'''simple docstring'''




from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
   from ..models.auto import AutoTokenizer






class 		__A       :






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              raise NotImplementedError()







       def 			_snake_case						(      self      ):
              raise NotImplementedError()






class 		__A       (_lowerCAmelCase   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_ = False				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		tokenizer
              __UpperCAmelCase  :				Any       				=		skip_prompt
              __UpperCAmelCase  :				Optional[int]       				=		decode_kwargs

              # variables used in the streaming process
              __UpperCAmelCase  :				Union[str, Any]       				=		[]
              __UpperCAmelCase  :				Any       				=		0
              __UpperCAmelCase  :				Any       				=		True






       def 			_snake_case						(      self				,     UpperCamelCase_      ):







              if len(value.shape      ) > 1 and value.shape[0] > 1:
                     raise ValueError("TextStreamer only supports batch size 1"      )
              elif len(value.shape      ) > 1:
                     __UpperCAmelCase  :				Tuple       				=		value[0]

              if self.skip_prompt and self.next_tokens_are_prompt:
                     __UpperCAmelCase  :				int       				=		False
                     return

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

              # After the symbol for a new line, we flush the cache.
              if text.endswith("\n"      ):
                     __UpperCAmelCase  :				Tuple       				=		text[self.print_len :]
                     __UpperCAmelCase  :				Optional[int]       				=		[]
                     __UpperCAmelCase  :				Dict       				=		0
              # If the last token is a CJK character, we print the characters.
              elif len(_lowerCAmelCase      ) > 0 and self._is_chinese_char(ord(text[-1]      )      ):
                     __UpperCAmelCase  :				int       				=		text[self.print_len :]
                     self.print_len += len(_lowerCAmelCase      )
              # 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:
                     __UpperCAmelCase  :				Dict       				=		text[self.print_len : text.rfind(" "      ) + 1]
                     self.print_len += len(_lowerCAmelCase      )

              self.on_finalized_text(_lowerCAmelCase      )






       def 			_snake_case						(      self      ):







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

              __UpperCAmelCase  :				List[Any]       				=		True
              self.on_finalized_text(_lowerCAmelCase				,     stream_end=_lowerCAmelCase      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = False      ):
              print(_lowerCAmelCase				,     flush=_lowerCAmelCase				,     end="" if not stream_end else None      )







       def 			_snake_case						(      self				,     UpperCamelCase_      ):







              if (
                  (cp >= 0X4E00 and cp <= 0X9FFF)
                  or (cp >= 0X3400 and cp <= 0X4DBF)  #
                  or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
                  or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
                  or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
                  or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
                  or (cp >= 0XF900 and cp <= 0XFAFF)
                  or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
              ):  #
                     return True

              return False






class 		__A       (_lowerCAmelCase   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     **UpperCamelCase_      ):
              super().__init__(_lowerCAmelCase				,     _lowerCAmelCase				,     **_lowerCAmelCase      )
              __UpperCAmelCase  :				List[Any]       				=		Queue()
              __UpperCAmelCase  :				Any       				=		None
              __UpperCAmelCase  :				Any       				=		timeout






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = False      ):
              self.text_queue.put(_lowerCAmelCase				,     timeout=self.timeout      )
              if stream_end:
                     self.text_queue.put(self.stop_signal				,     timeout=self.timeout      )






       def __iter__(      self      ):
              return self







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		self.text_queue.get(timeout=self.timeout      )
              if value == self.stop_signal:
                     raise StopIteration()
              else:
                     return value



'''simple docstring'''




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


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	int 							=   {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					"speech_to_text_2"
       snake_case       :List[Any]				    =					["past_key_values"]
       snake_case       :str				    =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}






       def __init__(      self				,     UpperCamelCase_=1_00_00				,     UpperCamelCase_=6				,     UpperCamelCase_=20_48				,     UpperCamelCase_=4				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_="relu"				,     UpperCamelCase_=2_56				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=2				,     UpperCamelCase_=True				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_=10_24				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Any       				=		vocab_size
              __UpperCAmelCase  :				Optional[int]       				=		d_model
              __UpperCAmelCase  :				Tuple       				=		decoder_ffn_dim
              __UpperCAmelCase  :				List[str]       				=		decoder_layers
              __UpperCAmelCase  :				str       				=		decoder_attention_heads
              __UpperCAmelCase  :				Dict       				=		dropout
              __UpperCAmelCase  :				Optional[Any]       				=		attention_dropout
              __UpperCAmelCase  :				int       				=		activation_dropout
              __UpperCAmelCase  :				Dict       				=		activation_function
              __UpperCAmelCase  :				Tuple       				=		init_std
              __UpperCAmelCase  :				Any       				=		decoder_layerdrop
              __UpperCAmelCase  :				str       				=		use_cache
              __UpperCAmelCase  :				int       				=		decoder_layers
              __UpperCAmelCase  :				Any       				=		scale_embedding  # scale factor will be sqrt(d_model) if True
              __UpperCAmelCase  :				Union[str, Any]       				=		max_target_positions

              super().__init__(
                  pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     decoder_start_token_id=UpperCamelCase_				,     **UpperCamelCase_				,     )






