"""simple docstring"""







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       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	bool						)  ->							torch.Tensor:
   # The first operation in a checkpoint can't be in-place, but it's
   # nice to have in-place addition during inference. Thus...
   if not inplace:
      __lowerCamelCase    		=					ma + ma
   else:
      ma += ma

   return ma



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	List[int]						)  ->							torch.Tensor:
   __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       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	int						)  ->							torch.Tensor:
   return t.reshape(t.shape[:-no_dims] + (-1,)						)



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	int      , lowercase_     :	float = 1e-4						)  ->							torch.Tensor:
   __lowerCamelCase    		=					mask.expand(*value.shape						)
   return torch.sum(mask * value      , dim=lowercase_						) / (eps + torch.sum(lowercase_      , dim=lowercase_						))



def       A__    (	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						)  ->							torch.Tensor:
   __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       A__    (	lowercase_     :	Callable[[list], Any]      , lowercase_     :	List[dict]						)  ->							dict:
   __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       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor						)  ->							torch.Tensor:
   __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       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	int = 0      , lowercase_     :	int = 0						)  ->							torch.Tensor:
   __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	:		Dict   =   TypeVar('''T''')



def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	Dict[Any, Union[dict, list, tuple, T]]      , lowercase_     :	Type[T]						)  ->							Dict[Any, Union[dict, list, tuple, Any]]:
   __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       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	T      , lowercase_     :	Type[T]						)  ->							Any:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	dict      , lowercase_     :	Type[T]						)  ->							dict:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	list      , lowercase_     :	Type[T]						)  ->							list:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	tuple      , lowercase_     :	Type[T]						)  ->							tuple:
   ...



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]						)  ->							Any:
   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	:		Union[str, Any]   =   partial(tree_map, leaf_type=torch.Tensor)







"""simple docstring"""







import argparse

import torch

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


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Any      , lowercase_     :	Optional[int]						)  ->							Union[str, Any]:
   __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       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	int						)  ->							List[Any]:
   __lowerCamelCase    		=					WavLMForAudioFrameClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['model.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.linear.bias']
   return model



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Dict      , lowercase_     :	List[str]						)  ->							Tuple:
   __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       A__    (	lowercase_     :	str      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]      , lowercase_     :	Union[str, Any]						)  ->							Optional[Any]:
   __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	:		Optional[Any]   =   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	:		Dict   =   parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






