import argparse

import torch

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


logging.set_verbosity_info()
__lowerCamelCase			: List[str]							   =		logging.get_logger(__name__)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       Dict	)							->					List[Any]:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Optional[Any]	)							->					int:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Any	)							->					int:







  """simple docstring"""





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

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



@torch.no_grad()
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       Optional[Any]	)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      torch.load(__UpperCamelCase ,     map_location="""cpu"""	)

  SCREAMING_SNAKE_CASE__										=      checkpoint["""Downstream"""]

  SCREAMING_SNAKE_CASE__										=      UniSpeechSatConfig.from_pretrained(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      WavaVecaFeatureExtractor.from_pretrained(
      __UpperCamelCase ,     return_attention_mask=__UpperCamelCase ,     do_normalize=__UpperCamelCase	)

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

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

  hf_feature_extractor.save_pretrained(__UpperCamelCase	)
  hf_model.save_pretrained(__UpperCamelCase	)


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)

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							ComputeEnvironment.AMAZON_SAGEMAKER
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							"ml.p3.2xlarge"
   lowerCAmelCase_														=							"accelerate_sagemaker_execution_role"
   lowerCAmelCase_														=							"hf-sm"
   lowerCAmelCase_														=							"us-east-1"
   lowerCAmelCase_														=							1
   lowerCAmelCase_														=							"accelerate-sagemaker-1"
   lowerCAmelCase_														=							"1.6"
   lowerCAmelCase_														=							"4.4"
   lowerCAmelCase_														=							"train.py"
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "False",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "--do_test",
       "False",
       "--do_predict",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]







class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
     assert isinstance(converted_args["""model_name_or_path"""]       ,	_lowercase      )
     assert isinstance(converted_args["""do_train"""]       ,	_lowercase      )
     assert isinstance(converted_args["""epochs"""]       ,	_lowercase      )
     assert isinstance(converted_args["""learning_rate"""]       ,	_lowercase      )
     assert isinstance(converted_args["""max_steps"""]       ,	_lowercase      )

     with pytest.raises(_lowercase      ):
       _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args      )
