import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				__SCREAMING_SNAKE_CASE						:    int     = 'platform'

				import jax
				import jax.numpy as jnp

				from transformers import MarianTokenizer
				from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase=None					,			__lowercase=None					,			__lowercase=None					,			__lowercase=None					,			__lowercase=None					,			__lowercase=None					,			)       ->       Dict:

						'''simple docstring'''



						if attention_mask is None:
												_snake_case						:     Union[str, Any]				=    np.where(input_ids != config.pad_token_id					,			1					,			0						)
						if decoder_attention_mask is None:
												_snake_case						:     int				=    np.where(decoder_input_ids != config.pad_token_id					,			1					,			0						)
						if head_mask is None:
												_snake_case						:     List[Any]				=    np.ones((config.encoder_layers, config.encoder_attention_heads)						)
						if decoder_head_mask is None:
												_snake_case						:     Any				=    np.ones((config.decoder_layers, config.decoder_attention_heads)						)
						if cross_attn_head_mask is None:
												_snake_case						:     Dict				=    np.ones((config.decoder_layers, config.decoder_attention_heads)						)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}



class    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				lowercase_=13      ,				lowercase_=7      ,				lowercase_=True      ,				lowercase_=False      ,				lowercase_=99      ,				lowercase_=16      ,				lowercase_=2      ,				lowercase_=4      ,				lowercase_=4      ,				lowercase_="gelu"      ,				lowercase_=0.1      ,				lowercase_=0.1      ,				lowercase_=32      ,				lowercase_=2      ,				lowercase_=1      ,				lowercase_=0      ,				lowercase_=0.02      ,				):
													_snake_case						:     Dict				=    parent
													_snake_case						:     Optional[int]				=    batch_size
													_snake_case						:     Any				=    seq_length
													_snake_case						:     Union[str, Any]				=    is_training
													_snake_case						:     Any				=    use_labels
													_snake_case						:     Dict				=    vocab_size
													_snake_case						:     Union[str, Any]				=    hidden_size
													_snake_case						:     List[str]				=    num_hidden_layers
													_snake_case						:     Union[str, Any]				=    num_attention_heads
													_snake_case						:     Any				=    intermediate_size
													_snake_case						:     int				=    hidden_act
													_snake_case						:     Tuple				=    hidden_dropout_prob
													_snake_case						:     List[Any]				=    attention_probs_dropout_prob
													_snake_case						:     Optional[Any]				=    max_position_embeddings
													_snake_case						:     Tuple				=    eos_token_id
													_snake_case						:     Dict				=    pad_token_id
													_snake_case						:     Union[str, Any]				=    bos_token_id
													_snake_case						:     Optional[Any]				=    initializer_range



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,				self.vocab_size				)      ,				3      ,				self.vocab_size				)
													_snake_case						:     Union[str, Any]				=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,				dtype=np.intaa				))      ,				-1				)

													_snake_case						:     str				=    shift_tokens_right(lowercase_      ,				1      ,				2				)

													_snake_case						:     Tuple				=    MarianConfig(
													    vocab_size=self.vocab_size      ,				d_model=self.hidden_size      ,				encoder_layers=self.num_hidden_layers      ,				decoder_layers=self.num_hidden_layers      ,				encoder_attention_heads=self.num_attention_heads      ,				decoder_attention_heads=self.num_attention_heads      ,				encoder_ffn_dim=self.intermediate_size      ,				decoder_ffn_dim=self.intermediate_size      ,				dropout=self.hidden_dropout_prob      ,				attention_dropout=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				eos_token_id=self.eos_token_id      ,				bos_token_id=self.bos_token_id      ,				pad_token_id=self.pad_token_id      ,				initializer_range=self.initializer_range      ,				use_cache=lowercase_      ,				)
													_snake_case						:     Tuple				=    prepare_marian_inputs_dict(lowercase_      ,				lowercase_      ,				lowercase_				)
													return config, inputs_dict



							def        UpperCamelCase       (						self				):
													_snake_case				,_snake_case						:     int				=    self.prepare_config_and_inputs()
													return config, inputs_dict



