



import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A__       =			logging.get_logger(__name__)

A__       =			["""UtteranceLevel"""]


@torch.no_grad()
def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       torch.load(snake_case      ,		map_location="""cpu"""     )
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(F'The supported s3prl models are {SUPPORTED_MODELS}'     )

					_lowerCAmelCase         =       checkpoint["""Downstream"""]

					_lowerCAmelCase         =       HubertConfig.from_pretrained(snake_case     )
					_lowerCAmelCase         =       HubertForSequenceClassification.from_pretrained(snake_case      ,		config=snake_case     )
					_lowerCAmelCase         =       WavaVecaFeatureExtractor.from_pretrained(
					    snake_case      ,		return_attention_mask=snake_case      ,		do_normalize=snake_case     )

					if hf_congfig.use_weighted_layer_sum:
										_lowerCAmelCase         =       checkpoint["""Featurizer"""]["""weights"""]

					_lowerCAmelCase         =       downstream_dict["""projector.weight"""]
					_lowerCAmelCase         =       downstream_dict["""projector.bias"""]
					_lowerCAmelCase         =       downstream_dict["""model.post_net.linear.weight"""]
					_lowerCAmelCase         =       downstream_dict["""model.post_net.linear.bias"""]

					hf_feature_extractor.save_pretrained(snake_case     )
					hf_model.save_pretrained(snake_case     )


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






"""simple docstring"""




import sys

sys.set_int_max_str_digits(0)  # type: ignore
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					if not f:
										return f

					# mark first 9 numbers
					A     =   int(str(lowercase__ )[:9] )

					for _ in range(9 ):
										A     =   1
										A     =   number // 10

					# check first 9 numbers for pandigitality

					for x in range(9 ):
										if not check_front[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers

					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers mod 1e9

					# mod m=1e9, done for fast optimisation
					A     =   [0] * 1_000_000
					A     =   1_000_000_000

					for x in range(1_000_000 ):
										A     =   (aa + ba) % m
										A     =   ba % m
										A     =   ca % m
										if checka(lowercase__ ):
															A     =   1

					for x in range(1_000_000 ):
										A     =   a + b
										A     =   b
										A     =   c
										# perform check only if in tocheck
										if tocheck[x + 3] and check(lowercase__ ):
															return x + 3  # first 2 already done
					return -1


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





