =    list(range(__UpperCamelCase			)			)
							if d_layer_ids is None:
											snake_case__      :   int      =    list(range(__UpperCamelCase			)			)

							snake_case__  ,      snake_case__      :   Dict      =    e_layer_ids, d_layer_ids  # type: List[int], List[int]

							if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
											if hparams.supervise_forward:
															snake_case__      :   Any      =    get_layers_to_supervise(
															    n_student=len(self.e_layer_ids			)    ,			n_teacher=__UpperCamelCase			)
															snake_case__      :   Union[str, Any]      =    get_layers_to_supervise(
															    n_student=len(self.d_layer_ids			)    ,			n_teacher=__UpperCamelCase			)
											else:  # student layer should emulate hidden states of the teacher layer it was copied from
															snake_case__      :   Union[str, Any]      =    self.e_layer_ids
															snake_case__      :   Union[str, Any]      =    self.d_layer_ids
							else:
											snake_case__      :   Any      =    None
											snake_case__      :   List[Any]      =    None

							snake_case__      :   List[Any]      =    nn.KLDivLoss(reduction='''batchmean'''			)
							snake_case__      :   Optional[Any]      =    2.0
							snake_case__      :   Dict      =    hparams.alpha_mlm
							snake_case__      :   List[str]      =    hparams.alpha_ce
							snake_case__      :   str      =    hparams.alpha_hid
							gc.collect()
							torch.cuda.empty_cache()



			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      int:
							"""simple docstring"""





							snake_case__      :   Dict      =    mask[:, :, None].expand_as(__UpperCamelCase			)
							snake_case__      :   Tuple      =    s_logits.size(-1			)
							snake_case__      :   List[Any]      =    torch.masked_select(__UpperCamelCase    ,			__UpperCamelCase			)  # (bs * seq_length * voc_size) modulo the 1s in mask
							snake_case__      :   Dict      =    torch.masked_select(__UpperCamelCase    ,			__UpperCamelCase			)  # (bs * seq_length * voc_size) modulo the 1s in mask
							snake_case__      :   Any      =    s_logits_slct.view(-1    ,			__UpperCamelCase			)  # (bs * seq_length, voc_size) modulo the 1s in mask
							snake_case__      :   Any      =    t_logits_slct.view(-1    ,			__UpperCamelCase			)  # (bs * seq_length, voc_size) modulo the 1s in mask
							assert t_logits_slct.size() == s_logits_slct.size()
							snake_case__      :   List[str]      =    (
							    self.ce_loss_fct(
							        nn.functional.log_softmax(s_logits_slct / self.temperature    ,			dim=-1			)    ,			nn.functional.softmax(t_logits_slct / self.temperature    ,			dim=-1			)    ,			)
							    * (self.temperature) ** 2
							)
							return loss_ce



			@staticmethod
			def 				lowerCAmelCase (     __UpperCamelCase		:						str    ,			__UpperCamelCase		:						List[str]			)      ->      Dict:
							"""simple docstring"""





							SummarizationModule.add_model_specific_args(__UpperCamelCase    ,			__UpperCamelCase			)
							add_distill_args(__UpperCamelCase			)
							return parser



			def 				lowerCAmelCase (     self		:						List[Any]    ,			__UpperCamelCase		:						dict			)      ->      tuple:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    self.tokenizer.pad_token_id
							snake_case__  ,      snake_case__  ,      snake_case__      :   List[Any]      =    batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
							if isinstance(self.model    ,			__UpperCamelCase			):
											snake_case__      :   str      =    self.model._shift_right(__UpperCamelCase			)
							else:
											snake_case__      :   Optional[Any]      =    shift_tokens_right(__UpperCamelCase    ,			__UpperCamelCase			)

							# noinspection PyCallingNonCallable
							snake_case__      :   Optional[Any]      =    self(
							    __UpperCamelCase    ,			attention_mask=__UpperCamelCase    ,			decoder_input_ids=__UpperCamelCase    ,			output_hidden_states=self.do_calc_hidden_loss    ,			output_attentions=__UpperCamelCase    ,			use_cache=__UpperCamelCase    ,			)
							snake_case__      :   List[str]      =    student_outputs['''logits''']

							# Same cross entropy vs. label smoothing logic as finetune.py
							assert lm_logits.shape[-1] == self.model.config.vocab_size
							if self.hparams.label_smoothing == 0:
											# Same behavior as modeling_bart.py, besides ignoring pad_token_id
											snake_case__      :   Any      =    nn.CrossEntropyLoss(ignore_index=__UpperCamelCase			)
											snake_case__      :   List[str]      =    loss_fct(lm_logits.view(-1    ,			lm_logits.shape[-1]			)    ,			labels.view(-1			)			)
							else:
											snake_case__      :   Tuple      =    nn.functional.log_softmax(__UpperCamelCase    ,			dim=-1			)
											snake_case__  ,      snake_case__      :   int      =    label_smoothed_nll_loss(
											    __UpperCamelCase    ,			__UpperCamelCase    ,			self.hparams.label_smoothing    ,			ignore_index=__UpperCamelCase			)

