



"""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:  int      				=						logging.get_logger(__name__)

def  _snake_case		(		UpperCamelCase		:						List[str]		):
 UpperCAmelCase      :   Any      =      torch.load(UpperCAmelCase_     ,						map_location="""cpu"""		)
 if "model" in sd.keys():
  UpperCAmelCase      :   Tuple      =      torch.load(UpperCAmelCase_     ,						map_location="""cpu"""		)['model']

 # pop unnecessary weights
 UpperCAmelCase      :   Union[str, Any]      =      [
     'decoder.version',
     'decoder.output_projection.weight',
 ]
 for key in keys_to_delete:
  if key in sd:
   sd.pop(UpperCAmelCase_		)

 UpperCAmelCase      :   Optional[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:
   UpperCAmelCase      :   Any      =      sd.pop(UpperCAmelCase_		)

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

   UpperCAmelCase      :   Union[str, Any]      =      key.replace(""".qkv_proj."""     ,						""".q_proj."""		)
   UpperCAmelCase      :   Dict      =      key.replace(""".qkv_proj."""     ,						""".k_proj."""		)
   UpperCAmelCase      :   Tuple      =      key.replace(""".qkv_proj."""     ,						""".v_proj."""		)

   UpperCAmelCase      :   Tuple      =      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      :   str      =      torch.split(UpperCAmelCase_     ,						depth // 3     ,						dim=0		)

   UpperCAmelCase      :   int      =      q
   UpperCAmelCase      :   Any      =      k
   UpperCAmelCase      :   List[Any]      =      v
   del sd[key]

 return sd

@torch.no_grad()
def  _snake_case		(		UpperCamelCase		:						Union[str, Any]     ,						UpperCamelCase		:						Dict     ,						UpperCamelCase		:						int=None		):
 UpperCAmelCase      :   Optional[Any]      =      load_checkpoint(UpperCAmelCase_		)

 if config is not None:
  UpperCAmelCase      :   Dict      =      OPTConfig.from_pretrained(UpperCAmelCase_		)
 else:
  UpperCAmelCase      :   List[str]      =      OPTConfig()

 UpperCAmelCase      :   str      =      OPTModel(UpperCAmelCase_		).half().eval()
 model.load_state_dict(UpperCAmelCase_		)

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


if __name__ == "__main__":
      A:  Optional[int]      				=						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:  List[Any]      				=						parser.parse_args()
      convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





'''simple docstring'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


