





'''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


_SCREAMING_SNAKE_CASE   =      4
_SCREAMING_SNAKE_CASE   =      3






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	pass





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
			for shard in shards:
						for i in range(lowerCamelCase_			):
									yield {"i": i, "shard": shard}




def 				_lowerCAmelCase  (			):
			__lowercase			  =							int(os.environ['''RANK''']			)
			__lowercase			  =							int(os.environ['''WORLD_SIZE''']			)

			__lowercase			  =							ArgumentParser()
			parser.add_argument('''--streaming'''						,    type=lowerCamelCase_			)
			parser.add_argument('''--local_rank'''						,    type=lowerCamelCase_			)
			parser.add_argument('''--num_workers'''						,    type=lowerCamelCase_						,    default=0			)
			__lowercase			  =							parser.parse_args()
			__lowercase			  =							args.streaming
			__lowercase			  =							args.num_workers

			__lowercase			  =							{'''shards''': [f"shard_{shard_idx}" for shard_idx in range(lowerCamelCase_			)]}
			__lowercase			  =							IterableDataset.from_generator(lowerCamelCase_						,    gen_kwargs=lowerCamelCase_			)
			if not streaming:
						__lowercase			  =							Dataset.from_list(list(lowerCamelCase_			)			)

			__lowercase			  =							split_dataset_by_node(lowerCamelCase_						,    rank=lowerCamelCase_						,    world_size=lowerCamelCase_			)
			__lowercase			  =							torch.utils.data.DataLoader(lowerCamelCase_						,    num_workers=lowerCamelCase_			)

			__lowercase			  =							NUM_SHARDS * NUM_ITEMS_PER_SHARD
			__lowercase			  =							full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)			)

			__lowercase			  =							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()












'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_SCREAMING_SNAKE_CASE   =      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE   =      {
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

_SCREAMING_SNAKE_CASE   =      {
    '''google/electra-small-generator''': 5_1_2,
    '''google/electra-base-generator''': 5_1_2,
    '''google/electra-large-generator''': 5_1_2,
    '''google/electra-small-discriminator''': 5_1_2,
    '''google/electra-base-discriminator''': 5_1_2,
    '''google/electra-large-discriminator''': 5_1_2,
}

_SCREAMING_SNAKE_CASE   =      {
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			str     =    VOCAB_FILES_NAMES
	a      :			Dict     =    PRETRAINED_VOCAB_FILES_MAP
	a      :			int     =    PRETRAINED_INIT_CONFIGURATION
	a      :			Tuple     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a      :			List[Any]     =    ElectraTokenizer


	def __init__(self       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase="[UNK]"       ,_lowerCamelCase="[SEP]"       ,_lowerCamelCase="[PAD]"       ,_lowerCamelCase="[CLS]"       ,_lowerCamelCase="[MASK]"       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							List[Any]:







				'''simple docstring'''





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

				__lowercase			  =							json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
				if (
				    normalizer_state.get('''lowercase'''       ,_lowerCamelCase	) != do_lower_case
				    or normalizer_state.get('''strip_accents'''       ,_lowerCamelCase	) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''       ,_lowerCamelCase	) != tokenize_chinese_chars
				):
							__lowercase			  =							getattr(_lowerCamelCase       ,normalizer_state.pop('''type'''	)	)
							__lowercase			  =							do_lower_case
							__lowercase			  =							strip_accents
							__lowercase			  =							tokenize_chinese_chars
							__lowercase			  =							normalizer_class(**_lowerCamelCase	)

				__lowercase			  =							do_lower_case


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							str:







				'''simple docstring'''





				__lowercase			  =							[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       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							List[int]:







				'''simple docstring'''





				__lowercase			  =							[self.sep_token_id]
				__lowercase			  =							[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       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Tuple[str]:







				'''simple docstring'''





				__lowercase			  =							self._tokenizer.model.save(_lowerCamelCase       ,name=_lowerCamelCase	)
				return tuple(_lowerCamelCase	)





