'''simple docstring'''

from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__a: List[str]          =   logging.get_logger(__name__)

__a: Optional[int]          =   {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__a: Optional[Any]          =   {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__a: Optional[Any]          =   {
    """facebook/blenderbot_small-90M""": 5_12,
}



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE							=    PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE							=    BlenderbotSmallTokenizer

       def __init__(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase=False	,   __lowerCAmelCase=True	,   **__lowerCAmelCase	,   )  ->  Union[str, Any]:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=__lowerCAmelCase	,   merges=__lowerCAmelCase	,   add_prefix_space=__lowerCAmelCase	,   trim_offsets=__lowerCAmelCase	,   )	,   bos_token=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   **__lowerCAmelCase	,   )
        lowercase__				:    Any  =					add_prefix_space

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=None )  ->  List[str]:
        lowercase__				:    Any  =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
         return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Optional[int]  =					[self.sep_token_id]
        lowercase__				:    Union[str, Any]  =					[self.cls_token_id]

        if token_ids_a is None:
         return len(cls + token_ids_a + sep ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]


'''simple docstring'''

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__a: Optional[int]          =   4
__a: Optional[Any]          =   3



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       pass


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 for shard in shards:
  for i in range(UpperCAmelCase     ):
   yield {"i": i, "shard": shard}
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					int(os.environ['''RANK''']     )
 lowercase__				:    List[str]  =					int(os.environ['''WORLD_SIZE''']     )

 lowercase__				:    Optional[Any]  =					ArgumentParser()
 parser.add_argument('''--streaming'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--local_rank'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--num_workers'''		,     type=UpperCAmelCase		,     default=0     )
 lowercase__				:    List[Any]  =					parser.parse_args()
 lowercase__				:    List[str]  =					args.streaming
 lowercase__				:    str  =					args.num_workers

 lowercase__				:    Optional[int]  =					{'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(UpperCAmelCase     )]}
 lowercase__				:    Tuple  =					IterableDataset.from_generator(UpperCAmelCase		,     gen_kwargs=UpperCAmelCase     )
 if not streaming:
  lowercase__				:    int  =					Dataset.from_list(list(UpperCAmelCase     )     )

 lowercase__				:    str  =					split_dataset_by_node(UpperCAmelCase		,     rank=UpperCAmelCase		,     world_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					torch.utils.data.DataLoader(UpperCAmelCase		,     num_workers=UpperCAmelCase     )

 lowercase__				:    Dict  =					NUM_SHARDS * NUM_ITEMS_PER_SHARD
 lowercase__				:    Optional[Any]  =					full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)     )

 lowercase__				:    Union[str, Any]  =					sum(1 for _ in dataloader     )
 if local_size != expected_local_size:
  raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
  main()

