




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
lowerCAmelCase_		      = pytest.mark.integration


lowerCAmelCase_		      = {'comet'}
lowerCAmelCase_		      = importlib.util.find_spec('fairseq') is not None

lowerCAmelCase_		      = {'code_eval'}
lowerCAmelCase_		      = os.name == 'nt'

lowerCAmelCase_		      = {'bertscore', 'frugalscore', 'perplexity'}
lowerCAmelCase_		      = importlib.util.find_spec('transformers') is not None






def 					snake_case(				__magic_name__    ) ->							int:





     '''simple docstring'''


     @wraps(__magic_name__    )
     def wrapper(self ,		__magic_name__    ):
          if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
               self.skipTest('''\"test requires Fairseq\"'''    )
          else:
               test_case(self ,		__magic_name__    )

     return wrapper






def 					snake_case(				__magic_name__    ) ->							str:





     '''simple docstring'''


     @wraps(__magic_name__    )
     def wrapper(self ,		__magic_name__    ):
          if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
               self.skipTest('''\"test requires transformers\"'''    )
          else:
               test_case(self ,		__magic_name__    )

     return wrapper






def 					snake_case(				__magic_name__    ) ->							Dict:





     '''simple docstring'''


     @wraps(__magic_name__    )
     def wrapper(self ,		__magic_name__    ):
          if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
               self.skipTest('''\"test not supported on Windows\"'''    )
          else:
               test_case(self ,		__magic_name__    )

     return wrapper






def 					snake_case(				) ->							List[str]:





     '''simple docstring'''


     lowercase					:   Optional[Any]								= [metric_dir.split(os.sep    )[-2] for metric_dir in glob.glob('''./metrics/*/'''    )]
     return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names()      )
@for_all_test_methods(
    _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase      )
@local
class 						_A					(  parameterized.TestCase      ):
  _UpperCamelCase   :			int =			{}
  _UpperCamelCase   :			List[str] =			None






  @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''      )
  @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''      )
  def 					__a			(  self		:	str      ,						_A		:	Tuple      )		->    Union[str, Any]:






       """simple docstring"""







       lowercase					:   List[Any]								= "[...]"
       lowercase					:   str								= importlib.import_module(
           datasets.load.metric_module_factory(os.path.join('''metrics'''      ,						snake_case__      )      ).module_path      )
       lowercase					:   List[Any]								= datasets.load.import_main_class(metric_module.__name__      ,						dataset=snake_case__      )
       # check parameters
       lowercase					:   Optional[Any]								= inspect.signature(metric._compute      ).parameters
       self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()      )      )  # no **kwargs
       # run doctest
       with self.patch_intensive_calls(snake_case__      ,						metric_module.__name__      ):
            with self.use_local_metrics():
                 try:
                      lowercase					:   Optional[Any]								= doctest.testmod(snake_case__      ,						verbose=snake_case__      ,						raise_on_error=snake_case__      )
                 except doctest.UnexpectedException as e:
                      raise e.exc_info[1]  # raise the exception that doctest caught
       self.assertEqual(results.failed      ,						0      )
       self.assertGreater(results.attempted      ,						1      )






  @slow
  def 					__a			(  self		:	Dict      ,						_A		:	Dict      )		->    List[str]:






       """simple docstring"""







       lowercase					:   List[Any]								= "[...]"
       lowercase					:   Dict								= importlib.import_module(
           datasets.load.metric_module_factory(os.path.join('''metrics'''      ,						snake_case__      )      ).module_path      )
       # run doctest
       with self.use_local_metrics():
            lowercase					:   List[Any]								= doctest.testmod(snake_case__      ,						verbose=snake_case__      ,						raise_on_error=snake_case__      )
       self.assertEqual(results.failed      ,						0      )
       self.assertGreater(results.attempted      ,						1      )






  @contextmanager
  def 					__a			(  self		:	Union[str, Any]      ,						_A		:	Optional[Any]      ,						_A		:	Tuple      )		->    Optional[int]:






       """simple docstring"""







       if metric_name in self.INTENSIVE_CALLS_PATCHER:
            with self.INTENSIVE_CALLS_PATCHER[metric_name](snake_case__      ):
                 yield
       else:
            yield






  @contextmanager
  def 					__a			(  self		:	str      )		->    Optional[int]:






       """simple docstring"""







       def load_local_metric(_A		:	Dict      ,						*_A		:	Dict      ,						**_A		:	str      ):
            return load_metric(os.path.join('''metrics'''      ,						snake_case__      )      ,						*snake_case__      ,						**snake_case__      )

       with patch('''datasets.load_metric'''      ) as mock_load_metric:
            lowercase					:   Dict								= load_local_metric
            yield






