





import argparse

import torch

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


logging.set_verbosity_info()
lowercase    : List[Any]    	=  logging.get_logger(__name__)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
							_UpperCamelCase              =						UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase    ,       config=lowerCAmelCase					)
							_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''']
							return model


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
							_UpperCamelCase              =						UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase    ,       config=lowerCAmelCase					)
							_UpperCamelCase              =						downstream_dict['''model.linear.weight''']
							_UpperCamelCase              =						downstream_dict['''model.linear.bias''']
							return model


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

							_UpperCamelCase              =						downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
							_UpperCamelCase              =						downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
							_UpperCamelCase              =						downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
							_UpperCamelCase              =						downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
							_UpperCamelCase              =						downstream_dict['''objective.W''']
							return model


@torch.no_grad()
def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
							_UpperCamelCase              =						torch.load(lowerCAmelCase    ,       map_location='''cpu'''					)

							_UpperCamelCase              =						checkpoint['''Downstream''']

							_UpperCamelCase              =						UniSpeechSatConfig.from_pretrained(lowerCAmelCase					)
							_UpperCamelCase              =						WavaVecaFeatureExtractor.from_pretrained(
							    lowerCAmelCase    ,       return_attention_mask=lowerCAmelCase    ,       do_normalize=lowerCAmelCase					)

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

							if hf_config.use_weighted_layer_sum:
														_UpperCamelCase              =						checkpoint['''Featurizer''']['''weights''']

							hf_feature_extractor.save_pretrained(lowerCAmelCase					)
							hf_model.save_pretrained(lowerCAmelCase					)


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













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						[]
       if len(lowerCAmelCase					) == 1:
              return [nums.copy()]
       for _ in range(len(lowerCAmelCase					)					):
              _UpperCamelCase              =						nums.pop(0					)
              _UpperCamelCase              =						permute(lowerCAmelCase					)
              for perm in permutations:
                     perm.append(lowerCAmelCase					)
              result.extend(lowerCAmelCase					)
              nums.append(lowerCAmelCase					)
       return result


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       def backtrack(lowerCAmelCase					):
              if start == len(lowerCAmelCase					) - 1:
                     output.append(nums[:]					)
              else:
                     for i in range(lowerCAmelCase    ,       len(lowerCAmelCase					)					):
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]
                            backtrack(start + 1					)
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]  # backtrack

       _UpperCamelCase              =						[]
       backtrack(0					)
       return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       lowercase    : Optional[Any]    	=  permutea([1, 2, 3])
       print(res)
       doctest.testmod()






