code: 
  
  
  
  import argparse
  import logging
  import math
  import time
  
  import torch
  
  from transformers import TransfoXLCorpus, TransfoXLLMHeadModel
  
  
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
  )
  __A       					=		logging.getLogger(__name__)
  
  
  
  def  __a  (					)   ->				Optional[Any]:
  
  
  
  
  
  
        '''simple docstring'''
  
  
  
        UpperCAmelCase_=     argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""       )
        parser.add_argument("""--model_name"""    ,type=lowerCAmelCase_    ,default="""transfo-xl-wt103"""    ,help="""pretrained model name"""       )
        parser.add_argument(
            """--split"""    ,type=lowerCAmelCase_    ,default="""test"""    ,choices=["""all""", """valid""", """test"""]    ,help="""which split to evaluate"""       )
        parser.add_argument("""--batch_size"""    ,type=lowerCAmelCase_    ,default=10    ,help="""batch size"""       )
        parser.add_argument("""--tgt_len"""    ,type=lowerCAmelCase_    ,default=1_28    ,help="""number of tokens to predict"""       )
        parser.add_argument("""--ext_len"""    ,type=lowerCAmelCase_    ,default=0    ,help="""length of the extended context"""       )
        parser.add_argument("""--mem_len"""    ,type=lowerCAmelCase_    ,default=16_00    ,help="""length of the retained previous heads"""       )
        parser.add_argument("""--clamp_len"""    ,type=lowerCAmelCase_    ,default=10_00    ,help="""max positional embedding index"""       )
        parser.add_argument("""--no_cuda"""    ,action="""store_true"""    ,help="""Do not use CUDA even though CUA is available"""       )
        parser.add_argument("""--work_dir"""    ,type=lowerCAmelCase_    ,required=lowerCAmelCase_    ,help="""path to the work_dir"""       )
        parser.add_argument("""--no_log"""    ,action="""store_true"""    ,help="""do not log the eval result"""       )
        parser.add_argument("""--same_length"""    ,action="""store_true"""    ,help="""set same length attention with masking"""       )
        parser.add_argument("""--server_ip"""    ,type=lowerCAmelCase_    ,default=""""""    ,help="""Can be used for distant debugging."""       )
        parser.add_argument("""--server_port"""    ,type=lowerCAmelCase_    ,default=""""""    ,help="""Can be used for distant debugging."""       )
        UpperCAmelCase_=     parser.parse_args()
        assert args.ext_len >= 0, "extended context length must be non-negative"
  
        if args.server_ip and args.server_port:
              # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
              import ptvsd
  
              print("""Waiting for debugger attach"""       )
              ptvsd.enable_attach(address=(args.server_ip, args.server_port)    ,redirect_output=lowerCAmelCase_       )
              ptvsd.wait_for_attach()
  
        UpperCAmelCase_=     torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""       )
        logger.info("""device: {}""".format(lowerCAmelCase_       )       )
  
        # Load a pre-processed dataset
        # You can also build the corpus yourself using TransfoXLCorpus methods
        # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
        # and tokenizing the dataset
        # The pre-processed corpus is a convertion (using the conversion script )
        UpperCAmelCase_=     TransfoXLCorpus.from_pretrained(args.model_name       )
  
        UpperCAmelCase_=     corpus.get_iterator("""valid"""    ,args.batch_size    ,args.tgt_len    ,device=lowerCAmelCase_    ,ext_len=args.ext_len       )
        UpperCAmelCase_=     corpus.get_iterator("""test"""    ,args.batch_size    ,args.tgt_len    ,device=lowerCAmelCase_    ,ext_len=args.ext_len       )
  
        # Load a pre-trained model
        UpperCAmelCase_=     TransfoXLLMHeadModel.from_pretrained(args.model_name       )
        model.to(lowerCAmelCase_       )
  
        logger.info(
            """Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}""".format(
                args.batch_size    ,args.tgt_len    ,args.ext_len    ,args.mem_len    ,args.clamp_len       )       )
  
        model.reset_memory_length(args.mem_len       )
        if args.clamp_len > 0:
              UpperCAmelCase_=     args.clamp_len
        if args.same_length:
              UpperCAmelCase_=     True
  
        ###############################################################################
        # Evaluation code
        ###############################################################################
        def evaluate(lowerCAmelCase_     :      Any       ):
              # Turn on evaluation mode which disables dropout.
              model.eval()
              UpperCAmelCase_,     UpperCAmelCase_=     0, 0.0
              UpperCAmelCase_=     time.time()
              with torch.no_grad():
                    UpperCAmelCase_=     None
                    for idx, (data, target, seq_len) in enumerate(lowerCAmelCase_       ):
                          UpperCAmelCase_=     model(lowerCAmelCase_    ,lm_labels=lowerCAmelCase_    ,mems=lowerCAmelCase_       )
                          UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     ret
                          UpperCAmelCase_=     loss.mean()
                          total_loss += seq_len * loss.item()
                          total_len += seq_len
                    UpperCAmelCase_=     time.time() - start_time
              logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(lowerCAmelCase_    ,10_00 * total_time / (idx + 1)       )       )
              return total_loss / total_len
  
