






'''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
      __A     						= '''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 							_SCREAMING_SNAKE_CASE	(					A	:							Tuple	,		A	:							List[Any]	,		A	:							Union[str, Any]=None	,		A	:							List[str]=None	,		A	:							Optional[Any]=None	,		A	:							Optional[int]=None	,		A	:							Any=None	,		A	:							Optional[Any]=None	,		)     ->			List[Any]:







   """simple docstring"""
   if attention_mask is None:
      __snake_case					:   int  				=			np.where(input_ids != config.pad_token_id	,		1	,		0	)
   if decoder_attention_mask is None:
      __snake_case					:   Any  				=			np.where(decoder_input_ids != config.pad_token_id	,		1	,		0	)
   if head_mask is None:
      __snake_case					:   Dict  				=			np.ones((config.encoder_layers, config.encoder_attention_heads)	)
   if decoder_head_mask is None:
      __snake_case					:   str  				=			np.ones((config.decoder_layers, config.decoder_attention_heads)	)
   if cross_attn_head_mask is None:
      __snake_case					:   Optional[int]  				=			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 a_     (     unittest.TestCase				):







   def __init__(self       , __a       , __a=1_3       , __a=7       , __a=True       , __a=False       , __a=9_9       , __a=1_6       , __a=2       , __a=4       , __a=4       , __a="gelu"       , __a=0.1       , __a=0.1       , __a=3_2       , __a=2       , __a=1       , __a=0       , __a=2       , __a=0.02       , )      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   int  				=			parent
      __snake_case					:   List[str]  				=			batch_size
      __snake_case					:   Tuple  				=			seq_length
      __snake_case					:   str  				=			is_training
      __snake_case					:   List[Any]  				=			use_labels
      __snake_case					:   List[str]  				=			vocab_size
      __snake_case					:   Optional[int]  				=			hidden_size
      __snake_case					:   List[Any]  				=			num_hidden_layers
      __snake_case					:   Dict  				=			num_attention_heads
      __snake_case					:   Dict  				=			intermediate_size
      __snake_case					:   Dict  				=			hidden_act
      __snake_case					:   Optional[int]  				=			hidden_dropout_prob
      __snake_case					:   List[Any]  				=			attention_probs_dropout_prob
      __snake_case					:   int  				=			max_position_embeddings
      __snake_case					:   Tuple  				=			eos_token_id
      __snake_case					:   List[Any]  				=			pad_token_id
      __snake_case					:   Union[str, Any]  				=			bos_token_id
      __snake_case					:   Dict  				=			decoder_start_token_id
      __snake_case					:   str  				=			initializer_range







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






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

      __snake_case					:   Tuple  				=			shift_tokens_right(_SCREAMING_SNAKE_CASE       , 1)

      __snake_case					:   Optional[Any]  				=			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=_SCREAMING_SNAKE_CASE       , )
      __snake_case					:   Dict  				=			prepare_mbart_inputs_dict(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE)
      return config, inputs_dict







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			self.prepare_config_and_inputs()
      return config, inputs_dict







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   int  				=			2_0
      __snake_case					:   Optional[int]  				=			model_class_name(_SCREAMING_SNAKE_CASE)

      __snake_case					:   Tuple  				=			model.encode(inputs_dict['input_ids'])

      __snake_case					:   List[Any]  				=			(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      __snake_case					:   str  				=			model.init_cache(decoder_input_ids.shape[0]       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE)
      __snake_case					:   List[str]  				=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       , dtype='i4')

      __snake_case					:   Optional[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					:   Tuple  				=			model.decode(
          decoder_input_ids[:, :-1]       , _SCREAMING_SNAKE_CASE       , decoder_attention_mask=_SCREAMING_SNAKE_CASE       , past_key_values=_SCREAMING_SNAKE_CASE       , decoder_position_ids=_SCREAMING_SNAKE_CASE       , )

      __snake_case					:   Union[str, Any]  				=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       , dtype='i4')
      __snake_case					:   List[str]  				=			model.decode(
          decoder_input_ids[:, -1:]       , _SCREAMING_SNAKE_CASE       , decoder_attention_mask=_SCREAMING_SNAKE_CASE       , past_key_values=outputs_cache.past_key_values       , decoder_position_ids=_SCREAMING_SNAKE_CASE       , )

      __snake_case					:   Any  				=			model.decode(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE)

      __snake_case					:   List[str]  				=			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      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			2_0
      __snake_case					:   List[str]  				=			model_class_name(_SCREAMING_SNAKE_CASE)

