


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case   							=						'''▁'''

__snake_case   							=						{'''vocab_file''': '''spiece.model'''}

__snake_case   							=						{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

__snake_case   							=						{
    '''google/pegasus-xsum''': 5_1_2,
}


__snake_case   							=						logging.get_logger(__name__)



class 	__lowerCamelCase			(_a						):
       _lowercase											=     VOCAB_FILES_NAMES

       _lowercase											=     VOCAB_FILES_NAMES
       _lowercase											=     PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=     ["""input_ids""", """attention_mask"""]







       def __init__(			self: List[Any],A_: Dict,A_: Optional[int]="<pad>",A_: Tuple="</s>",A_: int="<unk>",A_: Tuple="<mask_2>",A_: Optional[int]="<mask_1>",A_: str=None,A_: List[Any]=103,A_: Optional[Dict[str, Any]] = None,**A_: Dict,):






            '''simple docstring'''




            __UpperCamelCase =			offset
            if additional_special_tokens is not None:
                 if not isinstance(A_,A_					):
                      raise TypeError(
                          F'''additional_special_tokens should be of type {type(A_					)}, but is'''
                          F''' {type(A_					)}'''					)

                 __UpperCamelCase =			(
                     ([mask_token_sent] + additional_special_tokens)
                     if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                     else additional_special_tokens
                 )
                 # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                 additional_special_tokens_extended += [
                     F'''<unk_{i}>''' for i in range(len(A_					),self.offset - 1					)
                 ]

                 if len(set(A_					)					) != len(A_					):
                      raise ValueError(
                          'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
                          F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''					)
                 __UpperCamelCase =			additional_special_tokens_extended
            else:
                 __UpperCamelCase =			[mask_token_sent] if mask_token_sent is not None else []
                 additional_special_tokens += [F'''<unk_{i}>''' for i in range(2,self.offset					)]

            __UpperCamelCase =			{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                eos_token=A_,unk_token=A_,mask_token=A_,pad_token=A_,mask_token_sent=A_,offset=A_,additional_special_tokens=A_,sp_model_kwargs=self.sp_model_kwargs,**A_,)
            __UpperCamelCase =			mask_token_sent
            __UpperCamelCase =			vocab_file
            __UpperCamelCase =			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(A_					)

            # add special tokens to encoder dict
            __UpperCamelCase =			{
                0: self.pad_token,
                1: self.eos_token,
            }

            if self.mask_token_sent is not None:
                 self.encoder.update(
                     {
                         2: self.mask_token_sent,
                         3: self.mask_token,
                     }					)

            if self.offset > 0:
                 # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                 # mask_token_sent is already added to list -> so start at 1
                 self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,self.offset - 1					)}					)

            __UpperCamelCase =			{v: k for k, v in self.encoder.items()}







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            return len(self.sp_model					) + self.offset







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			{self.convert_ids_to_tokens(A_					): i for i in range(self.vocab_size					)}
            vocab.update(self.added_tokens_encoder					)
            return vocab







       def __getstate__(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.__dict__.copy()
            __UpperCamelCase =			None
            return state







       def __setstate__(			self: Dict,A_: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			d

            # for backward compatibility
            if not hasattr(self,'sp_model_kwargs'					):
                 __UpperCamelCase =			{}

            __UpperCamelCase =			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(self.vocab_file					)







       def 		snake_case_	(			self: Optional[int],A_: str					):






            '''simple docstring'''




            return self.sp_model.encode(A_,out_type=A_					)







       def 		snake_case_	(			self: str,A_: str					):






            '''simple docstring'''




            if token in self.decoder:
                 return self.decoder[token]
            elif token in self.added_tokens_decoder:
                 return self.added_tokens_decoder[token]
            __UpperCamelCase =			self.sp_model.piece_to_id(A_					)
            return sp_id + self.offset







       def 		snake_case_	(			self: Union[str, Any],A_: int					):






            '''simple docstring'''




            if index in self.encoder:
                 return self.encoder[index]
            elif index in self.added_tokens_encoder:
                 return self.added_tokens_encoder[index]
            else:
                 __UpperCamelCase =			self.sp_model.IdToPiece(index - self.offset					)
            return token







       def 		snake_case_	(			self: Union[str, Any],A_: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			[]
            __UpperCamelCase =			''
            for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                      out_string += self.sp_model.decode(A_					) + token
                      __UpperCamelCase =			[]
                 else:
                      current_sub_tokens.append(A_					)
            out_string += self.sp_model.decode(A_					)
            return out_string.strip()







       def 		snake_case_	(			self: Dict,A_: Any=False					):






            '''simple docstring'''




            return 1







       def 		snake_case_	(			self: Union[str, Any],A_: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			set(self.all_special_ids					)  # call it once instead of inside list comp
            all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

            return [1 if x in all_special_ids else 0 for x in seq]







       def 		snake_case_	(			self: Optional[Any],A_: List,A_: Optional[List] = None,A_: bool = False					):






