

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 							__lowercase     (	):

					"""simple docstring"""






					__lowerCAmelCase       =				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=1_000		,       help='Batch size during training.'		,       )
					parser.add_argument(
					    '--vocab_size'		,       type=UpperCAmelCase__		,       default=10_048		,       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'		,       )

					__lowerCAmelCase       =				parser.parse_args()
					return args


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				datasets.load_dataset(args.dataset_name		,       args.dataset_config		,       split='train'   )

					if args.limit is not None:
										__lowerCAmelCase       =				min(len(UpperCAmelCase__   )		,       args.limit   )
										__lowerCAmelCase       =				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.
					__lowerCAmelCase       =				Tokenizer(Unigram()   )
					__lowerCAmelCase       =				normalizers.Sequence([normalizers.Replace('``'		,       '"'   ), normalizers.Replace('\'\''		,       '"'   )]   )
					__lowerCAmelCase       =				pre_tokenizers.Metaspace()

					# Prepare the trainer.
					__lowerCAmelCase       =				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!'   )

					__lowerCAmelCase       =				tokenizer.token_to_id('[CLS]'   )
					__lowerCAmelCase       =				tokenizer.token_to_id('[SEP]'   )
					__lowerCAmelCase       =				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),
					    ]		,       )
					__lowerCAmelCase       =				decoders.Metaspace()

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


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









from typing import Callable, Optional, Union

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


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""prophetnet"""
							__UpperCAmelCase      =["""past_key_values"""]
							__UpperCAmelCase      ={
							    """num_attention_heads""": """num_encoder_attention_heads""",
							}




							def __init__(			self       ,    _A = 0.1       ,    _A = "gelu"       ,    _A = 3_0_5_2_2       ,    _A = 1_0_2_4       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 0.1       ,    _A = 0.1       ,    _A = 5_1_2       ,    _A = 0.02       ,    _A = True       ,    _A = True       ,    _A = 0       ,    _A = 2       ,    _A = 3_2       ,    _A = 1_2_8       ,    _A = False       ,    _A = 0.0       ,    _A = True       ,    _A = 0       ,    _A = 1       ,    _A = 2       ,    **_A       ,    ):
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				hidden_size
												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				num_encoder_layers
												__lowerCAmelCase       =				num_encoder_attention_heads
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				num_decoder_layers
												__lowerCAmelCase       =				num_decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												# parameters for prophetnet
												__lowerCAmelCase       =				ngram
												__lowerCAmelCase       =				num_buckets
												__lowerCAmelCase       =				relative_max_distance
												__lowerCAmelCase       =				disable_ngram_loss
												__lowerCAmelCase       =				eps

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

												__lowerCAmelCase       =				use_cache

												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    is_encoder_decoder=_A       ,    add_cross_attention=_A       ,    decoder_start_token_id=_A       ,    **_A       ,    )




							@property
							def 							A__	(			self				):
												return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 							A__	(			self       ,    _A				):
												raise NotImplementedError(
												    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
												    ' `num_decoder_layers`.'				)






