


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_fnet import FNetTokenizer
else:
				lowerCAmelCase__			:      List[str]								=None

lowerCAmelCase__			:      str								=logging.get_logger(__name__)
lowerCAmelCase__			:      Any								={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__			:      List[Any]								={
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__			:      Any								={
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

lowerCAmelCase__			:      Optional[Any]								='▁'







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						VOCAB_FILES_NAMES
							_UpperCAmelCase      =						PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase      =						["""input_ids""", """token_type_ids"""]
							_UpperCAmelCase      =						FNetTokenizer






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=False			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__="<unk>"			,						lowerCAmelCase__="[SEP]"			,						lowerCAmelCase__="<pad>"			,						lowerCAmelCase__="[CLS]"			,						lowerCAmelCase__="[MASK]"			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					(
										    AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__			,						normalized=lowerCAmelCase__       )
										    if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       )
										    else mask_token
										)

										super().__init__(
										    lowerCAmelCase__			,						tokenizer_file=lowerCAmelCase__			,						do_lower_case=lowerCAmelCase__			,						remove_space=lowerCAmelCase__			,						keep_accents=lowerCAmelCase__			,						unk_token=lowerCAmelCase__			,						sep_token=lowerCAmelCase__			,						pad_token=lowerCAmelCase__			,						cls_token=lowerCAmelCase__			,						mask_token=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					do_lower_case
										SCREAMING_SNAKE_CASE_		:     int					=					remove_space
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					keep_accents
										SCREAMING_SNAKE_CASE_		:     Any					=					vocab_file
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					False if not self.vocab_file else True






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[self.sep_token_id]
										SCREAMING_SNAKE_CASE_		:     Any					=					[self.cls_token_id]
										if token_ids_a is None:
													return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[self.sep_token_id]
										SCREAMING_SNAKE_CASE_		:     int					=					[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       ):





										"""simple docstring"""




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

										if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCAmelCase__       ):
													copyfile(self.vocab_file			,						lowerCAmelCase__       )

										return (out_vocab_file,)








"""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


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

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

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

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

	lowerCAmelCase__      :		int    							=      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()




