





'''simple docstring'''







import argparse
from pathlib import Path

import torch

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


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




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




     __a       =   torch.load(__SCREAMING_SNAKE_CASE				,  map_location="""cpu"""		)
     if "model" in sd.keys():
          __a       =   torch.load(__SCREAMING_SNAKE_CASE				,  map_location="""cpu"""		)["""model"""]

     # pop unnecessary weights
     __a       =   [
         """decoder.version""",
         """decoder.output_projection.weight""",
     ]
     for key in keys_to_delete:
          if key in sd:
               sd.pop(__SCREAMING_SNAKE_CASE		)

     __a       =   {
         """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:
               __a       =   sd.pop(__SCREAMING_SNAKE_CASE		)

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

               __a       =   key.replace(""".qkv_proj."""				,  """.q_proj."""		)
               __a       =   key.replace(""".qkv_proj."""				,  """.k_proj."""		)
               __a       =   key.replace(""".qkv_proj."""				,  """.v_proj."""		)

               __a       =   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
               __a       =   torch.split(__SCREAMING_SNAKE_CASE				,  depth // 3				,  dim=0		)

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

     return sd




@torch.no_grad()
def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=None		)				->					str:
     """simple docstring"""




     __a       =   load_checkpoint(__SCREAMING_SNAKE_CASE		)

     if config is not None:
          __a       =   OPTConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
     else:
          __a       =   OPTConfig()

     __a       =   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__":
 SCREAMING_SNAKE_CASE_      =			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.')
 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					bool:
     """simple docstring"""




     __a       =   [int(__SCREAMING_SNAKE_CASE		) for i in ip_va_address.split("""."""		) if i.isdigit()]
     return len(__SCREAMING_SNAKE_CASE		) == 4 and all(0 <= int(__SCREAMING_SNAKE_CASE		) <= 254 for octet in octets		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			input().strip()
 SCREAMING_SNAKE_CASE_      =			'valid' if is_ip_va_address_valid(ip) else 'invalid'
 print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")






