



import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:Any 			=		logging.get_logger(__name__)




def       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase ,	_lowerCAmelCase						)			->    Optional[int]:



    """simple docstring"""






    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       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase ,	_lowerCAmelCase						)			->    str:



    """simple docstring"""






    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       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase ,	_lowerCAmelCase						)			->    str:



    """simple docstring"""






    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       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase ,	_lowerCAmelCase ,	_lowerCAmelCase						)			->    List[str]:



    """simple docstring"""






    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__":
     SCREAMING_SNAKE_CASE_: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.""")
     SCREAMING_SNAKE_CASE_:Dict 			=		parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





