


import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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






def   _lowercase(					__a				:						List[str]    ,     __a				:						List[Any]    ,     __a				:						Tuple     ):
							a__      =WavaVecaForSequenceClassification.from_pretrained(__a    ,     config=__a     )
							a__      =downstream_dict['projector.weight']
							a__      =downstream_dict['projector.bias']
							a__      =downstream_dict['model.post_net.linear.weight']
							a__      =downstream_dict['model.post_net.linear.bias']
							return model






def   _lowercase(					__a				:						str    ,     __a				:						List[Any]    ,     __a				:						Tuple     ):
							a__      =WavaVecaForAudioFrameClassification.from_pretrained(__a    ,     config=__a     )
							a__      =downstream_dict['model.linear.weight']
							a__      =downstream_dict['model.linear.bias']
							return model






def   _lowercase(					__a				:						List[Any]    ,     __a				:						Union[str, Any]    ,     __a				:						Union[str, Any]     ):
							a__      =WavaVecaForXVector.from_pretrained(__a    ,     config=__a     )
							a__      =downstream_dict['connector.weight']
							a__      =downstream_dict['connector.bias']
							for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
														a__      =downstream_dict[
														    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
														]
														a__      =downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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






@torch.no_grad()
def   _lowercase(					__a				:						Tuple    ,     __a				:						Optional[int]    ,     __a				:						List[Any]    ,     __a				:						List[Any]     ):
							a__      =torch.load(__a    ,     map_location='cpu'     )

							a__      =checkpoint['Downstream']

							a__      =WavaVecaConfig.from_pretrained(__a     )
							a__      =WavaVecaFeatureExtractor.from_pretrained(
							    __a    ,     return_attention_mask=__a    ,     do_normalize=__a     )

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

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

							hf_feature_extractor.save_pretrained(__a     )
							hf_model.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Optional[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:						Dict   				=       parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

