





import argparse

import torch

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


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





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	Optional[int]:
						UpperCAmelCase_			:      Optional[int]     						=  UniSpeechSatForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[Any]     						=  downstream_dict['''projector.weight''']
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''projector.bias''']
						UpperCAmelCase_			:      Any     						=  downstream_dict['''model.post_net.linear.weight''']
						UpperCAmelCase_			:      str     						=  downstream_dict['''model.post_net.linear.bias''']
						return model





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	Optional[int]:
						UpperCAmelCase_			:      Optional[Any]     						=  UniSpeechSatForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Any     						=  downstream_dict['''model.linear.weight''']
						UpperCAmelCase_			:      Dict     						=  downstream_dict['''model.linear.bias''']
						return model





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Tuple     						=  UniSpeechSatForXVector.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  downstream_dict['''connector.weight''']
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''connector.bias''']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
												UpperCAmelCase_			:      Any     						=  downstream_dict[
												    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
												]
												UpperCAmelCase_			:      Dict     						=  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

						UpperCAmelCase_			:      Any     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
						UpperCAmelCase_			:      Dict     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
						UpperCAmelCase_			:      Optional[int]     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
						UpperCAmelCase_			:      int     						=  downstream_dict['''objective.W''']
						return model





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Dict     						=  torch.load(SCREAMING_SNAKE_CASE__,			map_location='''cpu'''   )

						UpperCAmelCase_			:      int     						=  checkpoint['''Downstream''']

						UpperCAmelCase_			:      List[Any]     						=  UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Tuple     						=  WavaVecaFeatureExtractor.from_pretrained(
						    SCREAMING_SNAKE_CASE__,			return_attention_mask=SCREAMING_SNAKE_CASE__,			do_normalize=SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Union[str, Any]     						=  hf_config.architectures[0]
						if arch.endswith('''ForSequenceClassification'''   ):
												UpperCAmelCase_			:      Any     						=  convert_classification(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						elif arch.endswith('''ForAudioFrameClassification'''   ):
												UpperCAmelCase_			:      Tuple     						=  convert_diarization(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						elif arch.endswith('''ForXVector'''   ):
												UpperCAmelCase_			:      Optional[int]     						=  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:
												UpperCAmelCase_			:      Tuple     						=  checkpoint['''Featurizer''']['''weights''']

						hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__   )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       Dict						=  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.")
					snake_case_     :       str						=  parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[int]   ) ->	list[list[int]]:
						UpperCAmelCase_			:      int     						=  []
						if len(SCREAMING_SNAKE_CASE__   ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__   )   ):
												UpperCAmelCase_			:      List[Any]     						=  nums.pop(0   )
												UpperCAmelCase_			:      Optional[Any]     						=  permute(SCREAMING_SNAKE_CASE__   )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__   )
												result.extend(SCREAMING_SNAKE_CASE__   )
												nums.append(SCREAMING_SNAKE_CASE__   )
						return result





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Any:

						def backtrack(SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ):
												if start == len(SCREAMING_SNAKE_CASE__   ) - 1:
																		output.append(nums[:]   )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__,			len(SCREAMING_SNAKE_CASE__   )   ):
																								UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  nums[i], nums[start]
																								backtrack(start + 1   )
																								UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  nums[i], nums[start]  # backtrack

						UpperCAmelCase_			:      Optional[int]     						=  []
						backtrack(0   )
						return output


if __name__ == "__main__":
					import doctest

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





