



'''simple docstring'''




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

UpperCamelCase__	:     List[Any]    =			['''UtteranceLevel''']


@torch.no_grad()
def     lowerCAmelCase_       (     _lowerCamelCase:       int	,				_lowerCamelCase:       Optional[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       Optional[Any] ):
		__SCREAMING_SNAKE_CASE	:		Any   	=		torch.load(_lowerCamelCase	,				map_location="""cpu""" )
		if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
				raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}" )

		__SCREAMING_SNAKE_CASE	:		Any   	=		checkpoint["""Downstream"""]

		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		HubertConfig.from_pretrained(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		HubertForSequenceClassification.from_pretrained(_lowerCamelCase	,				config=_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		WavaVecaFeatureExtractor.from_pretrained(
		    _lowerCamelCase	,				return_attention_mask=_lowerCamelCase	,				do_normalize=_lowerCamelCase )

		if hf_congfig.use_weighted_layer_sum:
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		checkpoint["""Featurizer"""]["""weights"""]

		__SCREAMING_SNAKE_CASE	:		Dict   	=		downstream_dict["""projector.weight"""]
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		downstream_dict["""projector.bias"""]
		__SCREAMING_SNAKE_CASE	:		str   	=		downstream_dict["""model.post_net.linear.weight"""]
		__SCREAMING_SNAKE_CASE	:		int   	=		downstream_dict["""model.post_net.linear.bias"""]

		hf_feature_extractor.save_pretrained(_lowerCamelCase )
		hf_model.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
						UpperCamelCase__	:     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.''')
						UpperCamelCase__	:     List[Any]    =			parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




'''simple docstring'''




UpperCamelCase__	:     List[Any]    =			10_00


def     lowerCAmelCase_       (     _lowerCamelCase:       float ):
		return x * x


def     lowerCAmelCase_       (     _lowerCamelCase:       Any	,				_lowerCamelCase:       float	,				_lowerCamelCase:       float	,				_lowerCamelCase:       int = 4 ):
		assert callable(
		    _lowerCamelCase ), F"the function(object) passed should be callable your input : {function}"
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"a should be float or integer your input : {a}"
		assert isinstance(function(_lowerCamelCase )	,				(float, int) ), (
		    "the function should return integer or float return type of your function, "
		    F"{type(_lowerCamelCase )}"
		)
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"b should be float or integer your input : {b}"
		assert (
		    isinstance(_lowerCamelCase	,				_lowerCamelCase ) and precision > 0
		), F"precision should be positive integer your input : {precision}"

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(b - a) / N_STEPS
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		function(_lowerCamelCase ) + function(_lowerCamelCase )

		for i in range(1	,				_lowerCamelCase ):
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		a + h * i
				result += function(_lowerCamelCase ) * (4 if i % 2 else 2)

		result *= h / 3
		return round(_lowerCamelCase	,				_lowerCamelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()