





"""simple docstring"""





import copy

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


lowerCAmelCase__        =      logging.get_logger(__name__)

lowerCAmelCase__        =      {}





class 							_lowerCamelCase   (							_lowercase			):
						UpperCAmelCase_			=      "fsmt_decoder"


						def __init__(self ,				__a=0 ,				__a=0		)			-> Optional[int]:
								super().__init__()
								UpperCamelCase =						vocab_size
								UpperCamelCase =						bos_token_id





class 							_lowerCamelCase   (							_lowercase			):
						UpperCAmelCase_			=      "fsmt"
						UpperCAmelCase_			=      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


						def __init__(self ,				__a=["en", "de"] ,				__a=4_20_24 ,				__a=4_20_24 ,				__a="relu" ,				__a=10_24 ,				__a=2_00 ,				__a=10_24 ,				__a=40_96 ,				__a=12 ,				__a=16 ,				__a=0.0 ,				__a=40_96 ,				__a=12 ,				__a=16 ,				__a=0.0 ,				__a=0.0 ,				__a=0.1 ,				__a=0.0 ,				__a=0.02 ,				__a=2 ,				__a=True ,				__a=True ,				__a=False ,				__a=5 ,				__a=1.0 ,				__a=False ,				__a=True ,				__a=1 ,				__a=0 ,				__a=2 ,				__a=2 ,				**__a ,				)			-> List[str]:
								UpperCamelCase =						langs
								UpperCamelCase =						src_vocab_size
								UpperCamelCase =						tgt_vocab_size
								UpperCamelCase =						d_model  # encoder_embed_dim and decoder_embed_dim

								UpperCamelCase =						encoder_ffn_dim
								UpperCamelCase =						UpperCamelCase =						encoder_layers
								UpperCamelCase =						encoder_attention_heads
								UpperCamelCase =						encoder_layerdrop
								UpperCamelCase =						decoder_layerdrop
								UpperCamelCase =						decoder_ffn_dim
								UpperCamelCase =						decoder_layers
								UpperCamelCase =						decoder_attention_heads
								UpperCamelCase =						max_position_embeddings
								UpperCamelCase =						init_std  # Normal(0, this parameter)
								UpperCamelCase =						activation_function

								UpperCamelCase =						DecoderConfig(vocab_size=__a ,				bos_token_id=__a		)
								if "decoder" in common_kwargs:
										del common_kwargs["decoder"]

								UpperCamelCase =						scale_embedding  # scale factor will be sqrt(d_model) if True

								# 3 Types of Dropout
								UpperCamelCase =						attention_dropout
								UpperCamelCase =						activation_dropout
								UpperCamelCase =						dropout

								UpperCamelCase =						use_cache
								super().__init__(
								    pad_token_id=__a ,				bos_token_id=__a ,				eos_token_id=__a ,				decoder_start_token_id=__a ,				is_encoder_decoder=__a ,				tie_word_embeddings=__a ,				forced_eos_token_id=__a ,				max_length=__a ,				num_beams=__a ,				length_penalty=__a ,				early_stopping=__a ,				**__a ,				)




						def      snake_case_ (self		)			-> int:
								UpperCamelCase =						copy.deepcopy(self.__dict__		)
								UpperCamelCase =						self.decoder.to_dict()
								UpperCamelCase =						self.__class__.model_type
								return output













"""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


lowerCAmelCase__        =      logging.getLogger(__name__)


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset."     )
		parser.add_argument(
		    "--dataset_name"	,						type=_SCREAMING_SNAKE_CASE	,						default="wikitext"	,						help="Name of the training. Explore datasets at: hf.co/datasets."	,						)
		parser.add_argument(
		    "--dataset_config"	,						type=_SCREAMING_SNAKE_CASE	,						default="wikitext-103-raw-v1"	,						help="Configuration name of the dataset."     )
		parser.add_argument(
		    "--batch_size"	,						type=_SCREAMING_SNAKE_CASE	,						default=1_000	,						help="Batch size during training."	,						)
		parser.add_argument(
		    "--vocab_size"	,						type=_SCREAMING_SNAKE_CASE	,						default=10_048	,						help="Size of the desired vocabulary."	,						)
		parser.add_argument(
		    "--limit"	,						default=_SCREAMING_SNAKE_CASE	,						type=_SCREAMING_SNAKE_CASE	,						help="Limit the number of shards (used for debugging)."	,						)
		parser.add_argument(
		    "--export_to_hub"	,						action="store_true"	,						)

		UpperCamelCase =						parser.parse_args()
		return args


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						datasets.load_dataset(args.dataset_name	,						args.dataset_config	,						split="train"     )

		if args.limit is not None:
				UpperCamelCase =						min(len(_SCREAMING_SNAKE_CASE     )	,						args.limit     )
				UpperCamelCase =						dataset.select(range(_SCREAMING_SNAKE_CASE     )     )
				logger.info(F"Limiting the dataset to {args.limit} entries."     )

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

    # Prepare the tokenizer.
		UpperCamelCase =						Tokenizer(Unigram()     )
		UpperCamelCase =						normalizers.Sequence([normalizers.Replace("``"	,						"\""     ), normalizers.Replace("''"	,						"\""     )]     )
		UpperCamelCase =						pre_tokenizers.Metaspace()

		# Prepare the trainer.
		UpperCamelCase =						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=_SCREAMING_SNAKE_CASE     )
		logger.info("Tokenizer training complete!"     )

		UpperCamelCase =						tokenizer.token_to_id("[CLS]"     )
		UpperCamelCase =						tokenizer.token_to_id("[SEP]"     )
		UpperCamelCase =						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),
		    ]	,						)
		UpperCamelCase =						decoders.Metaspace()

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


if __name__ == "__main__":
						lowerCAmelCase__        =      parse_args()
						main(args)