        # Run on test data.
        if args.split == "all":
              UpperCAmelCase_=     evaluate(lowerCAmelCase_       )
              UpperCAmelCase_=     evaluate(lowerCAmelCase_       )
        elif args.split == "valid":
              UpperCAmelCase_=     evaluate(lowerCAmelCase_       )
              UpperCAmelCase_=     None
        elif args.split == "test":
              UpperCAmelCase_=     evaluate(lowerCAmelCase_       )
              UpperCAmelCase_=     None
  
        def format_log(lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      Optional[int]       ):
              UpperCAmelCase_=     """| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(lowerCAmelCase_    ,lowerCAmelCase_    ,math.exp(lowerCAmelCase_       )       )
              return log_str
  
        UpperCAmelCase_=     """"""
        if valid_loss is not None:
              log_str += format_log(lowerCAmelCase_    ,"""valid"""       )
        if test_loss is not None:
              log_str += format_log(lowerCAmelCase_    ,"""test"""       )
  
        logger.info("""=""" * 1_00       )
        logger.info(lowerCAmelCase_       )
        logger.info("""=""" * 1_00       )
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
style_context: 
  
  
  
  import copy
  from typing import Dict
  
  from ...generation.configuration_utils import GenerationConfig
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Optional[int]	     =							"semantic"
  
  
  
  
  	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[str]=10_000		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		int=768		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		int=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		int=10_048		,						__UpperCAmelCase			:		Dict=129_595		,						__UpperCAmelCase			:		Optional[Any]=129_599		,						__UpperCAmelCase			:		Tuple=10_000		,						__UpperCAmelCase			:		List[Any]=256		,						__UpperCAmelCase			:		Union[str, Any]=49.9		,						**__UpperCAmelCase			:		Optional[Any]		,						)    ->				Tuple:
  							super().__init__(
  							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						eos_token_id=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						max_new_tokens=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     text_encoding_offset
  							UpperCAmelCase_=     text_pad_token
  							UpperCAmelCase_=     eos_token_id
  							UpperCAmelCase_=     semantic_infer_token
  							UpperCAmelCase_=     semantic_vocab_size
  							UpperCAmelCase_=     max_input_semantic_length
  							UpperCAmelCase_=     semantic_rate_hz
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							"coarse_acoustics"
  
  
  
  
  	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=False		,						__UpperCAmelCase			:		List[str]=False		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=12_048		,						__UpperCAmelCase			:		Optional[Any]=75		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[int]=12_050		,						__UpperCAmelCase			:		Union[str, Any]=256		,						__UpperCAmelCase			:		int = 630		,						__UpperCAmelCase			:		int = 60		,						**__UpperCAmelCase			:		int		,						)    ->				Union[str, Any]:
  							super().__init__(
  							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     coarse_semantic_pad_token
  							UpperCAmelCase_=     coarse_rate_hz
  							UpperCAmelCase_=     n_coarse_codebooks
  							UpperCAmelCase_=     coarse_infer_token
  							UpperCAmelCase_=     max_coarse_input_length
  							UpperCAmelCase_=     max_coarse_history
  							UpperCAmelCase_=     sliding_window_len
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							"fine_acoustics"
  
  
  
  
  	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Tuple=0.5		,						__UpperCAmelCase			:		Optional[Any]=512		,						__UpperCAmelCase			:		Optional[Any]=1_024		,						__UpperCAmelCase			:		List[str]=8		,						**__UpperCAmelCase			:		str		,						)    ->				Tuple:
  							super().__init__(temperature=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     max_fine_history_length
  							UpperCAmelCase_=     max_fine_input_length
  							UpperCAmelCase_=     n_fine_codebooks
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							"bark"
  	a__   :      List[str]	     =							True
  
  
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		str=24_000		,						__UpperCAmelCase			:		List[str]=1_024		,						**__UpperCAmelCase			:		Optional[int]		,						)    ->				List[str]:
  							if semantic_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""semantic_config is None. initializing the semantic model with default values."""  )
  
  							if coarse_acoustics_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""  )
  
  							if fine_acoustics_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""  )
  
  							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
  							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
  							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )
  
  							UpperCAmelCase_=     sample_rate
  							UpperCAmelCase_=     codebook_size
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				Any:
  							return cls(
  							    semantic_config=semantic_config.to_dict()		,						coarse_acoustics_config=coarse_acoustics_config.to_dict()		,						fine_acoustics_config=fine_acoustics_config.to_dict()		,						**__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
  							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )
  
  							UpperCAmelCase_=     self.semantic_config.to_dict()
  							UpperCAmelCase_=     self.coarse_acoustics_config.to_dict()
  							UpperCAmelCase_=     self.fine_acoustics_config.to_dict()
  
  							UpperCAmelCase_=     self.__class__.model_type
  							return output
  
  
  
  
  