
"""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 					A__			(      _lowerCamelCase):
		A_		:			Optional[int]							  =      ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']

		def __init__( self			,						_SCREAMING_SNAKE_CASE					):
			assert Path(hparams.data_dir					).exists()
			__lowerCAmelCase     :		Union[str, Any]		     = Path(hparams.output_dir					)
			self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		Any		     = self.output_dir.joinpath('student'					)

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

			if hparams.length_penalty != -1:
				__lowerCAmelCase     :		Optional[Any]		     = hparams.length_penalty
			__lowerCAmelCase     :		Dict		     = hparams.teacher  # Use teacher's tokenizer
			super().__init__(_SCREAMING_SNAKE_CASE			,						model=_SCREAMING_SNAKE_CASE			,						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":
				__lowerCAmelCase     :		int		     = len(student.get_encoder().block					)
				__lowerCAmelCase     :		Tuple		     = len(student.get_decoder().block					)
				__lowerCAmelCase     :		List[Any]		     = len(teacher.get_encoder().block					)
				__lowerCAmelCase     :		List[Any]		     = len(teacher.get_decoder().block					)
			else:
				__lowerCAmelCase     :		Dict		     = student.config.encoder_layers
				__lowerCAmelCase     :		Union[str, Any]		     = student.config.decoder_layers
				__lowerCAmelCase     :		List[Any]		     = teacher.config.encoder_layers
				__lowerCAmelCase     :		Union[str, Any]		     = teacher.config.decoder_layers

			__lowerCAmelCase     :		Dict		     = not (hparams.student is None or hparams.teacher == hparams.student)
			__lowerCAmelCase     :		Tuple		     = (not self.different_base_models) and hparams.alpha_hid > 0
			__lowerCAmelCase     :		Any		     = self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
			# self.different_encoder determines whether we need to run the teacher encoder
			__lowerCAmelCase     :		Optional[int]		     = 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:
				__lowerCAmelCase     :		Dict		     = list(range(_SCREAMING_SNAKE_CASE					)					)
			if d_layer_ids is None:
				__lowerCAmelCase     :		Any		     = list(range(_SCREAMING_SNAKE_CASE					)					)

			__lowerCAmelCase     ,  __lowerCAmelCase     :		Tuple		     = 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:
					__lowerCAmelCase     :		Optional[int]		     = get_layers_to_supervise(
					    n_student=len(self.e_layer_ids					)			,						n_teacher=_SCREAMING_SNAKE_CASE					)
					__lowerCAmelCase     :		Any		     = get_layers_to_supervise(
					    n_student=len(self.d_layer_ids					)			,						n_teacher=_SCREAMING_SNAKE_CASE					)
				else:  # student layer should emulate hidden states of the teacher layer it was copied from
					__lowerCAmelCase     :		Tuple		     = self.e_layer_ids
					__lowerCAmelCase     :		Union[str, Any]		     = self.d_layer_ids
			else:
				__lowerCAmelCase     :		Tuple		     = None
				__lowerCAmelCase     :		str		     = None

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

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		List[Any]		     = mask[:, :, None].expand_as(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Dict		     = s_logits.size(-1					)
			__lowerCAmelCase     :		Optional[int]		     = torch.masked_select(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)  # (bs * seq_length * voc_size) modulo the 1s in mask
			__lowerCAmelCase     :		Union[str, Any]		     = torch.masked_select(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)  # (bs * seq_length * voc_size) modulo the 1s in mask
			__lowerCAmelCase     :		List[str]		     = s_logits_slct.view(-1			,						_SCREAMING_SNAKE_CASE					)  # (bs * seq_length, voc_size) modulo the 1s in mask
			__lowerCAmelCase     :		Optional[int]		     = t_logits_slct.view(-1			,						_SCREAMING_SNAKE_CASE					)  # (bs * seq_length, voc_size) modulo the 1s in mask
			assert t_logits_slct.size() == s_logits_slct.size()
			__lowerCAmelCase     :		int		     = (
			    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				( _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			add_distill_args(_SCREAMING_SNAKE_CASE					)
			return parser