							def zero_tensor():
											return torch.tensor(0.0			).type_as(__UpperCamelCase			)

							snake_case__      :   int      =    student_outputs[
							    '''encoder_last_hidden_state'''
							]  # use this unless self.different_base_models
							snake_case__  ,      snake_case__      :   Dict      =    zero_tensor(), zero_tensor()
							if self.different_encoder:  # compute encoder hidden state loss
											snake_case__      :   str      =    self.teacher.get_encoder()(
											    __UpperCamelCase    ,			attention_mask=__UpperCamelCase    ,			output_hidden_states=self.do_calc_hidden_loss    ,			)
											if self.different_base_models:
															snake_case__      :   List[str]      =    all_teacher_encoder_outputs['''last_hidden_state''']
											elif self.do_calc_hidden_loss:
															snake_case__      :   str      =    self.calc_hidden_loss(
															    __UpperCamelCase    ,			student_outputs['''encoder_hidden_states''']    ,			all_teacher_encoder_outputs['''hidden_states''']    ,			self.e_matches    ,			normalize_hidden=self.hparams.normalize_hidden    ,			)

							snake_case__      :   Union[str, Any]      =    self.teacher(
							    __UpperCamelCase    ,			attention_mask=__UpperCamelCase    ,			encoder_outputs=(teacher_enc_outputs,)    ,			decoder_input_ids=__UpperCamelCase    ,			output_hidden_states=self.do_calc_hidden_loss    ,			use_cache=__UpperCamelCase    ,			)
							snake_case__      :   int      =    decoder_input_ids.ne(__UpperCamelCase			)
							snake_case__      :   Optional[Any]      =    self.calc_ce_loss(__UpperCamelCase    ,			__UpperCamelCase    ,			teacher_outputs['''logits''']			)
							if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
											snake_case__      :   List[Any]      =    self.calc_hidden_loss(
											    __UpperCamelCase    ,			student_outputs['''decoder_hidden_states''']    ,			teacher_outputs['''decoder_hidden_states''']    ,			self.d_matches    ,			normalize_hidden=self.hparams.normalize_hidden    ,			)

							snake_case__      :   List[str]      =    (
							    self.alpha_ce * loss_ce
							    + self.alpha_mlm * student_lm_loss
							    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
							)
							return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







			@staticmethod
			def 				lowerCAmelCase (     __UpperCamelCase		:						Union[str, Any]    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						int			)      ->      int:
							"""simple docstring"""





							snake_case__      :   int      =    '''expected list or tuple for hidden_states, got tensor of shape: '''
							assert not isinstance(__UpperCamelCase    ,			torch.Tensor			), F'''{msg}{hidden_states.shape}'''
							assert not isinstance(__UpperCamelCase    ,			torch.Tensor			), F'''{msg}{hidden_states_T.shape}'''
							snake_case__      :   str      =    attention_mask.to(hidden_states[0]			)
							snake_case__      :   Dict      =    mask.sum() * hidden_states[0].size(-1			)
							snake_case__      :   Dict      =    torch.stack([hidden_states[i] for i in range(len(__UpperCamelCase			)			)]			)
							snake_case__      :   Any      =    torch.stack([hidden_states_T[j] for j in matches]			)
							assert student_states.shape == teacher_states.shape, F'''{student_states.shape} != {teacher_states.shape}'''
							if normalize_hidden:
											snake_case__      :   Optional[Any]      =    nn.functional.layer_norm(__UpperCamelCase    ,			student_states.shape[1:]			)
											snake_case__      :   Optional[int]      =    nn.functional.layer_norm(__UpperCamelCase    ,			teacher_states.shape[1:]			)
							snake_case__      :   List[str]      =    nn.functional.mse_loss(__UpperCamelCase    ,			__UpperCamelCase    ,			reduction='''none'''			)
							snake_case__      :   int      =    (mse * mask.unsqueeze(0			).unsqueeze(-1			)).sum() / valid_count
							return masked_mse



def 							__UpperCAmelCase				(				UpperCamelCase__				:str			)		->						Union[str, Any]:
				# NOTE: if --student argument was specified and the teacher and student base models
				# are different, the models still have to have the same tokenizer, specified by
				# --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
				# from bart to t5. This s because if the tokenizers are different, the output space
				# for the two models is also different and their logits are not comparable.
				parser.add_argument('''--teacher'''   ,				type=UpperCamelCase__			)
				parser.add_argument('''--alpha_ce'''   ,				default=0.8   ,				type=UpperCamelCase__			)
				parser.add_argument('''--alpha_mlm'''   ,				default=0.2   ,				type=UpperCamelCase__			)
				parser.add_argument('''--alpha_hid'''   ,				default=0.0   ,				type=UpperCamelCase__   ,				required=UpperCamelCase__			)
				parser.add_argument('''--student'''   ,				type=UpperCamelCase__   ,				required=UpperCamelCase__			)
				parser.add_argument('''--student_decoder_layers'''   ,				default=12   ,				type=UpperCamelCase__   ,				required=UpperCamelCase__			)
				parser.add_argument('''--student_encoder_layers'''   ,				default=12   ,				type=UpperCamelCase__   ,				required=UpperCamelCase__			)
				parser.add_argument('''--no_teacher'''   ,				action='''store_true'''   ,				default=UpperCamelCase__			)
				parser.add_argument('''--length_penalty'''   ,				type=UpperCamelCase__   ,				default=-1			)
				parser.add_argument('''--supervise_forward'''   ,				action='''store_true'''   ,				default=UpperCamelCase__			)
				parser.add_argument('''--normalize_hidden'''   ,				action='''store_true'''   ,				default=UpperCamelCase__			)






