




"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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 AutoTokenizer
  from transformers.models.mbart.modeling_flax_mbart import (
      FlaxMBartForConditionalGeneration,
      FlaxMBartForQuestionAnswering,
      FlaxMBartForSequenceClassification,
      FlaxMBartModel,
      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": decoder_attention_mask,
    }

class 							snake_case_ (				unittest.TestCase ):







   """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_=2					,				lowerCamelCase_=0.02					,				)							->      Dict:
       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										=  decoder_start_token_id
       UpperCamelCase										=  initializer_range







   def      UpperCAmelCase__ (		self)							->      Tuple:
       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)

       UpperCamelCase										=  MBartConfig(
           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					,				decoder_start_token_id=self.decoder_start_token_id					,				initializer_range=self.initializer_range					,				use_cache=lowerCamelCase_					,				)
       UpperCamelCase										=  prepare_mbart_inputs_dict(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)
       return config, inputs_dict







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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[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_)							->      Optional[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_ (				unittest.TestCase ):







   """simple docstring"""




   A_										=					99







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  np.array(
           [
               [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
               [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
               [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
               [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
               [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
               [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
               [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
               [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
               [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
               [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
               [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
               [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
               [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
           ]					,				dtype=np.intaa					,				)

       UpperCamelCase										=  input_ids.shape[0]
       UpperCamelCase										=  MBartConfig(
           vocab_size=self.vocab_size					,				d_model=2_4					,				encoder_layers=2					,				decoder_layers=2					,				encoder_attention_heads=2					,				decoder_attention_heads=2					,				encoder_ffn_dim=3_2					,				decoder_ffn_dim=3_2					,				max_position_embeddings=4_8					,				eos_token_id=2					,				pad_token_id=1					,				bos_token_id=0					,				)
       return config, input_ids, batch_size







   def      UpperCAmelCase__ (		self)							->      Tuple:
       UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  self._get_config_and_data()
       UpperCamelCase										=  FlaxMBartForSequenceClassification(lowerCamelCase_)
       UpperCamelCase										=  model(input_ids=lowerCamelCase_					,				decoder_input_ids=lowerCamelCase_)
       UpperCamelCase										=  (batch_size, config.num_labels)
       self.assertEqual(outputs['''logits'''].shape					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  self._get_config_and_data()
       UpperCamelCase										=  FlaxMBartForQuestionAnswering(lowerCamelCase_)
       UpperCamelCase										=  model(input_ids=lowerCamelCase_)

       self.assertEqual(outputs['''start_logits'''].shape					,				input_ids.shape)
       self.assertEqual(outputs['''end_logits'''].shape					,				input_ids.shape)







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  self._get_config_and_data()
       UpperCamelCase										=  FlaxMBartForConditionalGeneration(lowerCamelCase_)
       UpperCamelCase										=  lm_model(input_ids=lowerCamelCase_)
       UpperCamelCase										=  (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      int:
       UpperCamelCase										=  MBartConfig(
           vocab_size=self.vocab_size					,				d_model=1_4					,				encoder_layers=2					,				decoder_layers=2					,				encoder_attention_heads=2					,				decoder_attention_heads=2					,				encoder_ffn_dim=8					,				decoder_ffn_dim=8					,				max_position_embeddings=4_8					,				)
       UpperCamelCase										=  FlaxMBartForConditionalGeneration(lowerCamelCase_)
       UpperCamelCase										=  np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]					,				dtype=np.intaa)
       UpperCamelCase										=  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,				dtype=np.intaa)
       UpperCamelCase										=  lm_model(input_ids=lowerCamelCase_					,				decoder_input_ids=lowerCamelCase_)
       UpperCamelCase										=  (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]					,				dtype=np.intaa)
       UpperCamelCase										=  shift_tokens_right(lowerCamelCase_					,				1)
       UpperCamelCase										=  np.equal(lowerCamelCase_					,				1).astype(np.floataa).sum()
       UpperCamelCase										=  np.equal(lowerCamelCase_					,				1).astype(np.floataa).sum()
       self.assertEqual(shifted.shape					,				input_ids.shape)
       self.assertEqual(lowerCamelCase_					,				n_pad_before - 1)
       self.assertTrue(np.equal(shifted[:, 0]					,				2).all())

@require_flax
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase  ,   lowerCamelCase_ ):







   """simple docstring"""




   A_										=					True
   A_										=					(
       (
           FlaxMBartModel,
           FlaxMBartForConditionalGeneration,
           FlaxMBartForSequenceClassification,
           FlaxMBartForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   A_										=					(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()







   def      UpperCAmelCase__ (		self)							->      Any:
       UpperCamelCase										=  FlaxMBartModelTester(self)







   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(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Dict:
       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)							->      Optional[int]:
       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)							->      List[str]:
       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)							->      Any:
       for model_class_name in self.all_model_classes:
           UpperCamelCase										=  model_class_name.from_pretrained('''facebook/mbart-large-cc25'''					,				from_pt=lowerCamelCase_)
           # FlaxMBartForSequenceClassification 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_										=					[
       ''' UN Chief Says There Is No Military Solution in Syria''',
   ]
   A_										=					[
       '''Şeful ONU declară că nu există o soluţie militară în Siria''',
   ]
   A_										=					'''facebook/mbart-large-en-ro'''







   @cached_property
   def      UpperCAmelCase__ (		self)							->      List[str]:
       return AutoTokenizer.from_pretrained(self.model_name)







   @cached_property
   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase										=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name					,				from_pt=lowerCamelCase_)
       return model







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







   def      UpperCAmelCase__ (		self					,				**lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase										=  self.tokenizer(self.src_text					,				**lowerCamelCase_					,				return_tensors='''np''')
       UpperCamelCase										=  self.model.generate(
           model_inputs.input_ids					,				attention_mask=model_inputs.attention_mask					,				decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO''']					,				early_stopping=lowerCamelCase_					,				num_beams=2					,				).sequences
       UpperCamelCase										=  self.tokenizer.batch_decode(lowerCamelCase_					,				skip_special_tokens=lowerCamelCase_)
       return generated_words







   @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()						)