
"""simple docstring"""

import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
UpperCamelCase     :    Dict       =			logging.get_logger(__name__)






def 							A						(    snake_case				:List[Any]		)				->     Tuple:
 __UpperCamelCase      =	torch.load(__SCREAMING_SNAKE_CASE    , map_location='cpu'		)
 if "model" in sd.keys():
  __UpperCamelCase      =	torch.load(__SCREAMING_SNAKE_CASE    , 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(__SCREAMING_SNAKE_CASE		)

 __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(__SCREAMING_SNAKE_CASE		)

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

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

 return sd






@torch.no_grad()
def 							A						(    snake_case				:Tuple    , snake_case				:List[Any]    , snake_case				:List[Any]=None		)				->     Union[str, Any]:
 __UpperCamelCase      =	load_checkpoint(__SCREAMING_SNAKE_CASE		)

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

 __UpperCamelCase      =	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__":
      UpperCamelCase     :    Optional[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.")
      UpperCamelCase     :    int       =			parser.parse_args()
      convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)







"""simple docstring"""

def 							A						(    snake_case				:list[list[int]]    , snake_case				:int    , snake_case				:int    , snake_case				:list[int]		)				->     bool:

 # 1. Validate that path exists between current and next vertices
 if graph[path[curr_ind - 1]][next_ver] == 0:
  return False

 # 2. Validate that next vertex is not already in path
 return not any(vertex == next_ver for vertex in path		)






def 							A						(    snake_case				:list[list[int]]    , snake_case				:list[int]    , snake_case				:int		)				->     bool:

 # Base Case
 if curr_ind == len(snake_case		):
  # return whether path exists between current and starting vertices
  return graph[path[curr_ind - 1]][path[0]] == 1

 # Recursive Step
 for next_ver in range(0    , len(snake_case		)		):
  if valid_connection(snake_case    , snake_case    , snake_case    , snake_case		):
   # Insert current vertex  into path as next transition
   __UpperCamelCase      =	next_ver
   # Validate created path
   if util_hamilton_cycle(snake_case    , snake_case    , curr_ind + 1		):
    return True
   # Backtrack
   __UpperCamelCase      =	-1
 return False






def 							A						(    snake_case				:list[list[int]]    , snake_case				:int = 0		)				->     list[int]:
 __UpperCamelCase      =	[-1] * (len(snake_case		) + 1)
 # initialize start and end of path with starting index
 __UpperCamelCase      =	__UpperCamelCase      =	start_index
 # evaluate and if we find answer return path either return empty array
 return path if util_hamilton_cycle(snake_case    , snake_case    , 1		) else []