            '''simple docstring'''




            if already_has_special_tokens:
                 return self._special_token_mask(A_					)
            elif token_ids_a is None:
                 return self._special_token_mask(A_					) + [1]
            else:
                 return self._special_token_mask(token_ids_a + token_ids_a					) + [1]







       def 		snake_case_	(			self: Optional[Any],A_: Optional[int],A_: Tuple=None					):






            '''simple docstring'''




            if token_ids_a is None:
                 return token_ids_a + [self.eos_token_id]
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return token_ids_a + token_ids_a + [self.eos_token_id]







       def 		snake_case_	(			self: List[str],A_: str,A_: Optional[str] = None					):






            '''simple docstring'''




            if not os.path.isdir(A_					):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                 return
            __UpperCamelCase =			os.path.join(
                A_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

            if os.path.abspath(self.vocab_file					) != os.path.abspath(A_					) and os.path.isfile(self.vocab_file					):
                 copyfile(self.vocab_file,A_					)
            elif not os.path.isfile(self.vocab_file					):
                 with open(A_,'wb'					) as fi:
                      __UpperCamelCase =			self.sp_model.serialized_model_proto()
                      fi.write(A_					)

            return (out_vocab_file,)




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 							lowerCAmelCase__   ( __lowercase						):

						def __init__(      self							,				a=0.01							,				a=10_00					)       ->   Optional[int]:


								'''simple docstring'''

								_UpperCamelCase         =						p_stop
								_UpperCamelCase         =						max_length







						def __iter__(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						0
								_UpperCamelCase         =						False
								while not stop and count < self.max_length:
										yield count
										count += 1
										_UpperCamelCase         =						random.random() < self.p_stop







class 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self							,				a							,				a							,				a=False							,				a=True					)       ->   Optional[Any]:


								'''simple docstring'''

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

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a					)

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=3							,				drop_last=a					)
								# Expected shouldn't change
								self.check_batch_sampler_shards(a							,				a					)

								# Check the shards when the dataset is a round multiple of batch size but not total batch size.
								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a					)

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

								# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
								# num_processes batch.
								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a					)

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

								# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								_UpperCamelCase         =						BatchSampler(range(20					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a					)

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

								# Check the shards when the dataset is very small.
								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[[[0, 1, 0]], [[1, 0, 1]]]
								self.check_batch_sampler_shards(a							,				a					)

								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[[], []]
								self.check_batch_sampler_shards(a							,				a					)

						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=4							,				drop_last=a					)
								# Expected shouldn't change
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size.
								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size or num_processes.
								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a					)

								# Check the shards when the dataset is very small.
								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[[[0, 1]], [[0, 1]]]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[[], []]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=3							,				drop_last=a					)
								# Expected shouldn't change
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								# Check the shards when the dataset is a round multiple of batch size but not total batch size.
								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				even_batches=a					)

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

								# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
								# num_processes batch.
								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								_UpperCamelCase         =						BatchSampler(range(20					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				even_batches=a					)

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

								# Check the shards when the dataset is very small.
								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[[[0, 1]], []]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[[], []]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=4							,				drop_last=a					)
								# Expected shouldn't change
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size.
								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a							,				even_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size or num_processes.
								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[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(a							,				a							,				split_batches=a							,				even_batches=a					)

								# Check the shards when the dataset is very small.
								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[[[0, 1]], []]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[[], []]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								_UpperCamelCase         =						[BatchSamplerShard(a							,				2							,				a							,				even_batches=a					) 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 			A_					(      self							,				a							,				a							,				a							,				a=False							,				a=2							,				a=False					)       ->   Any:


								'''simple docstring'''

								random.seed(a					)
								_UpperCamelCase         =						list(a					)

								_UpperCamelCase         =						[
								    IterableDatasetShard(
								        a							,				batch_size=a							,				drop_last=a							,				num_processes=a							,				process_index=a							,				split_batches=a							,				)
								    for i in range(a					)
								]
								_UpperCamelCase         =						[]
								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(a					)
										iterable_dataset_lists.append(list(a					)					)

								_UpperCamelCase         =						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
								_UpperCamelCase         =						iterable_dataset_lists[0]
								for l in iterable_dataset_lists[1:]:
										self.assertEqual(len(a					)							,				len(a					)					)
										self.assertTrue(len(a					) % shard_batch_size == 0					)

								_UpperCamelCase         =						[]
								for idx in range(0							,				len(a					)							,				a					):
										for l in iterable_dataset_lists:
												observed += l[idx : idx + shard_batch_size]

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

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						42
								_UpperCamelCase         =						RandomIterableDataset()

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

								# Edge case with a very small dataset
								_UpperCamelCase         =						RandomIterableDataset(max_length=2					)

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

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(16					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						SkipBatchSampler(a							,				2					)
								self.assertListEqual(list(a					)							,				[[8, 9, 10, 11], [12, 13, 14, 15]]					)

						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						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 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

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

						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

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

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







						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

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

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



