
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax
				import jax.numpy as jnp
				import numpy as np

				from transformers.modeling_flax_pytorch_utils import (
				    convert_pytorch_state_dict_to_flax,
				    load_flax_weights_in_pytorch_model,
				)
				from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
				import torch






@require_flax
class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=14  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=37  ,						SCREAMING_SNAKE_CASE_="gelu"  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=512  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						SCREAMING_SNAKE_CASE_=None  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      seq_length
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      use_input_mask
													__UpperCamelCase  	=      use_labels
													__UpperCamelCase  	=      vocab_size
													__UpperCamelCase  	=      d_model
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      ffn_dim
													__UpperCamelCase  	=      activation_function
													__UpperCamelCase  	=      activation_dropout
													__UpperCamelCase  	=      attention_dropout
													__UpperCamelCase  	=      max_position_embeddings
													__UpperCamelCase  	=      initializer_range
													__UpperCamelCase  	=      None
													__UpperCamelCase  	=      0
													__UpperCamelCase  	=      2
													__UpperCamelCase  	=      1



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      np.clip(ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size      )  ,						3  ,						self.vocab_size      )

													__UpperCamelCase  	=      None
													if self.use_input_mask:
																			__UpperCamelCase  	=      random_attention_mask([self.batch_size, self.seq_length]      )

													__UpperCamelCase  	=      XGLMConfig(
													    vocab_size=self.vocab_size  ,						d_model=self.hidden_size  ,						num_layers=self.num_hidden_layers  ,						attention_heads=self.num_attention_heads  ,						ffn_dim=self.ffn_dim  ,						activation_function=self.activation_function  ,						activation_dropout=self.activation_dropout  ,						attention_dropout=self.attention_dropout  ,						max_position_embeddings=self.max_position_embeddings  ,						initializer_range=self.initializer_range  ,						use_cache=SCREAMING_SNAKE_CASE_  ,						bos_token_id=self.bos_token_id  ,						eos_token_id=self.eos_token_id  ,						pad_token_id=self.pad_token_id  ,						)

													return (config, input_ids, input_mask)



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      config_and_inputs
													__UpperCamelCase  	=      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
													return config, inputs_dict



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      self.prepare_config_and_inputs()

													__UpperCamelCase  	=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
													__UpperCamelCase  	=      ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2      )

													return (
													    config,
													    input_ids,
													    attention_mask,
													    encoder_hidden_states,
													    encoder_attention_mask,
													)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      20
													__UpperCamelCase  	=      model_class_name(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      model.init_cache(input_ids.shape[0]  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      jnp.ones((input_ids.shape[0], max_decoder_length)  ,						dtype='''i4'''      )

													__UpperCamelCase  	=      jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1      )[None, :]  ,						(input_ids.shape[0], input_ids.shape[-1] - 1)      )
													__UpperCamelCase  	=      model(
													    input_ids[:, :-1]  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						past_key_values=SCREAMING_SNAKE_CASE_  ,						position_ids=SCREAMING_SNAKE_CASE_  ,						)

													__UpperCamelCase  	=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,						dtype='''i4'''      )
													__UpperCamelCase  	=      model(
													    input_ids[:, -1:]  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						past_key_values=outputs_cache.past_key_values  ,						position_ids=SCREAMING_SNAKE_CASE_  ,						)

													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
													self.parent.assertTrue(diff < 1E-3  ,						msg=F"Max diff is {diff}"      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      20
													__UpperCamelCase  	=      model_class_name(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      jnp.concatenate(
													    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]  ,						axis=-1  ,						)

													__UpperCamelCase  	=      model.init_cache(input_ids.shape[0]  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1      )[None, :]  ,						(input_ids.shape[0], input_ids.shape[-1] - 1)      )

													__UpperCamelCase  	=      model(
													    input_ids[:, :-1]  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						past_key_values=SCREAMING_SNAKE_CASE_  ,						position_ids=SCREAMING_SNAKE_CASE_  ,						)
													__UpperCamelCase  	=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,						dtype='''i4'''      )
													__UpperCamelCase  	=      model(
													    input_ids[:, -1:]  ,						past_key_values=outputs_cache.past_key_values  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						position_ids=SCREAMING_SNAKE_CASE_  ,						)

													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
													self.parent.assertTrue(diff < 1E-3  ,						msg=F"Max diff is {diff}"      )






@require_sentencepiece
@require_flax
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
							_snake_case  		=	(FlaxXGLMForCausalLM,) if is_flax_available() else ()



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      FlaxXGLMModelTester(self      )



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							@slow
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      XGLMTokenizer.from_pretrained('''XGLM'''  ,						padding_side='''left'''      )
													__UpperCamelCase  	=      tokenizer(['''Hello this is a long string''', '''Hey''']  ,						return_tensors='''np'''  ,						padding=SCREAMING_SNAKE_CASE_  ,						truncation=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''      )
													__UpperCamelCase  	=      1
													__UpperCamelCase  	=      False

													__UpperCamelCase  	=      jax.jit(model.generate      )

													__UpperCamelCase  	=      jit_generate(inputs['''input_ids''']  ,						attention_mask=inputs['''attention_mask''']      ).sequences

													__UpperCamelCase  	=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_  ,						skip_special_tokens=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      [
													    '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
													    '''Hey, I\'m a newbie to the forum and I\'''',
													]

													self.assertListEqual(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							@is_pt_flax_cross_test
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__      ):
																									# prepare inputs
																									__UpperCamelCase  	=      self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																									__UpperCamelCase  	=      {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									__UpperCamelCase  	=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

