


'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()
__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)






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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       Tuple					    =  UniSpeechSatForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE						,      config=_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  downstream_dict['model.linear.weight']
					__a			:       str					    =  downstream_dict['model.linear.bias']
					return model






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

					__a			:       Optional[Any]					    =  downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					__a			:       List[Any]					    =  downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					__a			:       Optional[int]					    =  downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					__a			:       Union[str, Any]					    =  downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					__a			:       List[str]					    =  downstream_dict['objective.W']
					return model






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Tuple					    =  torch.load(_SCREAMING_SNAKE_CASE						,      map_location='cpu'   )

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

					__a			:       Any					    =  UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  WavaVecaFeatureExtractor.from_pretrained(
					    _SCREAMING_SNAKE_CASE						,      return_attention_mask=_SCREAMING_SNAKE_CASE						,      do_normalize=_SCREAMING_SNAKE_CASE   )

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

					hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE   )
					hf_model.save_pretrained(_SCREAMING_SNAKE_CASE   )


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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase          =  {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''InstructBlipQFormerModel''',
						    '''InstructBlipPreTrainedModel''',
						    '''InstructBlipForConditionalGeneration''',
						    '''InstructBlipVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