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

			# noinspection PyCallingNonCallable
			__lowerCAmelCase     :		List[str]		     = self(
			    _SCREAMING_SNAKE_CASE			,						attention_mask=_SCREAMING_SNAKE_CASE			,						decoder_input_ids=_SCREAMING_SNAKE_CASE			,						output_hidden_states=self.do_calc_hidden_loss			,						output_attentions=_SCREAMING_SNAKE_CASE			,						use_cache=_SCREAMING_SNAKE_CASE			,						)
			__lowerCAmelCase     :		Dict		     = 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
				__lowerCAmelCase     :		Optional[Any]		     = nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		List[str]		     = loss_fct(lm_logits.view(-1			,						lm_logits.shape[-1]					)			,						labels.view(-1					)					)
			else:
				__lowerCAmelCase     :		Any		     = nn.functional.log_softmax(_SCREAMING_SNAKE_CASE			,						dim=-1					)
				__lowerCAmelCase     ,  __lowerCAmelCase     :		List[str]		     = label_smoothed_nll_loss(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						self.hparams.label_smoothing			,						ignore_index=_SCREAMING_SNAKE_CASE					)

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

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

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

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



def        __lowerCAmelCase							(_UpperCamelCase						):
	# 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 					A__			(      _lowerCamelCase):
		A_		:			Union[str, Any]							  =      'translation'
		A_		:			Dict							  =      ['bleu']
		A_		:			int							  =      'bleu'

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					):
			super().__init__(_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)
			assert hparams.src_lang is not None
			assert hparams.tgt_lang is not None
			__lowerCAmelCase     :		str		     = hparams.src_lang
			__lowerCAmelCase     :		Optional[int]		     = hparams.tgt_lang
			if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,						_SCREAMING_SNAKE_CASE					):
				__lowerCAmelCase     :		str		     = self.tokenizer.lang_code_to_id[hparams.tgt_lang]

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			return calculate_bleu(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)







		@staticmethod
		def 				__lowerCamelCase				( _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			TranslationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			add_distill_args(_SCREAMING_SNAKE_CASE					)
			return parser



def        __lowerCAmelCase							(_UpperCamelCase						):
	if args.no_teacher:
		__lowerCAmelCase     :		Tuple		     = TranslationModule if 'translation' in args.task else SummarizationModule
	else:  # DISTILL WITH TEACHER
		__lowerCAmelCase     :		List[str]		     = TranslationDistiller if 'translation' in args.task else SummarizationDistiller
	__lowerCAmelCase     :		str		     = module_cls.__name__
	print(F"using module {args.setup_cls}"						)
	__lowerCAmelCase     :		Optional[int]		     = module_cls(_UpperCamelCase						)
	return model
def        __lowerCAmelCase							(_UpperCamelCase						):
	Path(args.output_dir						).mkdir(exist_ok=_UpperCamelCase						)
	check_output_dir(_UpperCamelCase			,    expected_items=3						)

	__lowerCAmelCase     :		List[Any]		     = create_module(_UpperCamelCase						)
	return ft_main(_UpperCamelCase			,    model=_UpperCamelCase						)


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

							distill_main(args)

"""simple docstring"""







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        __lowerCAmelCase							():
	__lowerCAmelCase     :		Optional[int]		     = Dataset.from_dict({'i': range(17						)}						)
	__lowerCAmelCase     :		List[Any]		     = len(_UpperCamelCase						)
	__lowerCAmelCase     :		Tuple		     = 3
	__lowerCAmelCase     :		List[Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(_UpperCamelCase						) 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        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[Any]		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		List[Any]		     = len(list(_UpperCamelCase						)						)
	__lowerCAmelCase     :		str		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(list(_UpperCamelCase						)						) 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        __lowerCAmelCase							(_UpperCamelCase						):
	def gen(_UpperCamelCase						):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[int]		     = shards_per_node * world_size
	__lowerCAmelCase     :		Dict		     = {'shards': [F"shard_{shard_idx}.txt" for shard_idx in range(_UpperCamelCase						)]}
	__lowerCAmelCase     :		Dict		     = IterableDataset.from_generator(_UpperCamelCase			,    gen_kwargs=_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = len(list(_UpperCamelCase						)						)
	assert full_ds.n_shards == world_size * shards_per_node
	__lowerCAmelCase     :		Union[str, Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(_UpperCamelCase						)						) 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        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		str		     = 2
	__lowerCAmelCase     :		Any		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = len(list(_UpperCamelCase						)						)

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

	__lowerCAmelCase     :		List[str]		     = split_dataset_by_node(full_ds.shuffle(seed=42						)			,    rank=0			,    world_size=_UpperCamelCase						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(full_ds.shuffle()			,    rank=0			,    world_size=_UpperCamelCase						)
@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        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = 2
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		List[Any]		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		int		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		Tuple		     = F"\n        --streaming={streaming}\n    ".split()
	__lowerCAmelCase     :		Optional[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    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        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = True
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		Tuple		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		Optional[Any]		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)