





import math
from collections.abc import Generator


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      Generator           =						(i for i in range(1 ,			(max_n + 1) ))
	for i in (n for n in numbers if n > 1):
		for j in range(2 ,			__a ):
			if (i % j) == 0:
				break
		else:
			yield i


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      Generator           =						(i for i in range(1 ,			(max_n + 1) ))
	for i in (n for n in numbers if n > 1):
		# only need to check for factors up to sqrt(i)
		snake_case_		:      Union[str, Any]           =						int(math.sqrt(__a ) ) + 1
		for j in range(2 ,			__a ):
			if (i % j) == 0:
				break
		else:
			yield i


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      Generator           =						(i for i in range(1 ,			(max_n + 1) ,			2 ))
	# It's useless to test even numbers as they will not be prime
	if max_n > 2:
		yield 2  # Because 2 will not be tested, it's necessary to yield it now
	for i in (n for n in numbers if n > 1):
		snake_case_		:      int           =						int(math.sqrt(__a ) ) + 1
		for j in range(3 ,			__a ,			2 ):
			# As we removed the even numbers, we don't need them now
			if (i % j) == 0:
				break
		else:
			yield i


def 				SCREAMING_SNAKE_CASE__    (     ):
	from timeit import timeit

	snake_case_		:      str           =						'from __main__ import slow_primes, primes, fast_primes'
	print(timeit('slow_primes(1_000_000_000_000)' ,			setup=__a ,			number=1_00_00_00 ) )
	print(timeit('primes(1_000_000_000_000)' ,			setup=__a ,			number=1_00_00_00 ) )
	print(timeit('fast_primes(1_000_000_000_000)' ,			setup=__a ,			number=1_00_00_00 ) )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		int(input("""Calculate primes up to:\n>> """).strip())
						for ret in primes(number):
												print(ret)
						benchmark()


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  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}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

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

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

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


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

