


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
 from ..models.auto import AutoTokenizer




class A  :






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Dict )					->						List[Any]:
             """simple docstring"""




             raise NotImplementedError()






      def        lowerCamelCase       (					self						:     str )					->						List[str]:
             """simple docstring"""




             raise NotImplementedError()




class A  (   UpperCamelCase_     ):






      def __init__(					self						:     int			,   lowercase_						:     "AutoTokenizer"			,   lowercase_						:     bool = False			,   **lowercase_						:     int )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=tokenizer
             _lowerCamelCase       :		List[Any]		=skip_prompt
             _lowerCamelCase       :		Any		=decode_kwargs

             # variables used in the streaming process
             _lowerCamelCase       :		Optional[Any]		=[]
             _lowerCamelCase       :		Tuple		=0
             _lowerCamelCase       :		Tuple		=True






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     str )					->						Any:
             """simple docstring"""




             if len(value.shape ) > 1 and value.shape[0] > 1:
                    raise ValueError('TextStreamer only supports batch size 1' )
             elif len(value.shape ) > 1:
                    _lowerCamelCase       :		Optional[int]		=value[0]

             if self.skip_prompt and self.next_tokens_are_prompt:
                    _lowerCamelCase       :		Union[str, Any]		=False
                    return

             # Add the new token to the cache and decodes the entire thing.
             self.token_cache.extend(value.tolist() )
             _lowerCamelCase       :		Tuple		=self.tokenizer.decode(self.token_cache			,   **self.decode_kwargs )

             # After the symbol for a new line, we flush the cache.
             if text.endswith('\n' ):
                    _lowerCamelCase       :		Any		=text[self.print_len :]
                    _lowerCamelCase       :		int		=[]
                    _lowerCamelCase       :		List[Any]		=0
             # If the last token is a CJK character, we print the characters.
             elif len(lowercase_ ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
                    _lowerCamelCase       :		Dict		=text[self.print_len :]
                    self.print_len += len(lowercase_ )
             # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
             # which may change with the subsequent token -- there are probably smarter ways to do this!)
             else:
                    _lowerCamelCase       :		int		=text[self.print_len : text.rfind(' ' ) + 1]
                    self.print_len += len(lowercase_ )

             self.on_finalized_text(lowercase_ )






      def        lowerCamelCase       (					self						:     Dict )					->						List[Any]:
             """simple docstring"""




             if len(self.token_cache ) > 0:
                    _lowerCamelCase       :		Optional[Any]		=self.tokenizer.decode(self.token_cache			,   **self.decode_kwargs )
                    _lowerCamelCase       :		Tuple		=text[self.print_len :]
                    _lowerCamelCase       :		Optional[Any]		=[]
                    _lowerCamelCase       :		List[Any]		=0
             else:
                    _lowerCamelCase       :		List[Any]		=''

             _lowerCamelCase       :		Dict		=True
             self.on_finalized_text(lowercase_			,   stream_end=lowercase_ )






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     str			,   lowercase_						:     bool = False )					->						int:
             """simple docstring"""




             print(lowercase_			,   flush=lowercase_			,   end='' if not stream_end else None )






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     List[str] )					->						Optional[Any]:
             """simple docstring"""




             if (
                 (cp >= 0x4e00 and cp <= 0x9fff)
                 or (cp >= 0x3400 and cp <= 0x4dbf)  #
                 or (cp >= 0x20000 and cp <= 0x2a6df)  #
                 or (cp >= 0x2a700 and cp <= 0x2b73f)  #
                 or (cp >= 0x2b740 and cp <= 0x2b81f)  #
                 or (cp >= 0x2b820 and cp <= 0x2ceaf)  #
                 or (cp >= 0xf900 and cp <= 0xfaff)
                 or (cp >= 0x2f800 and cp <= 0x2fa1f)  #
             ):  #
                    return True

             return False




class A  (   UpperCamelCase_     ):






      def __init__(					self						:     Dict			,   lowercase_						:     "AutoTokenizer"			,   lowercase_						:     bool = False			,   lowercase_						:     Optional[float] = None			,   **lowercase_						:     Union[str, Any] )					->						str:
             """simple docstring"""




             super().__init__(lowercase_			,   lowercase_			,   **lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=Queue()
             _lowerCamelCase       :		str		=None
             _lowerCamelCase       :		Optional[Any]		=timeout






