



"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   {'vocab_file': 'vocab.txt'}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

lowerCAmelCase_   						=   {
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


lowerCAmelCase_   						=   {
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Union[str, Any]   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					str   	=				PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					Any   	=				PRETRAINED_INIT_CONFIGURATION
		lowerCAmelCase			:					List[str]   	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					Dict   	=				ConvBertTokenizer







		def __init__(	self						:  Union[str, Any]			,_snake_case						:  Tuple=None			,_snake_case						:  Dict=None			,_snake_case						:  Union[str, Any]=True			,_snake_case						:  List[str]="[UNK]"			,_snake_case						:  str="[SEP]"			,_snake_case						:  str="[PAD]"			,_snake_case						:  int="[CLS]"			,_snake_case						:  int="[MASK]"			,_snake_case						:  Optional[int]=True			,_snake_case						:  Optional[int]=None			,**_snake_case						:  List[Any]			,)					->		Tuple:






				"""simple docstring"""



				super().__init__(
				    _snake_case			,tokenizer_file=_snake_case			,do_lower_case=_snake_case			,unk_token=_snake_case			,sep_token=_snake_case			,pad_token=_snake_case			,cls_token=_snake_case			,mask_token=_snake_case			,tokenize_chinese_chars=_snake_case			,strip_accents=_snake_case			,**_snake_case			,)

				lowercase__   :    int				=			json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
				if (
				    normalizer_state.get('''lowercase'''			,_snake_case						) != do_lower_case
				    or normalizer_state.get('''strip_accents'''			,_snake_case						) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''			,_snake_case						) != tokenize_chinese_chars
				):
						lowercase__   :    int				=			getattr(_snake_case			,normalizer_state.pop('''type'''						)						)
						lowercase__   :    Optional[Any]				=			do_lower_case
						lowercase__   :    List[str]				=			strip_accents
						lowercase__   :    Optional[Any]				=			tokenize_chinese_chars
						lowercase__   :    List[str]				=			normalizer_class(**_snake_case						)

				lowercase__   :    Optional[int]				=			do_lower_case







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Dict			,_snake_case						:  Dict=None						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
						output += token_ids_a + [self.sep_token_id]

				return output







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			[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						) * [0] + len(token_ids_a + sep						) * [1]







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  str			,_snake_case						:  Optional[str] = None						)					->		Tuple[str]:






				"""simple docstring"""



				lowercase__   :    str				=			self._tokenizer.model.save(_snake_case			,name=_snake_case						)
				return tuple(_snake_case						)





"""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


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

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

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

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

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

		lowercase__   :    str				=			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()
