


import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE	:							List[Any]           =  logging.get_logger(__name__)

def 		snake_case_   (				lowercase__     :    Union[str, Any]       ,     lowercase__     :    int       ,     lowercase__     :    Dict     ):






				'''simple docstring'''






				_lowerCAmelCase  						=WavLMForSequenceClassification.from_pretrained(lowercase__       ,     config=lowercase__     )
				_lowerCAmelCase  						=downstream_dict["""projector.weight"""]
				_lowerCAmelCase  						=downstream_dict["""projector.bias"""]
				_lowerCAmelCase  						=downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase  						=downstream_dict["""model.post_net.linear.bias"""]
				return model

def 		snake_case_   (				lowercase__     :    List[str]       ,     lowercase__     :    str       ,     lowercase__     :    Tuple     ):






				'''simple docstring'''






				_lowerCAmelCase  						=WavLMForAudioFrameClassification.from_pretrained(lowercase__       ,     config=lowercase__     )
				_lowerCAmelCase  						=downstream_dict["""model.linear.weight"""]
				_lowerCAmelCase  						=downstream_dict["""model.linear.bias"""]
				return model

def 		snake_case_   (				lowercase__     :    Tuple       ,     lowercase__     :    Dict       ,     lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=WavLMForXVector.from_pretrained(lowercase__       ,     config=lowercase__     )
				_lowerCAmelCase  						=downstream_dict["""connector.weight"""]
				_lowerCAmelCase  						=downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
								_lowerCAmelCase  						=downstream_dict[
								    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
								]
								_lowerCAmelCase  						=downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

				_lowerCAmelCase  						=downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				_lowerCAmelCase  						=downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				_lowerCAmelCase  						=downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				_lowerCAmelCase  						=downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				_lowerCAmelCase  						=downstream_dict["""objective.W"""]
				return model

@torch.no_grad()
def 		snake_case_   (				lowercase__     :    str       ,     lowercase__     :    Tuple       ,     lowercase__     :    Optional[Any]       ,     lowercase__     :    Any     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.load(lowercase__       ,     map_location="""cpu"""     )

				_lowerCAmelCase  						=checkpoint["""Downstream"""]

				_lowerCAmelCase  						=WavLMConfig.from_pretrained(lowercase__     )
				_lowerCAmelCase  						=WavaVecaFeatureExtractor.from_pretrained(
				    lowercase__       ,     return_attention_mask=lowercase__       ,     do_normalize=lowercase__     )

				_lowerCAmelCase  						=hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""     ):
								_lowerCAmelCase  						=convert_classification(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif arch.endswith("""ForAudioFrameClassification"""     ):
								_lowerCAmelCase  						=convert_diarization(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif arch.endswith("""ForXVector"""     ):
								_lowerCAmelCase  						=convert_xvector(lowercase__       ,     lowercase__       ,     lowercase__     )
				else:
								raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"     )

				if hf_config.use_weighted_layer_sum:
								_lowerCAmelCase  						=checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(lowercase__     )
				hf_model.save_pretrained(lowercase__     )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE	:							List[str]           =  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	:							int           =  parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

