




"""simple docstring"""







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_      =					'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 ,):


    """simple docstring"""







    if attention_mask is None:
        UpperCamelCase										=  np.where(input_ids != config.pad_token_id ,1 ,0						)
    if decoder_attention_mask is None:
        UpperCamelCase										=  np.where(decoder_input_ids != config.pad_token_id ,1 ,0						)
    if head_mask is None:
        UpperCamelCase										=  np.ones((config.encoder_layers, config.encoder_attention_heads)						)
    if decoder_head_mask is None:
        UpperCamelCase										=  np.ones((config.decoder_layers, config.decoder_attention_heads)						)
    if cross_attn_head_mask is None:
        UpperCamelCase										=  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 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=1_3					,				lowerCamelCase_=7					,				lowerCamelCase_=True					,				lowerCamelCase_=False					,				lowerCamelCase_=9_9					,				lowerCamelCase_=1_6					,				lowerCamelCase_=2					,				lowerCamelCase_=4					,				lowerCamelCase_=4					,				lowerCamelCase_="gelu"					,				lowerCamelCase_=0.1					,				lowerCamelCase_=0.1					,				lowerCamelCase_=3_2					,				lowerCamelCase_=2					,				lowerCamelCase_=1					,				lowerCamelCase_=0					,				lowerCamelCase_=0.02					,				)							->      List[Any]:
       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  seq_length
       UpperCamelCase										=  is_training
       UpperCamelCase										=  use_labels
       UpperCamelCase										=  vocab_size
       UpperCamelCase										=  hidden_size
       UpperCamelCase										=  num_hidden_layers
       UpperCamelCase										=  num_attention_heads
       UpperCamelCase										=  intermediate_size
       UpperCamelCase										=  hidden_act
       UpperCamelCase										=  hidden_dropout_prob
       UpperCamelCase										=  attention_probs_dropout_prob
       UpperCamelCase										=  max_position_embeddings
       UpperCamelCase										=  eos_token_id
       UpperCamelCase										=  pad_token_id
       UpperCamelCase										=  bos_token_id
       UpperCamelCase										=  initializer_range







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

       UpperCamelCase										=  shift_tokens_right(lowerCamelCase_					,				1					,				2)

       UpperCamelCase										=  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=lowerCamelCase_					,				)
       UpperCamelCase										=  prepare_marian_inputs_dict(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)
       return config, inputs_dict







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase					,  UpperCamelCase										=  self.prepare_config_and_inputs()
       return config, inputs_dict







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Any:
       UpperCamelCase										=  2_0
       UpperCamelCase										=  model_class_name(lowerCamelCase_)

       UpperCamelCase										=  model.encode(inputs_dict['''input_ids'''])

       UpperCamelCase					,  UpperCamelCase										=  (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCamelCase										=  model.init_cache(decoder_input_ids.shape[0]					,				lowerCamelCase_					,				lowerCamelCase_)
       UpperCamelCase										=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,				dtype='''i4''')

       UpperCamelCase										=  jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]					,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,				)
       UpperCamelCase										=  model.decode(
           decoder_input_ids[:, :-1]					,				lowerCamelCase_					,				decoder_attention_mask=lowerCamelCase_					,				past_key_values=lowerCamelCase_					,				decoder_position_ids=lowerCamelCase_					,				)

       UpperCamelCase										=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,				dtype='''i4''')
       UpperCamelCase										=  model.decode(
           decoder_input_ids[:, -1:]					,				lowerCamelCase_					,				decoder_attention_mask=lowerCamelCase_					,				past_key_values=outputs_cache.past_key_values					,				decoder_position_ids=lowerCamelCase_					,				)

       UpperCamelCase										=  model.decode(lowerCamelCase_					,				lowerCamelCase_)

       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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      List[Any]:
       UpperCamelCase										=  2_0
       UpperCamelCase										=  model_class_name(lowerCamelCase_)

       UpperCamelCase										=  model.encode(inputs_dict['''input_ids'''])

       UpperCamelCase					,  UpperCamelCase										=  (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

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

       UpperCamelCase										=  model.init_cache(decoder_input_ids.shape[0]					,				lowerCamelCase_					,				lowerCamelCase_)
       UpperCamelCase										=  jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]					,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,				)

       UpperCamelCase										=  model.decode(
           decoder_input_ids[:, :-1]					,				lowerCamelCase_					,				decoder_attention_mask=lowerCamelCase_					,				past_key_values=lowerCamelCase_					,				decoder_position_ids=lowerCamelCase_					,				)
       UpperCamelCase										=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,				dtype='''i4''')
       UpperCamelCase										=  model.decode(
           decoder_input_ids[:, -1:]					,				lowerCamelCase_					,				past_key_values=outputs_cache.past_key_values					,				decoder_attention_mask=lowerCamelCase_					,				decoder_position_ids=lowerCamelCase_					,				)

       UpperCamelCase										=  model.decode(lowerCamelCase_					,				lowerCamelCase_					,				decoder_attention_mask=lowerCamelCase_)

       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_flax
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase  ,   lowerCamelCase_ ):







   """simple docstring"""




   A_										=					True
   A_										=					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   A_										=					(FlaxMarianMTModel,) if is_flax_available() else ()







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  FlaxMarianModelTester(self)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase					,  UpperCamelCase										=  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(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       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__):
               UpperCamelCase										=  self._prepare_for_class(lowerCamelCase_					,				lowerCamelCase_)
               UpperCamelCase										=  model_class(lowerCamelCase_)

