"""simple docstring"""







from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
       from ..models.auto import AutoTokenizer







class  UpperCamelCase	:
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        raise NotImplementedError()







   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        raise NotImplementedError()







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase = False  ,**__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Any									=			tokenizer
        lowercase_			:       Optional[Any]									=			skip_prompt
        lowercase_			:       Optional[int]									=			decode_kwargs

        # variables used in the streaming process
        lowercase_			:       Tuple									=			[]
        lowercase_			:       Optional[int]									=			0
        lowercase_			:       Union[str, Any]									=			True
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Tuple:


        '''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:
             lowercase_			:       Union[str, Any]									=			value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
             lowercase_			:       Any									=			False
             return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist()    )
        lowercase_			:       Optional[Any]									=			self.tokenizer.decode(self.token_cache  ,**self.decode_kwargs    )

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

        self.on_finalized_text(__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


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

        lowercase_			:       Dict									=			True
        self.on_finalized_text(__UpperCamelCase  ,stream_end=__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = False    )			->   Optional[Any]:


        '''simple docstring'''


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







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   List[str]:


        '''simple docstring'''


        if (
            (cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
            or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
            or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
            or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
            or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
            or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
            or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
            or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
        ):  #
             return True

        return False







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,**__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        super().__init__(__UpperCamelCase  ,__UpperCamelCase  ,**__UpperCamelCase    )
        lowercase_			:       Any									=			Queue()
        lowercase_			:       List[Any]									=			None
        lowercase_			:       List[str]									=			timeout
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = False    )			->   List[Any]:


        '''simple docstring'''


        self.text_queue.put(__UpperCamelCase  ,timeout=self.timeout    )
        if stream_end:
             self.text_queue.put(self.stop_signal  ,timeout=self.timeout    )
   def __iter__(    self    )			->   Any:


        '''simple docstring'''


        return self







   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			self.text_queue.get(timeout=self.timeout    )
        if value == self.stop_signal:
             raise StopIteration()
        else:
             return value


"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[str]									=			UniSpeechSatForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[Any]									=			downstream_dict['projector.weight']
     lowercase_			:       Optional[Any]									=			downstream_dict['projector.bias']
     lowercase_			:       Optional[int]									=			downstream_dict['model.post_net.linear.weight']
     lowercase_			:       Tuple									=			downstream_dict['model.post_net.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       List[Any]									=			UniSpeechSatForAudioFrameClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			downstream_dict['model.linear.weight']
     lowercase_			:       Union[str, Any]									=			downstream_dict['model.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       Any									=			UniSpeechSatForXVector.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			downstream_dict['connector.weight']
     lowercase_			:       List[str]									=			downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
          lowercase_			:       Dict									=			downstream_dict[
              F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          lowercase_			:       List[Any]									=			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     lowercase_			:       Optional[Any]									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     lowercase_			:       Dict									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     lowercase_			:       Dict									=			downstream_dict['objective.W']
     return model



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       int									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location='cpu'   )

     lowercase_			:       str									=			checkpoint['Downstream']

     lowercase_			:       str									=			UniSpeechSatConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Tuple									=			WavaVecaFeatureExtractor.from_pretrained(
         __SCREAMING_SNAKE_CASE  ,    return_attention_mask=__SCREAMING_SNAKE_CASE  ,    do_normalize=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[str]									=			hf_config.architectures[0]
     if arch.endswith('ForSequenceClassification'   ):
          lowercase_			:       List[Any]									=			convert_classification(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForAudioFrameClassification'   ):
          lowercase_			:       List[str]									=			convert_diarization(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForXVector'   ):
          lowercase_			:       Optional[Any]									=			convert_xvector(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     else:
          raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''   )

     if hf_config.use_weighted_layer_sum:
          lowercase_			:       Dict									=			checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE   )
     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE								=argparse.ArgumentParser()
       parser.add_argument(
           "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
       )
       parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
       parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
       __SCREAMING_SNAKE_CASE								=parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

