'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase__						:List[str]          =			logging.get_logger(__name__)
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Any:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				UniSpeechSatForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE,						config=_SCREAMING_SNAKE_CASE     )
     __lowerCamelCase						:       List[Any]				   =				downstream_dict["""projector.weight"""]
     __lowerCamelCase						:       str				   =				downstream_dict["""projector.bias"""]
     __lowerCamelCase						:       List[str]				   =				downstream_dict["""model.post_net.linear.weight"""]
     __lowerCamelCase						:       Any				   =				downstream_dict["""model.post_net.linear.bias"""]
     return model
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				UniSpeechSatForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE,						config=_SCREAMING_SNAKE_CASE     )
     __lowerCamelCase						:       Dict				   =				downstream_dict["""model.linear.weight"""]
     __lowerCamelCase						:       Union[str, Any]				   =				downstream_dict["""model.linear.bias"""]
     return model
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       Optional[Any]				   =				UniSpeechSatForXVector.from_pretrained(_SCREAMING_SNAKE_CASE,						config=_SCREAMING_SNAKE_CASE     )
     __lowerCamelCase						:       int				   =				downstream_dict["""connector.weight"""]
     __lowerCamelCase						:       List[Any]				   =				downstream_dict["""connector.bias"""]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
          __lowerCamelCase						:       Tuple				   =				downstream_dict[
              f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
          ]
          __lowerCamelCase						:       Dict				   =				downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

     __lowerCamelCase						:       Optional[int]				   =				downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
     __lowerCamelCase						:       str				   =				downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
     __lowerCamelCase						:       List[str]				   =				downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
     __lowerCamelCase						:       Optional[int]				   =				downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
     __lowerCamelCase						:       Union[str, Any]				   =				downstream_dict["""objective.W"""]
     return model
@torch.no_grad()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				torch.load(_SCREAMING_SNAKE_CASE,						map_location="""cpu"""     )

     __lowerCamelCase						:       Dict				   =				checkpoint["""Downstream"""]

     __lowerCamelCase						:       int				   =				UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
     __lowerCamelCase						:       int				   =				WavaVecaFeatureExtractor.from_pretrained(
         _SCREAMING_SNAKE_CASE,						return_attention_mask=_SCREAMING_SNAKE_CASE,						do_normalize=_SCREAMING_SNAKE_CASE     )

     __lowerCamelCase						:       str				   =				hf_config.architectures[0]
     if arch.endswith("""ForSequenceClassification"""     ):
          __lowerCamelCase						:       str				   =				convert_classification(_SCREAMING_SNAKE_CASE,						_SCREAMING_SNAKE_CASE,						_SCREAMING_SNAKE_CASE     )
     elif arch.endswith("""ForAudioFrameClassification"""     ):
          __lowerCamelCase						:       Any				   =				convert_diarization(_SCREAMING_SNAKE_CASE,						_SCREAMING_SNAKE_CASE,						_SCREAMING_SNAKE_CASE     )
     elif arch.endswith("""ForXVector"""     ):
          __lowerCamelCase						:       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:
          __lowerCamelCase						:       List[Any]				   =				checkpoint["""Featurizer"""]["""weights"""]

     hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE     )
     hf_model.save_pretrained(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			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.""")
   UpperCAmelCase__						:Any          =			parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











'''simple docstring'''

def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     # Return True if there is node that has not iterated.
     __lowerCamelCase						:       Optional[Any]				   =				[False] * len(_lowercase     )
     __lowerCamelCase						:       Dict				   =				[]
     queue.append(_lowercase     )
     __lowerCamelCase						:       Optional[int]				   =				True

     while queue:
          __lowerCamelCase						:       Optional[Any]				   =				queue.pop(0     )
          for ind in range(len(graph[u]     )     ):
               if visited[ind] is False and graph[u][ind] > 0:
                    queue.append(_lowercase     )
                    __lowerCamelCase						:       List[str]				   =				True
                    __lowerCamelCase						:       str				   =				u

     return visited[t]
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # This array is filled by BFS and to store path
     __lowerCamelCase						:       Any				   =				[-1] * (len(_lowercase     ))
     __lowerCamelCase						:       Optional[int]				   =				0
     while bfs(_lowercase,						_lowercase,						_lowercase,						_lowercase     ):
          __lowerCamelCase						:       List[Any]				   =				float("""Inf"""     )
          __lowerCamelCase						:       Union[str, Any]				   =				sink

          while s != source:
               # Find the minimum value in select path
               __lowerCamelCase						:       Optional[int]				   =				min(_lowercase,						graph[parent[s]][s]     )
               __lowerCamelCase						:       Union[str, Any]				   =				parent[s]

          max_flow += path_flow
          __lowerCamelCase						:       str				   =				sink

          while v != source:
               __lowerCamelCase						:       List[Any]				   =				parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               __lowerCamelCase						:       Dict				   =				parent[v]
     return max_flow


UpperCAmelCase__						:str          =			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__       ,				UpperCAmelCase__						:Optional[Any]          =			0, 5
print(ford_fulkerson(graph, source, sink))



