

import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase_							:					Any		 =   logging.get_logger(__name__)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int			,a__    :   Tuple       )					->				Optional[int]:
	__A :			int		    =			UniSpeechSatForSequenceClassification.from_pretrained(a__			,config=a__       )
	__A :			Dict		    =			downstream_dict["""projector.weight"""]
	__A :			int		    =			downstream_dict["""projector.bias"""]
	__A :			Any		    =			downstream_dict["""model.post_net.linear.weight"""]
	__A :			Any		    =			downstream_dict["""model.post_net.linear.bias"""]
	return model
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[int]			,a__    :   Optional[Any]			,a__    :   str       )					->				str:
	__A :			Dict		    =			UniSpeechSatForAudioFrameClassification.from_pretrained(a__			,config=a__       )
	__A :			List[str]		    =			downstream_dict["""model.linear.weight"""]
	__A :			int		    =			downstream_dict["""model.linear.bias"""]
	return model
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   str			,a__    :   Optional[Any]       )					->				str:
	__A :			Optional[Any]		    =			UniSpeechSatForXVector.from_pretrained(a__			,config=a__       )
	__A :			int		    =			downstream_dict["""connector.weight"""]
	__A :			Dict		    =			downstream_dict["""connector.bias"""]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
		__A :			str		    =			downstream_dict[
		    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
		]
		__A :			Tuple		    =			downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

	__A :			str		    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
	__A :			Optional[Any]		    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
	__A :			Union[str, Any]		    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
	__A :			Tuple		    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
	__A :			Union[str, Any]		    =			downstream_dict["""objective.W"""]
	return model
@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Dict			,a__    :   List[Any]			,a__    :   Tuple       )					->				List[str]:
	__A :			Dict		    =			torch.load(a__			,map_location="""cpu"""       )

	__A :			int		    =			checkpoint["""Downstream"""]

	__A :			Optional[int]		    =			UniSpeechSatConfig.from_pretrained(a__       )
	__A :			Optional[Any]		    =			WavaVecaFeatureExtractor.from_pretrained(
	    a__			,return_attention_mask=a__			,do_normalize=a__       )

	__A :			Union[str, Any]		    =			hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""       ):
		__A :			List[str]		    =			convert_classification(a__			,a__			,a__       )
	elif arch.endswith("""ForAudioFrameClassification"""       ):
		__A :			List[Any]		    =			convert_diarization(a__			,a__			,a__       )
	elif arch.endswith("""ForXVector"""       ):
		__A :			Tuple		    =			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 :			List[str]		    =			checkpoint["""Featurizer"""]["""weights"""]

	hf_feature_extractor.save_pretrained(a__       )
	hf_model.save_pretrained(a__       )


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








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_							:					int		 =   {
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''InformerForPrediction''',
			    '''InformerModel''',
			    '''InformerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_informer import (
						    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InformerForPrediction,
						    InformerModel,
						    InformerPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_							:					Optional[int]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





