"""simple docstring"""







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						't5'
   lowercase							=						['past_key_values']
   lowercase							=						{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
   def __init__(    self  ,__UpperCamelCase=3_2128  ,__UpperCamelCase=512  ,__UpperCamelCase=64  ,__UpperCamelCase=2048  ,__UpperCamelCase=6  ,__UpperCamelCase=None  ,__UpperCamelCase=8  ,__UpperCamelCase=32  ,__UpperCamelCase=128  ,__UpperCamelCase=0.1  ,__UpperCamelCase=1e-6  ,__UpperCamelCase=1.0  ,__UpperCamelCase="relu"  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,**__UpperCamelCase  ,)			->   str:


        '''simple docstring'''


        lowercase_			:       Any									=			vocab_size
        lowercase_			:       Tuple									=			d_model
        lowercase_			:       List[Any]									=			d_kv
        lowercase_			:       List[Any]									=			d_ff
        lowercase_			:       List[Any]									=			num_layers
        lowercase_			:       Optional[Any]									=			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowercase_			:       List[Any]									=			num_heads
        lowercase_			:       int									=			relative_attention_num_buckets
        lowercase_			:       Tuple									=			relative_attention_max_distance
        lowercase_			:       List[Any]									=			dropout_rate
        lowercase_			:       Optional[int]									=			layer_norm_epsilon
        lowercase_			:       Tuple									=			initializer_factor
        lowercase_			:       Union[str, Any]									=			feed_forward_proj
        lowercase_			:       List[Any]									=			use_cache

        lowercase_			:       List[str]									=			self.feed_forward_proj.split('-'    )
        lowercase_			:       Optional[Any]									=			act_info[-1]
        lowercase_			:       int									=			act_info[0] == 'gated'

        if len(__UpperCamelCase    ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase    ) > 2:
             raise ValueError(
                 f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                 'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                 '\'gated-gelu\' or \'relu\''    )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
             lowercase_			:       Tuple									=			'gelu_new'

        super().__init__(
            pad_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase  ,is_encoder_decoder=__UpperCamelCase  ,**__UpperCamelCase  ,)







class  UpperCamelCase	(    lowercase_						):
   @property
   def     _UpperCAmelCase     (    self    )			->   Mapping[str, Mapping[int, str]]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			{
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
             lowercase_			:       Any									=			'past_encoder_sequence + sequence'
             lowercase_			:       List[str]									=			{0: 'batch'}
             lowercase_			:       Dict									=			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
             lowercase_			:       Union[str, Any]									=			{0: 'batch', 1: 'decoder_sequence'}
             lowercase_			:       List[str]									=			{0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
             self.fill_with_past_key_values_(__UpperCamelCase  ,direction='inputs'    )

        return common_inputs







   @property
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return 13


"""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)

