'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:					Any													=		logging.get_logger(__name__)

_lowercase		:					List[Any]													=		{
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'''reformer'''
			UpperCamelCase__							=			['''past_buckets_states''']
			UpperCamelCase__							=			{}







			def __init__(      self							:    Dict							,     lowercase_							:    int=64							,     lowercase_							:    List[str]=["local", "lsh", "local", "lsh", "local", "lsh"]							,     lowercase_							:    List[Any]=1.0							,     lowercase_							:    List[str]=True							,     lowercase_							:    int=[64, 64]							,     lowercase_							:    List[Any]=[64, 192]							,     lowercase_							:    int=0							,     lowercase_							:    int=2							,     lowercase_							:    Optional[int]=512							,     lowercase_							:    List[Any]=None							,     lowercase_							:    Optional[int]="relu"							,     lowercase_							:    List[Any]=0.05							,     lowercase_							:    Optional[Any]=256							,     lowercase_							:    Any=0.02							,     lowercase_							:    Tuple=False							,     lowercase_							:    Dict=1E-12							,     lowercase_							:    Union[str, Any]=1							,     lowercase_							:    Dict=0							,     lowercase_							:    int=0.05							,     lowercase_							:    List[Any]=64							,     lowercase_							:    int=64							,     lowercase_							:    int=0.0							,     lowercase_							:    Optional[Any]=1							,     lowercase_							:    int=0							,     lowercase_							:    Any=4096							,     lowercase_							:    List[str]=12							,     lowercase_							:    Union[str, Any]=None							,     lowercase_							:    Any=1							,     lowercase_							:    Tuple=0							,     lowercase_							:    Tuple=320							,     lowercase_							:    Any=False							,     lowercase_							:    str=True							,     lowercase_							:    Dict=None							,     **lowercase_							:    List[Any]							,     ):
										lowercase_   :     List[Any]									=			hash_seed
										lowercase_   :     str									=			vocab_size
										lowercase_   :     List[Any]									=			attention_head_size
										lowercase_   :     int									=			hidden_size
										lowercase_   :     Dict									=			num_attention_heads
										lowercase_   :     int									=			num_hashes
										lowercase_   :     Dict									=			len(lowercase_       )
										lowercase_   :     int									=			tuple(lowercase_       ) if isinstance(lowercase_							,     lowercase_       ) else num_buckets
										lowercase_   :     Union[str, Any]									=			lsh_attn_chunk_length
										lowercase_   :     int									=			local_attn_chunk_length
										lowercase_   :     int									=			lsh_num_chunks_after
										lowercase_   :     Optional[Any]									=			lsh_num_chunks_before
										lowercase_   :     List[str]									=			local_num_chunks_after
										lowercase_   :     Optional[Any]									=			local_num_chunks_before
										lowercase_   :     List[str]									=			hidden_act
										lowercase_   :     Union[str, Any]									=			feed_forward_size
										lowercase_   :     Tuple									=			hidden_dropout_prob
										lowercase_   :     str									=			lsh_attention_probs_dropout_prob
										lowercase_   :     int									=			local_attention_probs_dropout_prob
										lowercase_   :     Tuple									=			max_position_embeddings
										lowercase_   :     Optional[Any]									=			initializer_range
										lowercase_   :     Tuple									=			layer_norm_eps
										lowercase_   :     str									=			axial_pos_embds
										lowercase_   :     List[str]									=			tuple(lowercase_       )
										lowercase_   :     Union[str, Any]									=			tuple(lowercase_       )
										lowercase_   :     Any									=			axial_norm_std
										lowercase_   :     str									=			chunk_size_lm_head
										lowercase_   :     Any									=			attn_layers
										lowercase_   :     Union[str, Any]									=			use_cache
										lowercase_   :     List[str]									=			classifier_dropout
										super().__init__(
										    pad_token_id=lowercase_							,     eos_token_id=lowercase_							,     is_decoder=lowercase_							,     tie_word_embeddings=lowercase_							,     **lowercase_							,     )






'''simple docstring'''




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_lowercase		:					str													=		logging.getLogger(__name__)




def 				lowerCamelCase      ( )     -> Tuple:
							lowercase_   :     Any									=			argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""					)
							parser.add_argument(
							    """--dataset_name""" , type=UpperCAmelCase__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , )
							parser.add_argument(
							    """--dataset_config""" , type=UpperCAmelCase__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset."""					)
							parser.add_argument(
							    """--batch_size""" , type=UpperCAmelCase__ , default=1000 , help="""Batch size during training.""" , )
							parser.add_argument(
							    """--vocab_size""" , type=UpperCAmelCase__ , default=10048 , help="""Size of the desired vocabulary.""" , )
							parser.add_argument(
							    """--limit""" , default=UpperCAmelCase__ , type=UpperCAmelCase__ , help="""Limit the number of shards (used for debugging).""" , )
							parser.add_argument(
							    """--export_to_hub""" , action="""store_true""" , )

							lowercase_   :     List[Any]									=			parser.parse_args()
							return args




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int]					)     -> List[Any]:
							lowercase_   :     Any									=			datasets.load_dataset(args.dataset_name , args.dataset_config , split="""train"""					)

							if args.limit is not None:
														lowercase_   :     Optional[Any]									=			min(len(UpperCAmelCase__					) , args.limit					)
														lowercase_   :     Dict									=			dataset.select(range(UpperCAmelCase__					)					)
														logger.info(F'''Limiting the dataset to {args.limit} entries.'''					)

							def batch_iterator():
														for i in range(0 , len(UpperCAmelCase__					) , args.batch_size					):
																					yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
							lowercase_   :     Optional[int]									=			Tokenizer(Unigram()					)
							lowercase_   :     Optional[int]									=			normalizers.Sequence([normalizers.Replace("""``""" , """\""""					), normalizers.Replace("""''""" , """\""""					)]					)
							lowercase_   :     List[Any]									=			pre_tokenizers.Metaspace()

							# Prepare the trainer.
							lowercase_   :     str									=			UnigramTrainer(
							    unk_token="""<unk>""" , special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""] , vocab_size=args.vocab_size , )

							logger.info("""Training the tokenizer."""					)
							tokenizer.train_from_iterator(batch_iterator() , trainer=UpperCAmelCase__					)
							logger.info("""Tokenizer training complete!"""					)

							lowercase_   :     Dict									=			tokenizer.token_to_id("""[CLS]"""					)
							lowercase_   :     Any									=			tokenizer.token_to_id("""[SEP]"""					)
							lowercase_   :     Optional[Any]									=			processors.TemplateProcessing(
							    single="""[CLS]:0 $A:0 [SEP]:0""" , pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1""" , special_tokens=[
							        ("""[CLS]""", cls_token_id),
							        ("""[SEP]""", sep_token_id),
							    ] , )
							lowercase_   :     int									=			decoders.Metaspace()

							if args.export_to_hub:
														logger.info("""Exporting the trained tokenzier to Hub."""					)
														lowercase_   :     Any									=			AlbertTokenizerFast(tokenizer_object=UpperCAmelCase__					)
														new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""					)


if __name__ == "__main__":
						_lowercase		:					Dict													=		parse_args()
						main(args)





