'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase_ : Dict						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Optional[Any]				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      UniSpeechSatForSequenceClassification.from_pretrained(__lowerCamelCase ,		config=__lowerCamelCase				)
      lowercase_     =      downstream_dict["projector.weight"]
      lowercase_     =      downstream_dict["projector.bias"]
      lowercase_     =      downstream_dict["model.post_net.linear.weight"]
      lowercase_     =      downstream_dict["model.post_net.linear.bias"]
      return model


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Any				)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      UniSpeechSatForAudioFrameClassification.from_pretrained(__lowerCamelCase ,		config=__lowerCamelCase				)
      lowercase_     =      downstream_dict["model.linear.weight"]
      lowercase_     =      downstream_dict["model.linear.bias"]
      return model


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						str				)	->							Any:
      '''simple docstring'''


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

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


@torch.no_grad()
def __a (	__lowerCamelCase			:						Any ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						Optional[int]				)	->							str:
      '''simple docstring'''


      lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      checkpoint["Downstream"]

      lowercase_     =      UniSpeechSatConfig.from_pretrained(__lowerCamelCase				)
      lowercase_     =      WavaVecaFeatureExtractor.from_pretrained(
          __lowerCamelCase ,		return_attention_mask=__lowerCamelCase ,		do_normalize=__lowerCamelCase				)

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

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

      hf_feature_extractor.save_pretrained(__lowerCamelCase				)
      hf_model.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : 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.")
       lowerCAmelCase_ : int						  =      parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCAmelCase_ : Dict						  =      TypeVar("T")





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	bool = True)      ->					None:
         lowercase_     =      {}  # dictionary of lists
         lowercase_     =      directed


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	T)      ->					GraphAdjacencyList[T]:

         if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      [source_vertex]
         else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      []
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      []

         return self


   def __repr__(   self				:	str)      ->					str:
         return pformat(self.adj_list)