      __snake_case					:   str  				=			model.encode(inputs_dict['input_ids'])

      __snake_case					:   List[str]  				=			(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      __snake_case					:   List[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					:   Tuple  				=			model.init_cache(decoder_input_ids.shape[0]       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE)
      __snake_case					:   Union[str, Any]  				=			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					:   List[str]  				=			model.decode(
          decoder_input_ids[:, :-1]       , _SCREAMING_SNAKE_CASE       , decoder_attention_mask=_SCREAMING_SNAKE_CASE       , past_key_values=_SCREAMING_SNAKE_CASE       , decoder_position_ids=_SCREAMING_SNAKE_CASE       , )
      __snake_case					:   Optional[int]  				=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       , dtype='i4')
      __snake_case					:   List[Any]  				=			model.decode(
          decoder_input_ids[:, -1:]       , _SCREAMING_SNAKE_CASE       , past_key_values=outputs_cache.past_key_values       , decoder_attention_mask=_SCREAMING_SNAKE_CASE       , decoder_position_ids=_SCREAMING_SNAKE_CASE       , )

      __snake_case					:   Optional[int]  				=			model.decode(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , decoder_attention_mask=_SCREAMING_SNAKE_CASE)

      __snake_case					:   Dict  				=			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 a_     (     unittest.TestCase				):
   _snake_case	   =				99







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   Dict  				=			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       , )

      __snake_case					:   Union[str, Any]  				=			input_ids.shape[0]
      __snake_case					:   Dict  				=			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      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   Dict  				=			self._get_config_and_data()
      __snake_case					:   Any  				=			FlaxMBartForSequenceClassification(_SCREAMING_SNAKE_CASE)
      __snake_case					:   Dict  				=			model(input_ids=_SCREAMING_SNAKE_CASE       , decoder_input_ids=_SCREAMING_SNAKE_CASE)
      __snake_case					:   List[str]  				=			(batch_size, config.num_labels)
      self.assertEqual(outputs['logits'].shape       , _SCREAMING_SNAKE_CASE)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self._get_config_and_data()
      __snake_case					:   Optional[Any]  				=			FlaxMBartForQuestionAnswering(_SCREAMING_SNAKE_CASE)
      __snake_case					:   Dict  				=			model(input_ids=_SCREAMING_SNAKE_CASE)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self._get_config_and_data()
      __snake_case					:   List[Any]  				=			FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE)
      __snake_case					:   int  				=			lm_model(input_ids=_SCREAMING_SNAKE_CASE)
      __snake_case					:   Optional[Any]  				=			(batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs['logits'].shape       , _SCREAMING_SNAKE_CASE)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			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       , )
      __snake_case					:   Optional[int]  				=			FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE)
      __snake_case					:   str  				=			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)
      __snake_case					:   Union[str, Any]  				=			np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]       , dtype=np.intaa)
      __snake_case					:   int  				=			lm_model(input_ids=_SCREAMING_SNAKE_CASE       , decoder_input_ids=_SCREAMING_SNAKE_CASE)
      __snake_case					:   Optional[Any]  				=			(*summary.shape, config.vocab_size)
      self.assertEqual(outputs['logits'].shape       , _SCREAMING_SNAKE_CASE)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   int  				=			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)
      __snake_case					:   Tuple  				=			shift_tokens_right(_SCREAMING_SNAKE_CASE       , 1)
      __snake_case					:   List[Any]  				=			np.equal(_SCREAMING_SNAKE_CASE       , 1).astype(np.floataa).sum()
      __snake_case					:   Tuple  				=			np.equal(_SCREAMING_SNAKE_CASE       , 1).astype(np.floataa).sum()
      self.assertEqual(shifted.shape       , input_ids.shape)
      self.assertEqual(_SCREAMING_SNAKE_CASE       , n_pad_before - 1)
      self.assertTrue(np.equal(shifted[:, 0]       , 2).all())




@require_flax
class a_     (     _lowerCamelCase		,   unittest.TestCase		,   _lowerCamelCase				):
   _snake_case	   =				True
   _snake_case	   =				(
       (
           FlaxMBartModel,
           FlaxMBartForConditionalGeneration,
           FlaxMBartForSequenceClassification,
           FlaxMBartForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   _snake_case	   =				(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			FlaxMBartModelTester(self)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   int  				=			self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			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(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			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					:   List[Any]  				=			self._prepare_for_class(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE)
            __snake_case					:   str  				=			model_class(_SCREAMING_SNAKE_CASE)

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

            with self.subTest('JIT Enabled'):
               __snake_case					:   Tuple  				=			encode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

            with self.subTest('JIT Disabled'):
               with jax.disable_jit():
                  __snake_case					:   int  				=			encode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   List[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					:   List[str]  				=			model_class(_SCREAMING_SNAKE_CASE)
            __snake_case					:   str  				=			model.encode(inputs_dict['input_ids']       , inputs_dict['attention_mask'])

