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


lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class        SCREAMING_SNAKE_CASE     (snake_case_   ):
   _UpperCamelCase					:	Optional[Any]				      = """transfo-xl"""
   _UpperCamelCase					:	List[str]				      = ["""mems"""]
   _UpperCamelCase					:	Optional[Any]				      = {
       """n_token""": """vocab_size""",
       """hidden_size""": """d_model""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }


   def __init__(					self    :       Any    ,  a    :       Tuple=267_735    ,  a    :       Optional[int]=[20_000, 40_000, 200_000]    ,  a    :       Optional[Any]=1_024    ,  a    :       Union[str, Any]=1_024    ,  a    :       Any=16    ,  a    :       List[str]=64    ,  a    :       Tuple=4_096    ,  a    :       Union[str, Any]=4    ,  a    :       Union[str, Any]=False    ,  a    :       List[Any]=18    ,  a    :       Optional[Any]=1_600    ,  a    :       Dict=1_000    ,  a    :       Union[str, Any]=True    ,  a    :       str=True    ,  a    :       List[str]=0    ,  a    :       Union[str, Any]=-1    ,  a    :       Optional[int]=True    ,  a    :       Optional[int]=0.1    ,  a    :       List[Any]=0.0    ,  a    :       Tuple=True    ,  a    :       Optional[Any]="normal"    ,  a    :       int=0.01    ,  a    :       Union[str, Any]=0.01    ,  a    :       int=0.02    ,  a    :       List[Any]=1E-5    ,  a    :       Dict=0    ,  **a    :       Union[str, Any]    ,  )->     Tuple:





     """simple docstring"""







     lowercase__			    =						vocab_size
     lowercase__			    =						[]
     self.cutoffs.extend(a )
     if proj_share_all_but_first:
       lowercase__			    =						[False] + [True] * len(self.cutoffs )
     else:
       lowercase__			    =						[False] + [False] * len(self.cutoffs )
     lowercase__			    =						d_model
     lowercase__			    =						d_embed
     lowercase__			    =						d_head
     lowercase__			    =						d_inner
     lowercase__			    =						div_val
     lowercase__			    =						pre_lnorm
     lowercase__			    =						n_layer
     lowercase__			    =						n_head
     lowercase__			    =						mem_len
     lowercase__			    =						same_length
     lowercase__			    =						attn_type
     lowercase__			    =						clamp_len
     lowercase__			    =						sample_softmax
     lowercase__			    =						adaptive
     lowercase__			    =						dropout
     lowercase__			    =						dropatt
     lowercase__			    =						untie_r
     lowercase__			    =						init
     lowercase__			    =						init_range
     lowercase__			    =						proj_init_std
     lowercase__			    =						init_std
     lowercase__			    =						layer_norm_epsilon
     super().__init__(eos_token_id=a    ,  **a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Any:





     """simple docstring"""







     logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
     return -1


   @max_position_embeddings.setter
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       Optional[Any] )->     Optional[int]:





     """simple docstring"""







     raise NotImplementedError(
         f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







     return self.tokenizer.decode(*a    ,  **a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