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     int				=    20
													_snake_case						:     List[str]				=    model_class_name(lowercase_				)

													_snake_case						:     Optional[Any]				=    model.encode(inputs_dict["input_ids"]				)

													_snake_case				,_snake_case						:     Optional[Any]				=    (
													    inputs_dict["decoder_input_ids"],
													    inputs_dict["decoder_attention_mask"],
													)

													_snake_case						:     Optional[int]				=    model.init_cache(decoder_input_ids.shape[0]      ,				lowercase_      ,				lowercase_				)
													_snake_case						:     List[Any]				=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,				dtype="i4"				)

													_snake_case						:     Tuple				=    jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]      ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,				)
													_snake_case						:     Optional[Any]				=    model.decode(
													    decoder_input_ids[:, :-1]      ,				lowercase_      ,				decoder_attention_mask=lowercase_      ,				past_key_values=lowercase_      ,				decoder_position_ids=lowercase_      ,				)

													_snake_case						:     str				=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,				dtype="i4"				)
													_snake_case						:     Union[str, Any]				=    model.decode(
													    decoder_input_ids[:, -1:]      ,				lowercase_      ,				decoder_attention_mask=lowercase_      ,				past_key_values=outputs_cache.past_key_values      ,				decoder_position_ids=lowercase_      ,				)

													_snake_case						:     Tuple				=    model.decode(lowercase_      ,				lowercase_				)

													_snake_case						:     Tuple				=    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        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Any				=    20
													_snake_case						:     int				=    model_class_name(lowercase_				)

													_snake_case						:     List[str]				=    model.encode(inputs_dict["input_ids"]				)

													_snake_case				,_snake_case						:     Dict				=    (
													    inputs_dict["decoder_input_ids"],
													    inputs_dict["decoder_attention_mask"],
													)

													_snake_case						:     str				=    jnp.concatenate(
													    [
													        decoder_attention_mask,
													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
													    ]      ,				axis=-1      ,				)

													_snake_case						:     Optional[int]				=    model.init_cache(decoder_input_ids.shape[0]      ,				lowercase_      ,				lowercase_				)
													_snake_case						:     int				=    jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]      ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,				)

													_snake_case						:     int				=    model.decode(
													    decoder_input_ids[:, :-1]      ,				lowercase_      ,				decoder_attention_mask=lowercase_      ,				past_key_values=lowercase_      ,				decoder_position_ids=lowercase_      ,				)
													_snake_case						:     int				=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,				dtype="i4"				)
													_snake_case						:     Tuple				=    model.decode(
													    decoder_input_ids[:, -1:]      ,				lowercase_      ,				past_key_values=outputs_cache.past_key_values      ,				decoder_attention_mask=lowercase_      ,				decoder_position_ids=lowercase_      ,				)

													_snake_case						:     Optional[int]				=    model.decode(lowercase_      ,				lowercase_      ,				decoder_attention_mask=lowercase_				)

													_snake_case						:     int				=    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_flax
class    lowercase_    (     __snake_case						, unittest.TestCase						, __snake_case		):
							_lowerCamelCase						=		True
							_lowerCamelCase						=		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							_lowerCamelCase						=		(FlaxMarianMTModel,) if is_flax_available() else ()



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    FlaxMarianModelTester(self				)



							def        UpperCamelCase       (						self				):
													_snake_case				,_snake_case						:     List[Any]				=    self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(lowercase_      ,				lowercase_      ,				lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case				,_snake_case						:     str				=    self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_      ,				lowercase_      ,				lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case				,_snake_case						:     Optional[Any]				=    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__				):
																									_snake_case						:     Dict				=    self._prepare_for_class(lowercase_      ,				lowercase_				)
																									_snake_case						:     Union[str, Any]				=    model_class(lowercase_				)

																									@jax.jit
																									def encode_jitted(lowercase_      ,				lowercase_=None      ,				**lowercase_				):
																															return model.encode(input_ids=lowercase_      ,				attention_mask=lowercase_				)

																									with self.subTest("JIT Enabled"				):
																															_snake_case						:     Union[str, Any]				=    encode_jitted(**lowercase_				).to_tuple()

