






import math
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      UpperCamelCase					       =  [True] * n
      UpperCamelCase					       =  False
      UpperCamelCase					       =  False
      UpperCamelCase					       =  True

      for i in range(3       ,							int(n**0.5 + 1)       ,							2):
            UpperCamelCase					       =  i * 2
            while index < n:
                  UpperCamelCase					       =  False
                  UpperCamelCase					       =  index + i

      UpperCamelCase					       =  [2]

      for i in range(3       ,							_UpperCamelCase       ,							2):
            if is_prime[i]:
                  primes.append(_UpperCamelCase)

      return primes
def 		lowercase__  (				_UpperCamelCase = 99_99_66_66_33_33) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  math.floor(math.sqrt(_UpperCamelCase)) + 1_00
      UpperCamelCase					       =  prime_sieve(_UpperCamelCase)

      UpperCamelCase					       =  0
      UpperCamelCase					       =  0
      UpperCamelCase					       =  primes[prime_index]

      while (last_prime**2) <= limit:
            UpperCamelCase					       =  primes[prime_index + 1]

            UpperCamelCase					       =  last_prime**2
            UpperCamelCase					       =  next_prime**2

            # Get numbers divisible by lps(current)
            UpperCamelCase					       =  lower_bound + last_prime
            while upper_bound > current <= limit:
                  matches_sum += current
                  current += last_prime

            # Reset the upper_bound
            while (upper_bound - next_prime) > limit:
                  upper_bound -= next_prime

            # Add the numbers divisible by ups(current)
            UpperCamelCase					       =  upper_bound - next_prime
            while current > lower_bound:
                  matches_sum += current
                  current -= next_prime

            # Remove the numbers divisible by both ups and lps
            UpperCamelCase					       =  0
            while upper_bound > current <= limit:
                  if current <= lower_bound:
                        # Increment the current number
                        current += last_prime * next_prime
                        continue

                  if current > limit:
                        break

                  # Remove twice since it was added by both ups and lps
                  matches_sum -= current * 2

                  # Increment the current number
                  current += last_prime * next_prime

            # Setup for next pair
            UpperCamelCase					       =  next_prime
            prime_index += 1

      return matches_sum


if __name__ == "__main__":
       print(solution())











import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          'decoder.project_in_dim.weight': 'decoder.project_in.weight',
          'decoder.project_out_dim.weight': 'decoder.project_out.weight',
          'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
          'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  list(sd.keys())
      for key in keys:
            if ".qkv_proj." in key:
                  UpperCamelCase					       =  sd[key]
                  # We split QKV in separate Q,K,V

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

      # Check results
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  Any        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--fairseq_path''',
           type=str,
           help=(
               '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
               ''' https://huggingface.co/models?other=opt_metasq'''
           ),
       )
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



