
'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''bigbird_pegasus'''
				_snake_case		     =			['''past_key_values''']
				_snake_case		     =			{
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''hidden_size''': '''d_model''',
				    '''attention_probs_dropout_prob''': '''attention_dropout''',
				}




				def __init__(				self						:					str,     SCREAMING_SNAKE_CASE_						:					List[str]=9_61_03,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=40_96,     SCREAMING_SNAKE_CASE_						:					Tuple=16,     SCREAMING_SNAKE_CASE_						:					Any=40_96,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=16,     SCREAMING_SNAKE_CASE_						:					Any=16,     SCREAMING_SNAKE_CASE_						:					str=40_96,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=16,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[int]=0.0,     SCREAMING_SNAKE_CASE_						:					Tuple=True,     SCREAMING_SNAKE_CASE_						:					str=True,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="gelu_new",     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=10_24,     SCREAMING_SNAKE_CASE_						:					Any=0.1,     SCREAMING_SNAKE_CASE_						:					Tuple=0.0,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=0.0,     SCREAMING_SNAKE_CASE_						:					int=0.02,     SCREAMING_SNAKE_CASE_						:					str=2,     SCREAMING_SNAKE_CASE_						:					Optional[int]=0.0,     SCREAMING_SNAKE_CASE_						:					Any=True,     SCREAMING_SNAKE_CASE_						:					List[Any]=0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=2,     SCREAMING_SNAKE_CASE_						:					Tuple=1,     SCREAMING_SNAKE_CASE_						:					Any="block_sparse",     SCREAMING_SNAKE_CASE_						:					Optional[Any]=64,     SCREAMING_SNAKE_CASE_						:					Dict=3,     SCREAMING_SNAKE_CASE_						:					List[str]=False,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										snake_case							:  Union[str, Any]			=     vocab_size
										snake_case							:  Dict			=     max_position_embeddings
										snake_case							:  Optional[Any]			=     d_model
										snake_case							:  str			=     encoder_ffn_dim
										snake_case							:  Tuple			=     encoder_layers
										snake_case							:  Any			=     encoder_attention_heads
										snake_case							:  str			=     decoder_ffn_dim
										snake_case							:  List[str]			=     decoder_layers
										snake_case							:  str			=     decoder_attention_heads
										snake_case							:  Union[str, Any]			=     dropout
										snake_case							:  int			=     attention_dropout
										snake_case							:  List[Any]			=     activation_dropout
										snake_case							:  Tuple			=     activation_function
										snake_case							:  List[str]			=     init_std
										snake_case							:  Union[str, Any]			=     encoder_layerdrop
										snake_case							:  List[Any]			=     decoder_layerdrop
										snake_case							:  Optional[int]			=     classifier_dropout
										snake_case							:  Optional[int]			=     use_cache
										snake_case							:  Optional[Any]			=     encoder_layers
										snake_case							:  List[Any]			=     scale_embedding  # scale factor will be sqrt(d_model) if True

										# extra config
										snake_case							:  Optional[int]			=     attention_type
										snake_case							:  int			=     block_size
										snake_case							:  str			=     num_random_blocks
										snake_case							:  int			=     use_bias

										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_,     is_encoder_decoder=SCREAMING_SNAKE_CASE_,     decoder_start_token_id=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )




class     a (					__magic_name__     ):




				@property
				def 						__snake_case				(				self						:					List[str]   ):
										if self.task in ["default", "seq2seq-lm"]:
																snake_case							:  List[str]			=     OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]   )

																if self.use_past:
																						snake_case							:  Dict			=     {0: '''batch'''}
																						snake_case							:  Any			=     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																else:
																						snake_case							:  Union[str, Any]			=     {0: '''batch''', 1: '''decoder_sequence'''}
																						snake_case							:  Tuple			=     {0: '''batch''', 1: '''decoder_sequence'''}

																if self.use_past:
																						self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,     direction='''inputs'''   )
										elif self.task == "causal-lm":
																# TODO: figure this case out.
																snake_case							:  int			=     OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]   )
																if self.use_past:
																						snake_case,       snake_case							:  Dict			=     self.num_layers
																						for i in range(SCREAMING_SNAKE_CASE_   ):
																												snake_case							:  Union[str, Any]			=     {0: '''batch''', 2: '''past_sequence + sequence'''}
																												snake_case							:  int			=     {0: '''batch''', 2: '''past_sequence + sequence'''}
										else:
																snake_case							:  str			=     OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
																        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
																    ]   )

										return common_inputs




				@property
				def 						__snake_case				(				self						:					Optional[int]   ):
										if self.task in ["default", "seq2seq-lm"]:
																snake_case							:  Dict			=     super().outputs
										else:
																snake_case							:  str			=     super(SCREAMING_SNAKE_CASE_,     self   ).outputs
																if self.use_past:
																						snake_case,       snake_case							:  Dict			=     self.num_layers
																						for i in range(SCREAMING_SNAKE_CASE_   ):
																												snake_case							:  Optional[Any]			=     {0: '''batch''', 2: '''past_sequence + sequence'''}
																												snake_case							:  Optional[Any]			=     {0: '''batch''', 2: '''past_sequence + sequence'''}
										return common_outputs




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					PreTrainedTokenizer,     SCREAMING_SNAKE_CASE_						:					int = -1,     SCREAMING_SNAKE_CASE_						:					int = -1,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					Optional[TensorType] = None,     ):
										snake_case							:  Tuple			=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