  @classmethod
  def 					__a			(  cls		:	str      ,						_A		:	Any      )		->    Tuple:






       """simple docstring"""







       def wrapper(_A		:	Optional[int]      ):
            lowercase					:   Dict								= contextmanager(snake_case__      )
            lowercase					:   List[str]								= patcher
            return patcher

       return wrapper






@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''    )
def 					snake_case(				__magic_name__    ) ->							str:





     '''simple docstring'''


     import tensorflow.compat.va as tf
     from bleurt.score import Predictor

     tf.flags.DEFINE_string('''sv''' ,		'''''' ,		''''''    )  # handle pytest cli flags
     class 						_A					(  _lowerCamelCase      ):






       def 					__a			(  self		:	int      ,						_A		:	Any      )		->    Tuple:






            """simple docstring"""







            assert len(input_dict['''input_ids''']      ) == 2
            return np.array([1.03, 1.04]      )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
     with patch('''bleurt.score._create_predictor'''    ) as mock_create_predictor:
          lowercase					:   Optional[Any]								= MockedPredictor()
          yield






@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''    )
def 					snake_case(				__magic_name__    ) ->							Union[str, Any]:





     '''simple docstring'''


     import torch

     def bert_cos_score_idf(__magic_name__ ,		__magic_name__ ,		*__magic_name__ ,		**__magic_name__    ):
          return torch.tensor([[1.0, 1.0, 1.0]] * len(__magic_name__    )    )

     # mock get_model which is supposed to do download a bert model
     # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
     with patch('''bert_score.scorer.get_model'''    ), patch(
         '''bert_score.scorer.bert_cos_score_idf'''    ) as mock_bert_cos_score_idf:
          lowercase					:   Tuple								= bert_cos_score_idf
          yield






@LocalMetricTest.register_intensive_calls_patcher('''comet'''    )
def 					snake_case(				__magic_name__    ) ->							Tuple:





     '''simple docstring'''


     def load_from_checkpoint(__magic_name__    ):
          class 						_A					:






            def 					__a			(  self		:	Union[str, Any]      ,						_A		:	Optional[Any]      ,						*_A		:	Tuple      ,						**_A		:	Optional[Any]      )		->    Dict:






                 """simple docstring"""







                 assert len(snake_case__      ) == 2
                 lowercase					:   Union[str, Any]								= [0.19, 0.92]
                 return scores, sum(snake_case__      ) / len(snake_case__      )


          return Model()

     # mock load_from_checkpoint which is supposed to do download a bert model
     # mock load_from_checkpoint which is supposed to do download a bert model
     with patch('''comet.download_model'''    ) as mock_download_model:
          lowercase					:   Tuple								= None
          with patch('''comet.load_from_checkpoint'''    ) as mock_load_from_checkpoint:
               lowercase					:   List[Any]								= load_from_checkpoint
               yield






def 					snake_case(				) ->							Tuple:





     '''simple docstring'''


     lowercase					:   int								= load_metric(os.path.join('''metrics''' ,		'''seqeval'''    )    )
     lowercase					:   List[Any]								= "ERROR"
     lowercase					:   Optional[int]								= F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
     with pytest.raises(__magic_name__ ,		match=re.escape(__magic_name__    )    ):
          metric.compute(predictions=[] ,		references=[] ,		scheme=__magic_name__    )



"""simple docstring"""







import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__=0.01		,     snake_case__=1_000						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							p_stop
         lowerCAmelCase     :     Optional[Any]								=							max_length







       def __iter__(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							0
         lowerCAmelCase     :     Tuple								=							False
         while not stop and count < self.max_length:
           yield count
           count += 1
           lowerCAmelCase     :     Dict								=							random.random() < self.p_stop


