


import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__UpperCamelCase    		=       logging.get_logger(__name__)
def        UpperCamelCase_(				_A   :int  )->		Dict:
   UpperCamelCase__					=       torch.load(_A				,     map_location="cpu"  )
   if "model" in sd.keys():
      UpperCamelCase__					=       torch.load(_A				,     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(_A  )

   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(_A  )

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

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

   return sd
@torch.no_grad()
def        UpperCamelCase_(				_A   :Union[str, Any]				,     _A   :Dict				,     _A   :List[Any]=None  )->		Dict:
   UpperCamelCase__					=       load_checkpoint(_A  )

   if config is not None:
      UpperCamelCase__					=       OPTConfig.from_pretrained(_A  )
   else:
      UpperCamelCase__					=       OPTConfig()

   UpperCamelCase__					=       OPTModel(_A  ).half().eval()
   model.load_state_dict(_A  )

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


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






from typing import Any







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       data
						UpperCamelCase__					=       None







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.head
						while temp is not None:
									print(temp.data      ,						end=" "     )
									UpperCamelCase__					=       temp.next
						print()




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       Node(snake_case     )
						UpperCamelCase__					=       self.head
						UpperCamelCase__					=       new_node







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						if node_data_a == node_data_a:
									return
						else:
									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									if node_a is None or node_a is None:
												return

									UpperCamelCase__,      UpperCamelCase__					=       node_a.data, node_a.data


if __name__ == "__main__":
						__UpperCamelCase    		=       LinkedList()
						for i in range(5, 0, -1):
												ll.push(i)

						ll.print_list()

						ll.swap_nodes(1, 4)
						print('After swapping')
						ll.print_list()