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

               with self.subTest('''JIT Enabled'''):
                   UpperCamelCase										=  encode_jitted(**lowerCamelCase_).to_tuple()

               with self.subTest('''JIT Disabled'''):
                   with jax.disable_jit():
                       UpperCamelCase										=  encode_jitted(**lowerCamelCase_).to_tuple()

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







   def      UpperCAmelCase__ (		self)							->      Any:
       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__):
               UpperCamelCase										=  model_class(lowerCamelCase_)
               UpperCamelCase										=  model.encode(inputs_dict['''input_ids''']					,				inputs_dict['''attention_mask'''])

               UpperCamelCase										=  {
                   '''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(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_):
                   return model.decode(
                       decoder_input_ids=lowerCamelCase_					,				decoder_attention_mask=lowerCamelCase_					,				encoder_outputs=lowerCamelCase_					,				)

               with self.subTest('''JIT Enabled'''):
                   UpperCamelCase										=  decode_jitted(**lowerCamelCase_).to_tuple()

               with self.subTest('''JIT Disabled'''):
                   with jax.disable_jit():
                       UpperCamelCase										=  decode_jitted(**lowerCamelCase_).to_tuple()

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







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

@require_flax
@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   A_										=					None
   A_										=					None







   @classmethod
   def      UpperCAmelCase__ (		cls)							->      None:
       UpperCamelCase										=  F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
       return cls







   @cached_property
   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       return MarianTokenizer.from_pretrained(self.model_name)







   @property
   def      UpperCAmelCase__ (		self)							->      int:
       return self.tokenizer.eos_token_id







   @cached_property
   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  FlaxMarianMTModel.from_pretrained(self.model_name)
       self.assertEqual(model.config.decoder_start_token_id					,				model.config.pad_token_id)
       return model







   def      UpperCAmelCase__ (		self					,				**lowerCamelCase_)							->      Any:
       UpperCamelCase										=  self.translate_src_text(**lowerCamelCase_)
       self.assertListEqual(self.expected_text					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				**lowerCamelCase_)							->      Union[str, Any]:
       UpperCamelCase										=  self.tokenizer(self.src_text					,				padding=lowerCamelCase_					,				return_tensors='''np'''					,				**lowerCamelCase_)
       UpperCamelCase										=  self.model.generate(
           model_inputs.input_ids					,				attention_mask=model_inputs.attention_mask					,				num_beams=2					,				max_length=1_2_8					,				).sequences
       UpperCamelCase										=  self.tokenizer.batch_decode(lowerCamelCase_					,				skip_special_tokens=lowerCamelCase_)
       return generated_words

@require_flax
@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''en'''
   A_										=					'''fr'''
   A_										=					[
       '''I am a small frog.''',
       '''Now I can forget the 100 words of german that I know.''',
   ]
   A_										=					[
       '''Je suis une petite grenouille.''',
       '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
   ]







   @slow
   def      UpperCAmelCase__ (		self)							->      Tuple:
       self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''fr'''
   A_										=					'''en'''
   A_										=					[
       '''Donnez moi le micro.''',
       '''Tom et Mary étaient assis à une table.''',  # Accents
   ]
   A_										=					[
       '''Give me the microphone.''',
       '''Tom and Mary were sitting at a table.''',
   ]







   @slow
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''mt'''
   A_										=					'''en'''
   A_										=					['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
   A_										=					['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']







   @slow
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''en'''
   A_										=					'''de'''
   A_										=					[
       '''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.''',
   ]
   A_										=					[
       '''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)							->      Any:
       self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''en'''
   A_										=					'''zh'''
   A_										=					['''My name is Wolfgang and I live in Berlin''']
   A_										=					['''我叫沃尔夫冈 我住在柏林''']







   @slow
   def      UpperCAmelCase__ (		self)							->      Tuple:
       self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''ru'''
   A_										=					'''fr'''
   A_										=					['''Он показал мне рукопись своей новой пьесы.''']
   A_										=					['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']







   @slow
   def      UpperCAmelCase__ (		self)							->      Dict:
       self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''en'''
   A_										=					'''ROMANCE'''
   A_										=					[
       '''>>fr<< Don\'t spend so much time watching TV.''',
       '''>>pt<< Your message has been sent.''',
       '''>>es<< He\'s two years older than me.''',
   ]
   A_										=					[
       '''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)							->      int:
       self._assert_generated_batch_equal_expected()





"""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     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  Dataset.from_dict({'''i''': range(17						)}						)
    UpperCamelCase										=  len(_lowercase						)
    UpperCamelCase										=  3
    UpperCamelCase										=  [
        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   (							):


    """simple docstring"""







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

    UpperCamelCase										=  3
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    UpperCamelCase										=  [
        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						):


    """simple docstring"""







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

    UpperCamelCase										=  3
    UpperCamelCase										=  shards_per_node * world_size
    UpperCamelCase										=  {'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(_lowercase						)]}
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase ,gen_kwargs=_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    assert full_ds.n_shards == world_size * shards_per_node
    UpperCamelCase										=  [
        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   (							):


    """simple docstring"""







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

    UpperCamelCase										=  2
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)

    UpperCamelCase										=  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()

    UpperCamelCase										=  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						):


    """simple docstring"""







    UpperCamelCase										=  2
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n    '.split()
    UpperCamelCase										=  [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						):


    """simple docstring"""







    UpperCamelCase										=  True
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)