            __snake_case					:   List[Any]  				=			{
                '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(__a       , __a       , __a):
               return model.decode(
                   decoder_input_ids=_SCREAMING_SNAKE_CASE       , decoder_attention_mask=_SCREAMING_SNAKE_CASE       , encoder_outputs=_SCREAMING_SNAKE_CASE       , )

            with self.subTest('JIT Enabled'):
               __snake_case					:   Tuple  				=			decode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

            with self.subTest('JIT Disabled'):
               with jax.disable_jit():
                  __snake_case					:   Tuple  				=			decode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

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







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      for model_class_name in self.all_model_classes:
         __snake_case					:   Tuple  				=			model_class_name.from_pretrained('facebook/mbart-large-cc25'       , from_pt=_SCREAMING_SNAKE_CASE)
         # FlaxMBartForSequenceClassification expects eos token in input_ids
         __snake_case					:   Optional[int]  				=			np.ones((1, 1)) * model.config.eos_token_id
         __snake_case					:   Optional[int]  				=			model(_SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE)




@require_flax
@require_sentencepiece
@require_tokenizers
class a_     (     unittest.TestCase				):
   _snake_case	   =				[
       ' UN Chief Says There Is No Military Solution in Syria',
   ]
   _snake_case	   =				[
       'Şeful ONU declară că nu există o soluţie militară în Siria',
   ]
   _snake_case	   =				'facebook/mbart-large-en-ro'







   @cached_property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      return AutoTokenizer.from_pretrained(self.model_name)







   @cached_property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Any  				=			FlaxMBartForConditionalGeneration.from_pretrained(self.model_name       , from_pt=_SCREAMING_SNAKE_CASE)
      return model







   def      SCREAMING_SNAKE_CASE__       (self       , **__a)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			self.translate_src_text(**_SCREAMING_SNAKE_CASE)
      self.assertListEqual(self.expected_text       , _SCREAMING_SNAKE_CASE)







   def      SCREAMING_SNAKE_CASE__       (self       , **__a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Dict  				=			self.tokenizer(self.src_text       , **_SCREAMING_SNAKE_CASE       , return_tensors='np')
      __snake_case					:   Union[str, Any]  				=			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=_SCREAMING_SNAKE_CASE       , num_beams=2       , ).sequences
      __snake_case					:   Tuple  				=			self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE       , skip_special_tokens=_SCREAMING_SNAKE_CASE)
      return generated_words







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      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        __lowerCAmelCase							():
	__lowerCAmelCase     :		Optional[int]		     = Dataset.from_dict({'i': range(17						)}						)
	__lowerCAmelCase     :		List[Any]		     = len(_UpperCamelCase						)
	__lowerCAmelCase     :		Tuple		     = 3
	__lowerCAmelCase     :		List[Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(_UpperCamelCase						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[Any]		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		List[Any]		     = len(list(_UpperCamelCase						)						)
	__lowerCAmelCase     :		str		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(list(_UpperCamelCase						)						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize('shards_per_node'			,    [1, 2, 3]						)
def        __lowerCAmelCase							(_UpperCamelCase						):
	def gen(_UpperCamelCase						):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[int]		     = shards_per_node * world_size
	__lowerCAmelCase     :		Dict		     = {'shards': [F"shard_{shard_idx}.txt" for shard_idx in range(_UpperCamelCase						)]}
	__lowerCAmelCase     :		Dict		     = IterableDataset.from_generator(_UpperCamelCase			,    gen_kwargs=_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = len(list(_UpperCamelCase						)						)
	assert full_ds.n_shards == world_size * shards_per_node
	__lowerCAmelCase     :		Union[str, Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(_UpperCamelCase						)						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		str		     = 2
	__lowerCAmelCase     :		Any		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = len(list(_UpperCamelCase						)						)

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

	__lowerCAmelCase     :		List[str]		     = split_dataset_by_node(full_ds.shuffle(seed=42						)			,    rank=0			,    world_size=_UpperCamelCase						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(full_ds.shuffle()			,    rank=0			,    world_size=_UpperCamelCase						)
@pytest.mark.parametrize('streaming'			,    [False, True]						)
@require_torch
@pytest.mark.skipif(os.name == 'nt'			,    reason='execute_subprocess_async doesn\'t support windows'						)
@pytest.mark.integration
def        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = 2
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		List[Any]		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		int		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		Tuple		     = F"\n        --streaming={streaming}\n    ".split()
	__lowerCAmelCase     :		Optional[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)
@pytest.mark.parametrize(
    'nproc_per_node, num_workers'			,    [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]			,    )
@require_torch
@pytest.mark.skipif(os.name == 'nt'			,    reason='execute_subprocess_async doesn\'t support windows'						)
@pytest.mark.integration
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = True
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		Tuple		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		Optional[Any]		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)