



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__		(   lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Dict):





				'''simple docstring'''

				lowerCAmelCase__					:    Dict												=					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_	,config=lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					downstream_dict['''projector.weight''']
				lowerCAmelCase__					:    Union[str, Any]												=					downstream_dict['''projector.bias''']
				lowerCAmelCase__					:    int												=					downstream_dict['''model.post_net.linear.weight''']
				lowerCAmelCase__					:    List[str]												=					downstream_dict['''model.post_net.linear.bias''']
				return model






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		int	,lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_	,config=lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					downstream_dict['''model.linear.weight''']
				lowerCAmelCase__					:    List[Any]												=					downstream_dict['''model.linear.bias''']
				return model






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Dict):





				'''simple docstring'''

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

				lowerCAmelCase__					:    List[str]												=					downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
				lowerCAmelCase__					:    List[Any]												=					downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
				lowerCAmelCase__					:    str												=					downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
				lowerCAmelCase__					:    Any												=					downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
				lowerCAmelCase__					:    Any												=					downstream_dict['''objective.W''']
				return model






@torch.no_grad()
def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		str	,lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					torch.load(lowerCamelCase_	,map_location='''cpu''')

				lowerCAmelCase__					:    List[Any]												=					checkpoint['''Downstream''']

				lowerCAmelCase__					:    str												=					UniSpeechSatConfig.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					WavaVecaFeatureExtractor.from_pretrained(
				    lowerCamelCase_	,return_attention_mask=lowerCamelCase_	,do_normalize=lowerCamelCase_)

				lowerCAmelCase__					:    Optional[int]												=					hf_config.architectures[0]
				if arch.endswith('''ForSequenceClassification'''):
								lowerCAmelCase__					:    List[Any]												=					convert_classification(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)
				elif arch.endswith('''ForAudioFrameClassification'''):
								lowerCAmelCase__					:    Any												=					convert_diarization(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)
				elif arch.endswith('''ForXVector'''):
								lowerCAmelCase__					:    Optional[int]												=					convert_xvector(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)
				else:
								raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}""")

				if hf_config.use_weighted_layer_sum:
								lowerCAmelCase__					:    List[str]												=					checkpoint['''Featurizer''']['''weights''']

				hf_feature_extractor.save_pretrained(lowerCamelCase_)
				hf_model.save_pretrained(lowerCamelCase_)


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






def      lowerCAmelCase__		(   lowerCamelCase_		:		str	,lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					len(lowerCamelCase_)
				lowerCAmelCase__					:    str												=					len(lowerCamelCase_)
				lowerCAmelCase__					:    Union[str, Any]												=					[[False for _ in range(m + 1)] for _ in range(n + 1)]
				lowerCAmelCase__					:    List[Any]												=					True
				for i in range(lowerCamelCase_):
								for j in range(m + 1):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				lowerCAmelCase__					:    int												=					True
																if a[i].islower():
																				lowerCAmelCase__					:    int												=					True
				return dp[n][m]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