																									with self.subTest("JIT Disabled"				):
																															with jax.disable_jit():
																																					_snake_case						:     Any				=    encode_jitted(**lowercase_				).to_tuple()

																									self.assertEqual(len(lowercase_				)      ,				len(lowercase_				)				)
																									for jitted_output, output in zip(lowercase_      ,				lowercase_				):
																															self.assertEqual(jitted_output.shape      ,				output.shape				)



							def        UpperCamelCase       (						self				):
													_snake_case				,_snake_case						:     Union[str, Any]				=    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__				):
																									_snake_case						:     Tuple				=    model_class(lowercase_				)
																									_snake_case						:     Union[str, Any]				=    model.encode(inputs_dict["input_ids"]      ,				inputs_dict["attention_mask"]				)

																									_snake_case						:     int				=    {
																									    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																									    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																									    "encoder_outputs": encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(lowercase_      ,				lowercase_      ,				lowercase_				):
																															return model.decode(
																															    decoder_input_ids=lowercase_      ,				decoder_attention_mask=lowercase_      ,				encoder_outputs=lowercase_      ,				)

																									with self.subTest("JIT Enabled"				):
																															_snake_case						:     List[str]				=    decode_jitted(**lowercase_				).to_tuple()

																									with self.subTest("JIT Disabled"				):
																															with jax.disable_jit():
																																					_snake_case						:     int				=    decode_jitted(**lowercase_				).to_tuple()

																									self.assertEqual(len(lowercase_				)      ,				len(lowercase_				)				)
																									for jitted_output, output in zip(lowercase_      ,				lowercase_				):
																															self.assertEqual(jitted_output.shape      ,				output.shape				)



							@slow
							def        UpperCamelCase       (						self				):
													for model_class_name in self.all_model_classes:
																			_snake_case						:     Optional[Any]				=    model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"				)
																			# FlaxMarianForSequenceClassification expects eos token in input_ids
																			_snake_case						:     Dict				=    np.ones((1, 1)				) * model.config.eos_token_id
																			_snake_case						:     List[str]				=    model(lowercase_				)
																			self.assertIsNotNone(lowercase_				)




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     unittest.TestCase		):
							_lowerCamelCase						=		None
							_lowerCamelCase						=		None



							@classmethod
							def        UpperCamelCase       (						cls				):
													_snake_case						:     Dict				=    f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
													return cls



							@cached_property
							def        UpperCamelCase       (						self				):
													return MarianTokenizer.from_pretrained(self.model_name				)



							@property
							def        UpperCamelCase       (						self				):
													return self.tokenizer.eos_token_id



							@cached_property
							def        UpperCamelCase       (						self				):
													_snake_case						:     FlaxMarianMTModel				=    FlaxMarianMTModel.from_pretrained(self.model_name				)
													self.assertEqual(model.config.decoder_start_token_id      ,				model.config.pad_token_id				)
													return model



							def        UpperCamelCase       (						self      ,				**lowercase_				):
													_snake_case						:     List[Any]				=    self.translate_src_text(**lowercase_				)
													self.assertListEqual(self.expected_text      ,				lowercase_				)



							def        UpperCamelCase       (						self      ,				**lowercase_				):
													_snake_case						:     Optional[Any]				=    self.tokenizer(self.src_text      ,				padding=lowercase_      ,				return_tensors="np"      ,				**lowercase_				)
													_snake_case						:     Optional[int]				=    self.model.generate(
													    model_inputs.input_ids      ,				attention_mask=model_inputs.attention_mask      ,				num_beams=2      ,				max_length=128      ,				).sequences
													_snake_case						:     str				=    self.tokenizer.batch_decode(lowercase_      ,				skip_special_tokens=lowercase_				)
													return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'en'
							_lowerCamelCase						=		'fr'
							_lowerCamelCase						=		[
							    'I am a small frog.',
							    'Now I can forget the 100 words of german that I know.',
							]
							_lowerCamelCase						=		[
							    'Je suis une petite grenouille.',
							    'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
							]



