



'''simple docstring'''







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowercase__     :  int    					=			logging.get_logger(__name__)

lowercase__     :  Optional[Any]    					=			['UtteranceLevel']





@torch.no_grad()
def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			torch.load(lowerCAmelCase_					,  map_location="cpu"	)
    if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
        raise NotImplementedError(F'The supported s3prl models are {SUPPORTED_MODELS}'	)

    UpperCamelCase__      =			checkpoint["Downstream"]

    UpperCamelCase__      =			HubertConfig.from_pretrained(lowerCAmelCase_	)
    UpperCamelCase__      =			HubertForSequenceClassification.from_pretrained(lowerCAmelCase_					,  config=lowerCAmelCase_	)
    UpperCamelCase__      =			WavaVecaFeatureExtractor.from_pretrained(
        lowerCAmelCase_					,  return_attention_mask=lowerCAmelCase_					,  do_normalize=lowerCAmelCase_	)

    if hf_congfig.use_weighted_layer_sum:
        UpperCamelCase__      =			checkpoint["Featurizer"]["weights"]

    UpperCamelCase__      =			downstream_dict["projector.weight"]
    UpperCamelCase__      =			downstream_dict["projector.bias"]
    UpperCamelCase__      =			downstream_dict["model.post_net.linear.weight"]
    UpperCamelCase__      =			downstream_dict["model.post_net.linear.bias"]

    hf_feature_extractor.save_pretrained(lowerCAmelCase_	)
    hf_model.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
      lowercase__     :  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.')
      lowercase__     :  Optional[int]    					=			parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









'''simple docstring'''







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types

class       lowerCamelCase_							(	unittest.TestCase					,    lowerCAmelCase_  ):






   '''simple docstring'''

   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			load_tool("translation" )
       self.tool.setup()
       UpperCamelCase__      =			load_tool("translation"     ,						remote=__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.remote_tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.remote_tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       # There is a single output
       if len(self.tool.outputs ) == 1:
           UpperCamelCase__      =			[outputs]

       self.assertListEqual(output_types(__UpperCamelCase )     ,						self.tool.outputs )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       if not isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           UpperCamelCase__      =			[outputs]

       self.assertEqual(len(__UpperCamelCase )     ,						len(self.tool.outputs ) )

       for output, output_type in zip(__UpperCamelCase     ,						self.tool.outputs ):
           UpperCamelCase__      =			AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(__UpperCamelCase     ,						__UpperCamelCase ) )







   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]

       UpperCamelCase__      =			[]

       for _input, input_type in zip(__UpperCamelCase     ,						self.tool.inputs ):
           if isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
               _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
           else:
               _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       if not isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           UpperCamelCase__      =			[outputs]

       self.assertEqual(len(__UpperCamelCase )     ,						len(self.tool.outputs ) )