class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__=False		,     snake_case__=True						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							[
             BatchSamplerShard(snake_case__		,     2		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)
             for i in range(2						)
         ]
         lowerCAmelCase     :     Any								=							[list(snake_case__						) for batch_sampler_shard in batch_sampler_shards]
         if not split_batches:
           self.assertListEqual([len(snake_case__						) for shard in batch_sampler_shards]		,     [len(snake_case__						) for e in expected]						)
         self.assertListEqual(snake_case__		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							BatchSampler(range(24						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Any								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Tuple								=							BatchSampler(range(24						)		,     batch_size=3		,     drop_last=snake_case__						)
         # Expected shouldn't change
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         lowerCAmelCase     :     Union[str, Any]								=							BatchSampler(range(21						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[str]								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Tuple								=							BatchSampler(range(21						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Any								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         lowerCAmelCase     :     List[str]								=							BatchSampler(range(22						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Dict								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Dict								=							BatchSampler(range(22						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         lowerCAmelCase     :     Any								=							BatchSampler(range(20						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Tuple								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[str]								=							BatchSampler(range(20						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[str]								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         # Check the shards when the dataset is very small.
         lowerCAmelCase     :     Dict								=							BatchSampler(range(2						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							[[[0, 1, 0]], [[1, 0, 1]]]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Optional[Any]								=							BatchSampler(range(2						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Any								=							[[], []]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							BatchSampler(range(24						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     int								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__						)

         lowerCAmelCase     :     Dict								=							BatchSampler(range(24						)		,     batch_size=4		,     drop_last=snake_case__						)
         # Expected shouldn't change
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__						)

         # Check the shards when the dataset is not a round multiple of batch size.
         lowerCAmelCase     :     Optional[int]								=							BatchSampler(range(22						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__						)

         lowerCAmelCase     :     List[Any]								=							BatchSampler(range(22						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[str]								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__						)

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         lowerCAmelCase     :     Tuple								=							BatchSampler(range(21						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[str]								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__						)

         lowerCAmelCase     :     Any								=							BatchSampler(range(21						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__						)

         # Check the shards when the dataset is very small.
         lowerCAmelCase     :     Optional[Any]								=							BatchSampler(range(2						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							[[[0, 1]], [[0, 1]]]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__						)

         lowerCAmelCase     :     Optional[Any]								=							BatchSampler(range(2						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							[[], []]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							BatchSampler(range(24						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Any								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     Optional[Any]								=							BatchSampler(range(24						)		,     batch_size=3		,     drop_last=snake_case__						)
         # Expected shouldn't change
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         lowerCAmelCase     :     Dict								=							BatchSampler(range(21						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Dict								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     int								=							BatchSampler(range(21						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[str]								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         lowerCAmelCase     :     str								=							BatchSampler(range(22						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     List[str]								=							BatchSampler(range(22						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         lowerCAmelCase     :     str								=							BatchSampler(range(20						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     Any								=							BatchSampler(range(20						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							[
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         # Check the shards when the dataset is very small.
         lowerCAmelCase     :     Optional[int]								=							BatchSampler(range(2						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[str]								=							[[[0, 1]], []]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     str								=							BatchSampler(range(2						)		,     batch_size=3		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							[[], []]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     even_batches=snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							BatchSampler(range(24						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Dict								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     List[Any]								=							BatchSampler(range(24						)		,     batch_size=4		,     drop_last=snake_case__						)
         # Expected shouldn't change
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)

         # Check the shards when the dataset is not a round multiple of batch size.
         lowerCAmelCase     :     Tuple								=							BatchSampler(range(22						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[str]								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     int								=							BatchSampler(range(22						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     str								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         lowerCAmelCase     :     int								=							BatchSampler(range(21						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     str								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     str								=							BatchSampler(range(21						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							[
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)

         # Check the shards when the dataset is very small.
         lowerCAmelCase     :     Optional[Any]								=							BatchSampler(range(2						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     int								=							[[[0, 1]], []]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)

         lowerCAmelCase     :     Dict								=							BatchSampler(range(2						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[str]								=							[[], []]
         self.check_batch_sampler_shards(snake_case__		,     snake_case__		,     split_batches=snake_case__		,     even_batches=snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
         lowerCAmelCase     :     Tuple								=							[BatchSamplerShard(snake_case__		,     2		,     snake_case__		,     even_batches=snake_case__						) for i in range(2						)]

         self.assertEqual(len(batch_sampler_shards[0]						)		,     3						)
         self.assertEqual(len(batch_sampler_shards[1]						)		,     2						)

         self.assertListEqual(list(batch_sampler_shards[0]						)		,     [[0, 1, 2], [5, 6, 7, 8], [12, 13]]						)
         self.assertListEqual(list(batch_sampler_shards[1]						)		,     [[3, 4], [9, 10, 11]]						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__=False		,     snake_case__=2		,     snake_case__=False						):






