
'''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


UpperCAmelCase            = logging.getLogger(__name__)

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

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

def   A			(							A_				:       Dict							):
						snake_case							:  List[Any]			=     datasets.load_dataset(args.dataset_name     ,				args.dataset_config     ,				split='''train'''							)

						if args.limit is not None:
												snake_case							:  int			=     min(len(A_							)     ,				args.limit							)
												snake_case							:  Optional[int]			=     dataset.select(range(A_							)							)
												logger.info(F"""Limiting the dataset to {args.limit} entries."""							)

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

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

						# Prepare the trainer.
						snake_case							:  int			=     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=A_							)
						logger.info('''Tokenizer training complete!'''							)

						snake_case							:  Union[str, Any]			=     tokenizer.token_to_id('''[CLS]'''							)
						snake_case							:  Tuple			=     tokenizer.token_to_id('''[SEP]'''							)
						snake_case							:  List[str]			=     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),
						    ]     ,				)
						snake_case							:  str			=     decoders.Metaspace()

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


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



'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen_decoder'''
				_snake_case		     =			['''past_key_values''']




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict=20_48,     SCREAMING_SNAKE_CASE_						:					List[str]=20_48,     SCREAMING_SNAKE_CASE_						:					int=24,     SCREAMING_SNAKE_CASE_						:					Dict=40_96,     SCREAMING_SNAKE_CASE_						:					str=16,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					List[Any]=10_24,     SCREAMING_SNAKE_CASE_						:					int=0.1,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.0,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					Any=False,     SCREAMING_SNAKE_CASE_						:					List[str]=4,     SCREAMING_SNAKE_CASE_						:					str=20_48,     SCREAMING_SNAKE_CASE_						:					int=20_48,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     **SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  Optional[int]			=     vocab_size
										snake_case							:  Optional[int]			=     max_position_embeddings
										snake_case							:  Dict			=     hidden_size
										snake_case							:  Any			=     ffn_dim
										snake_case							:  Optional[Any]			=     num_hidden_layers
										snake_case							:  Tuple			=     num_attention_heads
										snake_case							:  int			=     dropout
										snake_case							:  Union[str, Any]			=     attention_dropout
										snake_case							:  Optional[int]			=     activation_dropout
										snake_case							:  Union[str, Any]			=     activation_function
										snake_case							:  Tuple			=     initializer_factor
										snake_case							:  List[Any]			=     layerdrop
										snake_case							:  Optional[int]			=     use_cache
										snake_case							:  Dict			=     scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case							:  Tuple			=     num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_,     tie_word_embeddings=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )




class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen'''
				_snake_case		     =			True




				def __init__(				self						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''   )

										snake_case							:  Any			=     kwargs.pop('''text_encoder'''   )
										snake_case							:  List[Any]			=     text_encoder_config.pop('''model_type'''   )

										snake_case							:  Union[str, Any]			=     kwargs.pop('''audio_encoder'''   )
										snake_case							:  Tuple			=     audio_encoder_config.pop('''model_type'''   )

										snake_case							:  List[str]			=     kwargs.pop('''decoder'''   )

										snake_case							:  List[str]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     True




				@classmethod
				def 						__snake_case				(				cls						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					MusicgenDecoderConfig,     **SCREAMING_SNAKE_CASE_						:					Optional[Any],     ):
										return cls(
										    text_encoder=text_encoder_config.to_dict(),     audio_encoder=audio_encoder_config.to_dict(),     decoder=decoder_config.to_dict(),     **SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Dict			=     copy.deepcopy(self.__dict__   )
										snake_case							:  Tuple			=     self.text_encoder.to_dict()
										snake_case							:  str			=     self.audio_encoder.to_dict()
										snake_case							:  Dict			=     self.decoder.to_dict()
										snake_case							:  Optional[int]			=     self.__class__.model_type
										return output