										# Generate decoder inputs
										snake_case							:  Optional[Any]			=     seq_length if not self.use_past else 1
										snake_case							:  List[Any]			=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										snake_case							:  Dict			=     dict(**SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										if self.use_past:
																if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
																else:
																						import torch
																snake_case,       snake_case							:  Dict			=     common_inputs['''input_ids'''].shape
																snake_case							:  List[Any]			=     common_inputs['''decoder_input_ids'''].shape[1]
																snake_case,       snake_case							:  Union[str, Any]			=     self.num_attention_heads
																snake_case							:  Dict			=     (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																snake_case							:  Tuple			=     decoder_seq_length + 3
																snake_case							:  Optional[int]			=     (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																snake_case							:  int			=     torch.cat(
																    [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )],     dim=1   )

																snake_case							:  str			=     []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																snake_case,       snake_case							:  Optional[int]			=     self.num_layers
																snake_case							:  Dict			=     min(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																snake_case							:  List[str]			=     max(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) - min_num_layers
																snake_case							:  int			=     '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																for _ in range(SCREAMING_SNAKE_CASE_   ):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(SCREAMING_SNAKE_CASE_   ),
																						        torch.zeros(SCREAMING_SNAKE_CASE_   ),
																						        torch.zeros(SCREAMING_SNAKE_CASE_   ),
																						        torch.zeros(SCREAMING_SNAKE_CASE_   ),
																						    )   )
																# TODO: test this.
																snake_case							:  Optional[int]			=     encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																for _ in range(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																						common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE_   ), torch.zeros(SCREAMING_SNAKE_CASE_   ))   )
										return common_inputs




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					PreTrainedTokenizer,     SCREAMING_SNAKE_CASE_						:					int = -1,     SCREAMING_SNAKE_CASE_						:					int = -1,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					Optional[TensorType] = None,     ):
										snake_case							:  List[str]			=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

										if self.use_past:
																if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
																else:
																						import torch
																snake_case,       snake_case							:  Any			=     common_inputs['''input_ids'''].shape
																# Not using the same length for past_key_values
																snake_case							:  str			=     seqlen + 2
																snake_case,       snake_case							:  int			=     self.num_layers
																snake_case,       snake_case							:  int			=     self.num_attention_heads
																snake_case							:  Union[str, Any]			=     (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																snake_case							:  str			=     common_inputs['''attention_mask'''].dtype
																snake_case							:  Optional[Any]			=     torch.cat(
																    [common_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     dtype=SCREAMING_SNAKE_CASE_   )],     dim=1   )
																snake_case							:  List[Any]			=     [
																    (torch.zeros(SCREAMING_SNAKE_CASE_   ), torch.zeros(SCREAMING_SNAKE_CASE_   )) for _ in range(SCREAMING_SNAKE_CASE_   )
																]
										return common_inputs




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					PreTrainedTokenizer,     SCREAMING_SNAKE_CASE_						:					int = -1,     SCREAMING_SNAKE_CASE_						:					int = -1,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					Optional[TensorType] = None,     ):
										# Copied from OnnxConfig.generate_dummy_inputs
										# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										snake_case							:  List[Any]			=     compute_effective_axis_dimension(
										    SCREAMING_SNAKE_CASE_,     fixed_dimension=OnnxConfig.default_fixed_batch,     num_token_to_add=0   )

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										snake_case							:  Any			=     tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     compute_effective_axis_dimension(
										    SCREAMING_SNAKE_CASE_,     fixed_dimension=OnnxConfig.default_fixed_sequence,     num_token_to_add=SCREAMING_SNAKE_CASE_   )

										# Generate dummy inputs according to compute batch and sequence
										snake_case							:  Optional[Any]			=     [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
										snake_case							:  Optional[int]			=     dict(tokenizer(SCREAMING_SNAKE_CASE_,     return_tensors=SCREAMING_SNAKE_CASE_   )   )
										return common_inputs




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PreTrainedTokenizer,     SCREAMING_SNAKE_CASE_						:					int = -1,     SCREAMING_SNAKE_CASE_						:					int = -1,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					Optional[TensorType] = None,     ):
										if self.task in ["default", "seq2seq-lm"]:
																snake_case							:  List[Any]			=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    SCREAMING_SNAKE_CASE_,     batch_size=SCREAMING_SNAKE_CASE_,     seq_length=SCREAMING_SNAKE_CASE_,     is_pair=SCREAMING_SNAKE_CASE_,     framework=SCREAMING_SNAKE_CASE_   )

										elif self.task == "causal-lm":
																snake_case							:  Dict			=     self._generate_dummy_inputs_for_causal_lm(
																    SCREAMING_SNAKE_CASE_,     batch_size=SCREAMING_SNAKE_CASE_,     seq_length=SCREAMING_SNAKE_CASE_,     is_pair=SCREAMING_SNAKE_CASE_,     framework=SCREAMING_SNAKE_CASE_   )
										else:
																snake_case							:  Optional[int]			=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    SCREAMING_SNAKE_CASE_,     batch_size=SCREAMING_SNAKE_CASE_,     seq_length=SCREAMING_SNAKE_CASE_,     is_pair=SCREAMING_SNAKE_CASE_,     framework=SCREAMING_SNAKE_CASE_   )

										return common_inputs




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Optional[Any]   ):
										if self.task in ["default", "seq2seq-lm"]:
																snake_case							:  str			=     super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										else:
																snake_case							:  List[str]			=     super(SCREAMING_SNAKE_CASE_,     self   )._flatten_past_key_values_(
																    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )



"""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()						)