         """simple docstring"""





         random.seed(snake_case__						)
         lowerCAmelCase     :     List[str]								=							list(snake_case__						)

         lowerCAmelCase     :     Optional[int]								=							[
             IterableDatasetShard(
                 snake_case__		,     batch_size=snake_case__		,     drop_last=snake_case__		,     num_processes=snake_case__		,     process_index=snake_case__		,     split_batches=snake_case__		,     )
             for i in range(snake_case__						)
         ]
         lowerCAmelCase     :     str								=							[]
         for iterable_dataset_shard in iterable_dataset_shards:
           # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
           random.seed(snake_case__						)
           iterable_dataset_lists.append(list(snake_case__						)						)

         lowerCAmelCase     :     List[Any]								=							batch_size // num_processes if split_batches else batch_size
         # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
         lowerCAmelCase     :     Tuple								=							iterable_dataset_lists[0]
         for l in iterable_dataset_lists[1:]:
           self.assertEqual(len(snake_case__						)		,     len(snake_case__						)						)
           self.assertTrue(len(snake_case__						) % shard_batch_size == 0						)

         lowerCAmelCase     :     List[Any]								=							[]
         for idx in range(0		,     len(snake_case__						)		,     snake_case__						):
           for l in iterable_dataset_lists:
             observed += l[idx : idx + shard_batch_size]

         if not drop_last:
           while len(snake_case__						) < len(snake_case__						):
             reference += reference
         self.assertListEqual(snake_case__		,     reference[: len(snake_case__						)]						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							42
         lowerCAmelCase     :     Tuple								=							RandomIterableDataset()

         self.check_iterable_dataset_shards(snake_case__		,     snake_case__		,     batch_size=4		,     drop_last=snake_case__		,     split_batches=snake_case__						)
         self.check_iterable_dataset_shards(snake_case__		,     snake_case__		,     batch_size=4		,     drop_last=snake_case__		,     split_batches=snake_case__						)
         self.check_iterable_dataset_shards(snake_case__		,     snake_case__		,     batch_size=4		,     drop_last=snake_case__		,     split_batches=snake_case__						)
         self.check_iterable_dataset_shards(snake_case__		,     snake_case__		,     batch_size=4		,     drop_last=snake_case__		,     split_batches=snake_case__						)

         # Edge case with a very small dataset
         lowerCAmelCase     :     List[str]								=							RandomIterableDataset(max_length=2						)

         self.check_iterable_dataset_shards(snake_case__		,     snake_case__		,     batch_size=4		,     drop_last=snake_case__		,     split_batches=snake_case__						)
         self.check_iterable_dataset_shards(snake_case__		,     snake_case__		,     batch_size=4		,     drop_last=snake_case__		,     split_batches=snake_case__						)
         self.check_iterable_dataset_shards(snake_case__		,     snake_case__		,     batch_size=4		,     drop_last=snake_case__		,     split_batches=snake_case__						)
         self.check_iterable_dataset_shards(snake_case__		,     snake_case__		,     batch_size=4		,     drop_last=snake_case__		,     split_batches=snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							BatchSampler(range(16						)		,     batch_size=4		,     drop_last=snake_case__						)
         lowerCAmelCase     :     List[Any]								=							SkipBatchSampler(snake_case__		,     2						)
         self.assertListEqual(list(snake_case__						)		,     [[8, 9, 10, 11], [12, 13, 14, 15]]						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							SkipDataLoader(list(range(16						)						)		,     batch_size=4		,     skip_batches=2						)
         self.assertListEqual([t.tolist() for t in dataloader]		,     [[8, 9, 10, 11], [12, 13, 14, 15]]						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							DataLoader(list(range(16						)						)		,     batch_size=4						)
         lowerCAmelCase     :     Optional[int]								=							skip_first_batches(snake_case__		,     num_batches=2						)
         self.assertListEqual([t.tolist() for t in new_dataloader]		,     [[8, 9, 10, 11], [12, 13, 14, 15]]						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							DataLoaderShard(list(range(16						)						)		,     batch_size=4						)
         for idx, _ in enumerate(snake_case__						):
           self.assertEqual(dataloader.end_of_dataloader		,     idx == 3						)

         # Test it also works on the second iteration
         for idx, _ in enumerate(snake_case__						):
           self.assertEqual(dataloader.end_of_dataloader		,     idx == 3						)







       def        lowercase__							(						self						):






         """simple docstring"""





         Accelerator()
         lowerCAmelCase     :     Dict								=							DataLoaderDispatcher(range(16						)		,     batch_size=4						)
         for idx, _ in enumerate(snake_case__						):
           self.assertEqual(dataloader.end_of_dataloader		,     idx == 3						)

         # Test it also works on the second iteration
         for idx, _ in enumerate(snake_case__						):
           self.assertEqual(dataloader.end_of_dataloader		,     idx == 3						)





