



'''simple docstring'''


import argparse
from pathlib import Path

import torch

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


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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=torch.load(lowercase__       ,	map_location="cpu"       )
     if "model" in sd.keys():
          a_       		=torch.load(lowercase__       ,	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(lowercase__       )

     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(lowercase__       )

     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_		,						a_		,						a_       		=torch.split(lowercase__       ,	depth // 3       ,	dim=0       )

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

     return sd






@torch.no_grad()
def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__=None       ):


     '''simple docstring'''







     a_       		=load_checkpoint(lowercase__       )

     if config is not None:
          a_       		=OPTConfig.from_pretrained(lowercase__       )
     else:
          a_       		=OPTConfig()

     a_       		=OPTModel(lowercase__       ).half().eval()
     model.load_state_dict(lowercase__       )

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


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







'''simple docstring'''


from __future__ import annotations

lowercase							       =			[]






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for i in range(len(lowercase__       )       ):
          if board[row][i] == 1:
               return False
     for i in range(len(lowercase__       )       ):
          if board[i][column] == 1:
               return False
     for i, j in zip(range(lowercase__       ,	-1       ,	-1       )       ,	range(lowercase__       ,	-1       ,	-1       )       ):
          if board[i][j] == 1:
               return False
     for i, j in zip(range(lowercase__       ,	-1       ,	-1       )       ,	range(lowercase__       ,	len(lowercase__       )       )       ):
          if board[i][j] == 1:
               return False
     return True






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if row >= len(lowercase__       ):
          solution.append(lowercase__       )
          printboard(lowercase__       )
          print()
          return True
     for i in range(len(lowercase__       )       ):
          if is_safe(lowercase__       ,	lowercase__       ,	lowercase__       ):
               a_       		=1
               solve(lowercase__       ,	row + 1       )
               a_       		=0
     return False






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     for i in range(len(lowercase__       )       ):
          for j in range(len(lowercase__       )       ):
               if board[i][j] == 1:
                    print("Q"       ,	end=" "       )
               else:
                    print("."       ,	end=" "       )
          print()


# n=int(input("The no. of queens"))
lowercase							       =			8
lowercase							       =			[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('''The total no. of solutions are :''', len(solution))


