






'''simple docstring'''
import argparse
from pathlib import Path

import torch

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


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





def 	_a(					UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.load(SCREAMING_SNAKE_CASE_,		map_location='''cpu'''							)
      if "model" in sd.keys():
            SCREAMING_SNAKE_CASE__       :			Dict													=torch.load(SCREAMING_SNAKE_CASE_,		map_location='''cpu'''							)['''model''']

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

      SCREAMING_SNAKE_CASE__       :			List[Any]													={
          '''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:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=sd.pop(SCREAMING_SNAKE_CASE_							)

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

                  SCREAMING_SNAKE_CASE__       :			int													=key.replace('''.qkv_proj.''',		'''.q_proj.'''							)
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=key.replace('''.qkv_proj.''',		'''.k_proj.'''							)
                  SCREAMING_SNAKE_CASE__       :			Dict													=key.replace('''.qkv_proj.''',		'''.v_proj.'''							)

                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=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
                  SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.split(SCREAMING_SNAKE_CASE_,		depth // 3,		dim=0							)

                  SCREAMING_SNAKE_CASE__       :			List[Any]													=q
                  SCREAMING_SNAKE_CASE__       :			str													=k
                  SCREAMING_SNAKE_CASE__       :			List[str]													=v
                  del sd[key]

      return sd





@torch.no_grad()
def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Optional[int]=None							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=load_checkpoint(SCREAMING_SNAKE_CASE_							)

      if config is not None:
            SCREAMING_SNAKE_CASE__       :			Any													=OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
      else:
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=OPTConfig()

      SCREAMING_SNAKE_CASE__       :			Optional[int]													=OPTModel(SCREAMING_SNAKE_CASE_							).half().eval()
      model.load_state_dict(SCREAMING_SNAKE_CASE_							)

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


if __name__ == "__main__":
     a_					=	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.')
     a_					=	parser.parse_args()
     convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)







'''simple docstring'''
def 	_a(					UpperCamelCase__			:  Optional[Any]=2_8_1_2_3							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[Any]													=[1] * (limit + 1)

      for i in range(2,		int(limit**0.5							) + 1							):
            sum_divs[i * i] += i
            for k in range(i + 1,		limit // i + 1							):
                  sum_divs[k * i] += k + i

      SCREAMING_SNAKE_CASE__       :			List[str]													=set()
      SCREAMING_SNAKE_CASE__       :			str													=0

      for n in range(1,		limit + 1							):
            if sum_divs[n] > n:
                  abundants.add(UpperCamelCase__							)

            if not any((n - a in abundants) for a in abundants							):
                  res += n

      return res


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