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     str			,   lowercase_						:     bool = False )					->						Optional[Any]:
             """simple docstring"""




             self.text_queue.put(lowercase_			,   timeout=self.timeout )
             if stream_end:
                    self.text_queue.put(self.stop_signal			,   timeout=self.timeout )






      def __iter__(					self						:     int )					->						str:
             """simple docstring"""




             return self






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.text_queue.get(timeout=self.timeout )
             if value == self.stop_signal:
                    raise StopIteration()
             else:
                    return value






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


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Dict      ='transfo-xl'
      UpperCamelCase__							:  int      =['mems']
      UpperCamelCase__							:  List[str]      ={
          'n_token': 'vocab_size',
          'hidden_size': 'd_model',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }






      def __init__(					self						:     Tuple			,   lowercase_						:     List[Any]=26_7735			,   lowercase_						:     List[Any]=[2_0000, 4_0000, 20_0000]			,   lowercase_						:     Tuple=1024			,   lowercase_						:     Optional[int]=1024			,   lowercase_						:     str=16			,   lowercase_						:     int=64			,   lowercase_						:     Any=4096			,   lowercase_						:     List[Any]=4			,   lowercase_						:     List[Any]=False			,   lowercase_						:     List[str]=18			,   lowercase_						:     Tuple=1600			,   lowercase_						:     List[str]=1000			,   lowercase_						:     Any=True			,   lowercase_						:     str=True			,   lowercase_						:     Optional[int]=0			,   lowercase_						:     List[Any]=-1			,   lowercase_						:     Optional[int]=True			,   lowercase_						:     Any=0.1			,   lowercase_						:     Tuple=0.0			,   lowercase_						:     str=True			,   lowercase_						:     Union[str, Any]="normal"			,   lowercase_						:     Dict=0.01			,   lowercase_						:     Optional[Any]=0.01			,   lowercase_						:     List[Any]=0.02			,   lowercase_						:     List[Any]=1E-5			,   lowercase_						:     Tuple=0			,   **lowercase_						:     Tuple			,   )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=vocab_size
             _lowerCamelCase       :		Optional[Any]		=[]
             self.cutoffs.extend(lowercase_ )
             if proj_share_all_but_first:
                    _lowerCamelCase       :		List[str]		=[False] + [True] * len(self.cutoffs )
             else:
                    _lowerCamelCase       :		str		=[False] + [False] * len(self.cutoffs )
             _lowerCamelCase       :		str		=d_model
             _lowerCamelCase       :		str		=d_embed
             _lowerCamelCase       :		str		=d_head
             _lowerCamelCase       :		Dict		=d_inner
             _lowerCamelCase       :		Union[str, Any]		=div_val
             _lowerCamelCase       :		Dict		=pre_lnorm
             _lowerCamelCase       :		Tuple		=n_layer
             _lowerCamelCase       :		Optional[Any]		=n_head
             _lowerCamelCase       :		List[Any]		=mem_len
             _lowerCamelCase       :		List[Any]		=same_length
             _lowerCamelCase       :		Optional[int]		=attn_type
             _lowerCamelCase       :		List[str]		=clamp_len
             _lowerCamelCase       :		int		=sample_softmax
             _lowerCamelCase       :		List[Any]		=adaptive
             _lowerCamelCase       :		List[str]		=dropout
             _lowerCamelCase       :		Any		=dropatt
             _lowerCamelCase       :		Dict		=untie_r
             _lowerCamelCase       :		List[Any]		=init
             _lowerCamelCase       :		List[str]		=init_range
             _lowerCamelCase       :		int		=proj_init_std
             _lowerCamelCase       :		Optional[int]		=init_std
             _lowerCamelCase       :		Tuple		=layer_norm_epsilon
             super().__init__(eos_token_id=lowercase_			,   **lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """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        lowerCamelCase       (					self						:     int			,   lowercase_						:     int )					->						List[str]:
             """simple docstring"""




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