																									__UpperCamelCase	,				__UpperCamelCase  	=      pt_inputs['''input_ids'''].shape
																									__UpperCamelCase  	=      np.random.randint(0  ,						seq_length - 1  ,						size=(batch_size,)      )
																									for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_      ):
																															__UpperCamelCase  	=      0
																															__UpperCamelCase  	=      1
																															__UpperCamelCase  	=      0
																															__UpperCamelCase  	=      1
																									__UpperCamelCase  	=      pt_model_class(SCREAMING_SNAKE_CASE_      ).eval()
																									# Flax models don't use the `use_cache` option and cache is not returned as a default.
																									# So we disable `use_cache` here for PyTorch model.
																									__UpperCamelCase  	=      False
																									__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_  ,						dtype=jnp.floataa      )
																									__UpperCamelCase  	=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,						SCREAMING_SNAKE_CASE_      )
																									__UpperCamelCase  	=      fx_state

																									with torch.no_grad():
																															__UpperCamelCase  	=      pt_model(**SCREAMING_SNAKE_CASE_      ).to_tuple()

																									__UpperCamelCase  	=      fx_model(**SCREAMING_SNAKE_CASE_      ).to_tuple()
																									self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						len(SCREAMING_SNAKE_CASE_      )  ,						'''Output lengths differ between Flax and PyTorch'''      )
																									for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																															self.assert_almost_equals(fx_output[:, -1]  ,						pt_output[:, -1].numpy()  ,						4E-2      )

																									with tempfile.TemporaryDirectory() as tmpdirname:
																															pt_model.save_pretrained(SCREAMING_SNAKE_CASE_      )
																															__UpperCamelCase  	=      model_class.from_pretrained(SCREAMING_SNAKE_CASE_  ,						from_pt=SCREAMING_SNAKE_CASE_      )

																									__UpperCamelCase  	=      fx_model_loaded(**SCREAMING_SNAKE_CASE_      ).to_tuple()
																									self.assertEqual(
																									    len(SCREAMING_SNAKE_CASE_      )  ,						len(SCREAMING_SNAKE_CASE_      )  ,						'''Output lengths differ between Flax and PyTorch'''      )
																									for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																															self.assert_almost_equals(fx_output_loaded[:, -1]  ,						pt_output[:, -1].numpy()  ,						4E-2      )



							@is_pt_flax_cross_test
							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__      ):
																									# prepare inputs
																									__UpperCamelCase  	=      self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																									__UpperCamelCase  	=      {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									__UpperCamelCase  	=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

																									__UpperCamelCase  	=      pt_model_class(SCREAMING_SNAKE_CASE_      ).eval()
																									__UpperCamelCase  	=      False
																									__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_  ,						dtype=jnp.floataa      )

																									__UpperCamelCase  	=      load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_  ,						fx_model.params      )
																									__UpperCamelCase	,				__UpperCamelCase  	=      pt_inputs['''input_ids'''].shape
																									__UpperCamelCase  	=      np.random.randint(0  ,						seq_length - 1  ,						size=(batch_size,)      )
																									for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_      ):
																															__UpperCamelCase  	=      0
																															__UpperCamelCase  	=      1
																															__UpperCamelCase  	=      0
																															__UpperCamelCase  	=      1

																									# make sure weights are tied in PyTorch
																									pt_model.tie_weights()

																									with torch.no_grad():
																															__UpperCamelCase  	=      pt_model(**SCREAMING_SNAKE_CASE_      ).to_tuple()

																									__UpperCamelCase  	=      fx_model(**SCREAMING_SNAKE_CASE_      ).to_tuple()
																									self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						len(SCREAMING_SNAKE_CASE_      )  ,						'''Output lengths differ between Flax and PyTorch'''      )
																									for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																															self.assert_almost_equals(fx_output[:, -1]  ,						pt_output[:, -1].numpy()  ,						4E-2      )

																									with tempfile.TemporaryDirectory() as tmpdirname:
																															fx_model.save_pretrained(SCREAMING_SNAKE_CASE_      )
																															__UpperCamelCase  	=      pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_  ,						from_flax=SCREAMING_SNAKE_CASE_      )

																									with torch.no_grad():
																															__UpperCamelCase  	=      pt_model_loaded(**SCREAMING_SNAKE_CASE_      ).to_tuple()

																									self.assertEqual(
																									    len(SCREAMING_SNAKE_CASE_      )  ,						len(SCREAMING_SNAKE_CASE_      )  ,						'''Output lengths differ between Flax and PyTorch'''      )
																									for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																															self.assert_almost_equals(fx_output[:, -1]  ,						pt_output[:, -1].numpy()  ,						4E-2      )



							@slow
							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase  	=      model_class_name.from_pretrained('''facebook/xglm-564M'''      )
																			__UpperCamelCase  	=      model(np.ones((1, 1)      )      )
																			self.assertIsNotNone(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__	=  Dataset.from_dict({"i": range(17)})
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  3
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(lowercase__) 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__	=  3
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(list(lowercase__)) 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_  (     lowercase__):
 def gen(lowercase__):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  shards_per_node * world_size
 lowerCamelCase__	=  {"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(lowercase__)]}
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__					,  gen_kwargs=lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 assert full_ds.n_shards == world_size * shards_per_node
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(lowercase__)) 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__	=  2
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))

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

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






@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_  (     lowercase__):
 lowerCamelCase__	=  2
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  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_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  True
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())