							@slow
							def        UpperCamelCase       (						self				):
													self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'fr'
							_lowerCamelCase						=		'en'
							_lowerCamelCase						=		[
							    'Donnez moi le micro.',
							    'Tom et Mary étaient assis à une table.',  # Accents
							]
							_lowerCamelCase						=		[
							    'Give me the microphone.',
							    'Tom and Mary were sitting at a table.',
							]



							@slow
							def        UpperCamelCase       (						self				):
													self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'mt'
							_lowerCamelCase						=		'en'
							_lowerCamelCase						=		['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
							_lowerCamelCase						=		['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']



							@slow
							def        UpperCamelCase       (						self				):
													self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'en'
							_lowerCamelCase						=		'de'
							_lowerCamelCase						=		[
							    'I am a small frog.',
							    'Now I can forget the 100 words of german that I know.',
							    'Tom asked his teacher for advice.',
							    'That\'s how I would do it.',
							    'Tom really admired Mary\'s courage.',
							    'Turn around and close your eyes.',
							]
							_lowerCamelCase						=		[
							    'Ich bin ein kleiner Frosch.',
							    'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
							    'Tom bat seinen Lehrer um Rat.',
							    'So würde ich das machen.',
							    'Tom bewunderte Marias Mut wirklich.',
							    'Drehen Sie sich um und schließen Sie die Augen.',
							]



							@slow
							def        UpperCamelCase       (						self				):
													self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'en'
							_lowerCamelCase						=		'zh'
							_lowerCamelCase						=		['My name is Wolfgang and I live in Berlin']
							_lowerCamelCase						=		['我叫沃尔夫冈 我住在柏林']



							@slow
							def        UpperCamelCase       (						self				):
													self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'ru'
							_lowerCamelCase						=		'fr'
							_lowerCamelCase						=		['Он показал мне рукопись своей новой пьесы.']
							_lowerCamelCase						=		['Il m\'a montré le manuscrit de sa nouvelle pièce.']



							@slow
							def        UpperCamelCase       (						self				):
													self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'en'
							_lowerCamelCase						=		'ROMANCE'
							_lowerCamelCase						=		[
							    '>>fr<< Don\'t spend so much time watching TV.',
							    '>>pt<< Your message has been sent.',
							    '>>es<< He\'s two years older than me.',
							]
							_lowerCamelCase						=		[
							    'Ne passez pas autant de temps à regarder la télé.',
							    'A sua mensagem foi enviada.',
							    'Es dos años más viejo que yo.',
							]



							@slow
							def        UpperCamelCase       (						self				):
													self._assert_generated_batch_equal_expected()
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 					snake_case						()       ->       int:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    Dataset.from_dict({"i": range(17						)}						)
						_snake_case						:     Tuple				=    len(__lowercase						)
						_snake_case						:     Any				=    3
						_snake_case						:     List[str]				=    [
						    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 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



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

						_snake_case						:     List[Any]				=    3
						_snake_case						:     Dict				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     List[Any]				=    len(list(__lowercase						)						)
						_snake_case						:     str				=    [
						    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 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



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

						_snake_case						:     str				=    3
						_snake_case						:     List[str]				=    shards_per_node * world_size
						_snake_case						:     List[Any]				=    {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowercase						)]}
						_snake_case						:     Optional[int]				=    IterableDataset.from_generator(__lowercase					,			gen_kwargs=__lowercase						)
						_snake_case						:     Tuple				=    len(list(__lowercase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_snake_case						:     List[Any]				=    [
						    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 					snake_case						()       ->       Dict:

						'''simple docstring'''



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

						_snake_case						:     Dict				=    2
						_snake_case						:     str				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     Dict				=    len(list(__lowercase						)						)

						_snake_case						:     Optional[int]				=    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()

						_snake_case						:     Optional[int]				=    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 					snake_case						(__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    2
						_snake_case						:     Optional[Any]				=    get_torch_dist_unique_port()
						_snake_case						:     Optional[int]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     Any				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     str				=    F"""
        --streaming={streaming}
    """.split()
						_snake_case						:     Optional[Any]				=    [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 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



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