






import argparse

import torch

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


logging.set_verbosity_info()
__lowerCamelCase :      str										=				logging.get_logger(__name__)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							Any					,  snake_case_ :							Optional[Any]       ):
	snake_case__			:				List[Any]       =  UniSpeechSatForSequenceClassification.from_pretrained(snake_case_					,  config=snake_case_       )
	snake_case__			:				int       =  downstream_dict["projector.weight"]
	snake_case__			:				int       =  downstream_dict["projector.bias"]
	snake_case__			:				List[Any]       =  downstream_dict["model.post_net.linear.weight"]
	snake_case__			:				Any       =  downstream_dict["model.post_net.linear.bias"]
	return model







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							str					,  snake_case_ :							str       ):
	snake_case__			:				Dict       =  UniSpeechSatForAudioFrameClassification.from_pretrained(snake_case_					,  config=snake_case_       )
	snake_case__			:				Tuple       =  downstream_dict["model.linear.weight"]
	snake_case__			:				Dict       =  downstream_dict["model.linear.bias"]
	return model







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

	snake_case__			:				str       =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
	snake_case__			:				Tuple       =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
	snake_case__			:				Dict       =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
	snake_case__			:				int       =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
	snake_case__			:				int       =  downstream_dict["objective.W"]
	return model







@torch.no_grad()
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Union[str, Any]					,  snake_case_ :							Dict					,  snake_case_ :							Tuple					,  snake_case_ :							int       ):
	snake_case__			:				List[Any]       =  torch.load(snake_case_					,  map_location="cpu"       )

	snake_case__			:				Any       =  checkpoint["Downstream"]

	snake_case__			:				str       =  UniSpeechSatConfig.from_pretrained(snake_case_       )
	snake_case__			:				Dict       =  WavaVecaFeatureExtractor.from_pretrained(
	    snake_case_					,  return_attention_mask=snake_case_					,  do_normalize=snake_case_       )

	snake_case__			:				str       =  hf_config.architectures[0]
	if arch.endswith("ForSequenceClassification"       ):
		snake_case__			:				Any       =  convert_classification(snake_case_					,  snake_case_					,  snake_case_       )
	elif arch.endswith("ForAudioFrameClassification"       ):
		snake_case__			:				int       =  convert_diarization(snake_case_					,  snake_case_					,  snake_case_       )
	elif arch.endswith("ForXVector"       ):
		snake_case__			:				Optional[int]       =  convert_xvector(snake_case_					,  snake_case_					,  snake_case_       )
	else:
		raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''       )

	if hf_config.use_weighted_layer_sum:
		snake_case__			:				List[str]       =  checkpoint["Featurizer"]["weights"]

	hf_feature_extractor.save_pretrained(snake_case_       )
	hf_model.save_pretrained(snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      Optional[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.""")
	__lowerCamelCase :      Union[str, Any]										=				parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











import sys

__lowerCamelCase :      List[str]										=				(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str = N       ):
	snake_case__			:				Any       =  -sys.maxsize - 1
	for i in range(len(snake_case_       ) - 12       ):
		snake_case__			:				Tuple       =  1
		for j in range(13       ):
			product *= int(n[i + j]       )
		if product > largest_product:
			snake_case__			:				Dict       =  product
	return largest_product


if __name__ == "__main__":
	print(f"{solution() = }")



