
"""simple docstring"""




import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


snake_case			       =		logging.get_logger(__name__)




class 			UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				Tuple						  =    "semantic"


				def __init__(    self		,     lowercase__=10000		,     lowercase__=True		,     lowercase__=768		,     lowercase__=False		,     lowercase__=False		,     lowercase__=False		,     lowercase__=False		,     lowercase__=0.7		,     lowercase__=True		,     lowercase__=10048		,     lowercase__=129595		,     lowercase__=129599		,     lowercase__=10000		,     lowercase__=256		,     lowercase__=49.9		,     **lowercase__		,     )   ->   Tuple:
									"""simple docstring"""







									super().__init__(
									    temperature=lowercase__		,     do_sample=lowercase__		,     eos_token_id=lowercase__		,     renormalize_logits=lowercase__		,     max_new_tokens=lowercase__		,     output_scores=lowercase__		,     return_dict_in_generate=lowercase__		,     output_hidden_states=lowercase__		,     output_attentions=lowercase__		,     **lowercase__		,     )

									SCREAMING_SNAKE_CASE           =     text_encoding_offset
									SCREAMING_SNAKE_CASE           =     text_pad_token
									SCREAMING_SNAKE_CASE           =     eos_token_id
									SCREAMING_SNAKE_CASE           =     semantic_infer_token
									SCREAMING_SNAKE_CASE           =     semantic_vocab_size
									SCREAMING_SNAKE_CASE           =     max_input_semantic_length
									SCREAMING_SNAKE_CASE           =     semantic_rate_hz






class 			UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				int						  =    "coarse_acoustics"


				def __init__(    self		,     lowercase__=True		,     lowercase__=False		,     lowercase__=False		,     lowercase__=False		,     lowercase__=False		,     lowercase__=0.7		,     lowercase__=True		,     lowercase__=12048		,     lowercase__=75		,     lowercase__=2		,     lowercase__=12050		,     lowercase__=256		,     lowercase__ = 630		,     lowercase__ = 60		,     **lowercase__		,     )   ->   Union[str, Any]:
									"""simple docstring"""







									super().__init__(
									    temperature=lowercase__		,     do_sample=lowercase__		,     renormalize_logits=lowercase__		,     output_scores=lowercase__		,     return_dict_in_generate=lowercase__		,     output_hidden_states=lowercase__		,     output_attentions=lowercase__		,     **lowercase__		,     )

									SCREAMING_SNAKE_CASE           =     coarse_semantic_pad_token
									SCREAMING_SNAKE_CASE           =     coarse_rate_hz
									SCREAMING_SNAKE_CASE           =     n_coarse_codebooks
									SCREAMING_SNAKE_CASE           =     coarse_infer_token
									SCREAMING_SNAKE_CASE           =     max_coarse_input_length
									SCREAMING_SNAKE_CASE           =     max_coarse_history
									SCREAMING_SNAKE_CASE           =     sliding_window_len






class 			UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				Optional[int]						  =    "fine_acoustics"


				def __init__(    self		,     lowercase__=0.5		,     lowercase__=512		,     lowercase__=1024		,     lowercase__=8		,     **lowercase__		,     )   ->   List[str]:
									"""simple docstring"""







									super().__init__(temperature=lowercase__      )

									SCREAMING_SNAKE_CASE           =     max_fine_history_length
									SCREAMING_SNAKE_CASE           =     max_fine_input_length
									SCREAMING_SNAKE_CASE           =     n_fine_codebooks






class 			UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				Union[str, Any]						  =    "bark"
				UpperCAmelCase_    :				Any						  =    True


				def __init__(    self		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__=24000		,     lowercase__=1024		,     **lowercase__		,     )   ->   str:
									"""simple docstring"""







									if semantic_config is None:
														SCREAMING_SNAKE_CASE           =     {}
														logger.info('semantic_config is None. initializing the semantic model with default values.'      )

									if coarse_acoustics_config is None:
														SCREAMING_SNAKE_CASE           =     {}
														logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'      )

									if fine_acoustics_config is None:
														SCREAMING_SNAKE_CASE           =     {}
														logger.info('fine_acoustics_config is None. initializing the fine model with default values.'      )

									SCREAMING_SNAKE_CASE           =     BarkSemanticGenerationConfig(**lowercase__      )
									SCREAMING_SNAKE_CASE           =     BarkCoarseGenerationConfig(**lowercase__      )
									SCREAMING_SNAKE_CASE           =     BarkFineGenerationConfig(**lowercase__      )

									SCREAMING_SNAKE_CASE           =     sample_rate
									SCREAMING_SNAKE_CASE           =     codebook_size


				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__		,     lowercase__		,     **lowercase__		,     )   ->   Dict:
									"""simple docstring"""







									return cls(
									    semantic_config=semantic_config.to_dict()		,     coarse_acoustics_config=coarse_acoustics_config.to_dict()		,     fine_acoustics_config=fine_acoustics_config.to_dict()		,     **lowercase__		,     )




				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     copy.deepcopy(self.__dict__      )

									SCREAMING_SNAKE_CASE           =     self.semantic_config.to_dict()
									SCREAMING_SNAKE_CASE           =     self.coarse_acoustics_config.to_dict()
									SCREAMING_SNAKE_CASE           =     self.fine_acoustics_config.to_dict()

									SCREAMING_SNAKE_CASE           =     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


snake_case			       =		logging.getLogger(__name__)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     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_0_0_0,					help='Batch size during training.',					)
					parser.add_argument(
					    '--vocab_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_4_8,					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',					)

					SCREAMING_SNAKE_CASE           =     parser.parse_args()
					return args

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.load_dataset(args.dataset_name,					args.dataset_config,					split='train'      )

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

					# Prepare the trainer.
					SCREAMING_SNAKE_CASE           =     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!'      )

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

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


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