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  'translation'
			A__								=  ['bleu']
			A__								=  'bleu'



			def __init__(     self		:						str    ,			__UpperCamelCase		:						str    ,			**__UpperCamelCase		:						int			)      ->      List[Any]:
							"""simple docstring"""





							super().__init__(__UpperCamelCase    ,			**__UpperCamelCase			)
							assert hparams.src_lang is not None
							assert hparams.tgt_lang is not None
							snake_case__      :   List[Any]      =    hparams.src_lang
							snake_case__      :   List[Any]      =    hparams.tgt_lang
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer    ,			__UpperCamelCase			):
											snake_case__      :   Dict      =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						int			)      ->      dict:
							"""simple docstring"""





							return calculate_bleu(__UpperCamelCase    ,			__UpperCamelCase			)







			@staticmethod
			def 				lowerCAmelCase (     __UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						str			)      ->      Optional[Any]:
							"""simple docstring"""





							TranslationModule.add_model_specific_args(__UpperCamelCase    ,			__UpperCamelCase			)
							add_distill_args(__UpperCamelCase			)
							return parser



def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple			)		->						List[Any]:
				if args.no_teacher:
								snake_case__      :   Optional[Any]      =    TranslationModule if '''translation''' in args.task else SummarizationModule
				else:  # DISTILL WITH TEACHER
								snake_case__      :   Optional[Any]      =    TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
				snake_case__      :   str      =    module_cls.__name__
				print(F'''using module {args.setup_cls}'''			)
				snake_case__      :   Optional[int]      =    module_cls(UpperCamelCase__			)
				return model


def 							__UpperCAmelCase				(				UpperCamelCase__				:str			)		->						Any:
				Path(args.output_dir			).mkdir(exist_ok=UpperCamelCase__			)
				check_output_dir(UpperCamelCase__   ,				expected_items=3			)

				snake_case__      :   Tuple      =    create_module(UpperCamelCase__			)
				return ft_main(UpperCamelCase__   ,				model=UpperCamelCase__			)


if __name__ == "__main__":
				_lowercase       :						Any						=argparse.ArgumentParser()
				_lowercase       :						Any						=pl.Trainer.add_argparse_args(parser)
				_lowercase       :						Dict						=SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
				_lowercase       :						str						=parser.parse_args()

				distill_main(args)






import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    Dataset.from_dict({'i': range(17							)}							)
			UpperCAmelCase__		:Optional[Any] 		=    len(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    3
			UpperCAmelCase__		:List[str] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(SCREAMING_SNAKE_CASE							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Union[str, Any] 		=    3
			UpperCAmelCase__		:Union[str, Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Union[str, Any] 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:Dict 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('shards_per_node'     ,						[1, 2, 3]							)
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			def gen(SCREAMING_SNAKE_CASE							):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(17							))

			UpperCAmelCase__		:int 		=    3
			UpperCAmelCase__		:Any 		=    shards_per_node * world_size
			UpperCAmelCase__		:int 		=    {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(SCREAMING_SNAKE_CASE							)]}
			UpperCAmelCase__		:Optional[Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE     ,						gen_kwargs=SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			assert full_ds.n_shards == world_size * shards_per_node
			UpperCAmelCase__		:Optional[Any] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Tuple 		=    2
			UpperCAmelCase__		:List[str] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:str 		=    len(list(SCREAMING_SNAKE_CASE							)							)

			UpperCAmelCase__		:List[str] 		=    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle(seed=42							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle()

			UpperCAmelCase__		:Dict 		=    split_dataset_by_node(full_ds.shuffle(seed=42							)     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(full_ds.shuffle()     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							)




@pytest.mark.parametrize('streaming'     ,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == 'nt'     ,						reason='execute_subprocess_async doesn\'t support windows'							)
@pytest.mark.integration
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    2
			UpperCAmelCase__		:str 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Optional[int] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Any 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Optional[int] 		=    f"""
        --streaming={streaming}
    """.split()
			UpperCAmelCase__		:Optional[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)




@pytest.mark.parametrize(
    'nproc_per_node, num_workers'     ,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]     ,						)
@require_torch
@pytest.mark.skipif(os.name == 'nt'     ,						reason='execute_subprocess_async doesn\'t support windows'							)
@pytest.mark.integration
def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    True
			UpperCAmelCase__		:Optional[Any] 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Union[str, Any] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			UpperCAmelCase__		:List[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)





