"""simple docstring"""







import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class  UpperCamelCase	:
   def __init__(    self  ,__UpperCamelCase=2  ,__UpperCamelCase=3  ,__UpperCamelCase=64  ,__UpperCamelCase=None    )			->   str:


        '''simple docstring'''


        lowercase_			:       int									=			np.random.default_rng(__lowerCamelCase    )
        lowercase_			:       List[Any]									=			length
        lowercase_			:       List[str]									=			rng.normal(size=(length,)    ).astype(np.floataa    )
        lowercase_			:       Optional[int]									=			a * self.x + b + rng.normal(scale=0.1  ,size=(length,)    ).astype(np.floataa    )
   def __len__(    self    )			->   int:


        '''simple docstring'''


        return self.length







   def __getitem__(    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return {"x": self.x[i], "y": self.y[i]}







class  UpperCamelCase	(    torch.nn.Module						):
   def __init__(    self  ,__UpperCamelCase=0  ,__UpperCamelCase=0  ,__UpperCamelCase=False    )			->   List[Any]:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       str									=			torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
        lowercase_			:       int									=			torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
        lowercase_			:       List[str]									=			True







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=None    )			->   Tuple:


        '''simple docstring'''


        if self.first_batch:
             print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
             lowercase_			:       Optional[int]									=			False
        return x * self.a[0] + self.b[0]







class  UpperCamelCase	(    torch.nn.Module						):
   def __init__(    self  ,__UpperCamelCase=0  ,__UpperCamelCase=0  ,__UpperCamelCase=False    )			->   List[str]:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       Dict									=			torch.nn.Parameter(torch.tensor(__lowerCamelCase    ).float()    )
        lowercase_			:       Any									=			torch.nn.Parameter(torch.tensor(__lowerCamelCase    ).float()    )
        lowercase_			:       Dict									=			True







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=None    )			->   List[str]:


        '''simple docstring'''


        if self.first_batch:
             print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
             lowercase_			:       Dict									=			False
        return x * self.a + self.b






def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int = 16   ):
     from datasets import load_dataset
     from transformers import AutoTokenizer

     lowercase_			:       Optional[Any]									=			AutoTokenizer.from_pretrained('bert-base-cased'   )
     lowercase_			:       str									=			{'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
     lowercase_			:       Union[str, Any]									=			load_dataset('csv'  ,    data_files=lowerCamelCase_   )
     lowercase_			:       Union[str, Any]									=			datasets['''train'''].unique('label'   )

     lowercase_			:       int									=			{v: i for i, v in enumerate(lowerCamelCase_   )}

     def tokenize_function(__SCREAMING_SNAKE_CASE							:  Any   ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_			:       int									=			tokenizer(
              examples['sentence1']  ,    examples['sentence2']  ,    truncation=lowerCamelCase_  ,    max_length=lowerCamelCase_  ,    padding='max_length'   )
          if "label" in examples:
               lowercase_			:       Optional[int]									=			[label_to_id[l] for l in examples['''label''']]
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     lowercase_			:       Optional[int]									=			datasets.map(
         lowerCamelCase_  ,    batched=lowerCamelCase_  ,    remove_columns=['sentence1', 'sentence2', 'label']  ,    )

     def collate_fn(__SCREAMING_SNAKE_CASE							:  List[str]   ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(lowerCamelCase_  ,    padding='max_length'  ,    max_length=1_28  ,    return_tensors='pt'   )
          return tokenizer.pad(lowerCamelCase_  ,    padding='longest'  ,    return_tensors='pt'   )

     # Instantiate dataloaders.
     lowercase_			:       int									=			DataLoader(tokenized_datasets['train']  ,    shuffle=lowerCamelCase_  ,    collate_fn=lowerCamelCase_  ,    batch_size=2   )
     lowercase_			:       int									=			DataLoader(tokenized_datasets['validation']  ,    shuffle=lowerCamelCase_  ,    collate_fn=lowerCamelCase_  ,    batch_size=1   )

     return train_dataloader, eval_dataloader


"""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)

