


'''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_ :     Tuple								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[Any]			):
 __A  :		List[Any]       =			torch.load(UpperCamelCase__,				map_location='cpu'			)
 if "model" in sd.keys():
  __A  :		int       =			torch.load(UpperCamelCase__,				map_location='cpu'			)['model']

 # pop unnecessary weights
 __A  :		List[Any]       =			[
     'decoder.version',
     'decoder.output_projection.weight',
 ]
 for key in keys_to_delete:
  if key in sd:
   sd.pop(UpperCamelCase__			)

 __A  :		str       =			{
     '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  :		Optional[int]       =			sd.pop(UpperCamelCase__			)

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

   __A  :		Tuple       =			key.replace('.qkv_proj.',				'.q_proj.'			)
   __A  :		str       =			key.replace('.qkv_proj.',				'.k_proj.'			)
   __A  :		Union[str, Any]       =			key.replace('.qkv_proj.',				'.v_proj.'			)

   __A  :		List[Any]       =			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  :		Tuple       =			torch.split(UpperCamelCase__,				depth // 3,				dim=0			)

   __A  :		Dict       =			q
   __A  :		str       =			k
   __A  :		Dict       =			v
   del sd[key]

 return sd


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Optional[Any]=None			):
 __A  :		List[str]       =			load_checkpoint(UpperCamelCase__			)

 if config is not None:
  __A  :		List[str]       =			OPTConfig.from_pretrained(UpperCamelCase__			)
 else:
  __A  :		Optional[int]       =			OPTConfig()

 __A  :		Optional[Any]       =			OPTModel(UpperCamelCase__			).half().eval()
 model.load_state_dict(UpperCamelCase__			)

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


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      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'''
from __future__ import annotations


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 __A  :		Dict       =			[]
 __A     ,__A  :		Dict       =			input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0			)			)
 __A  :		Optional[Any]       =			result + left + right
 return input_list


def 			_lowercase       (				UpperCamelCase__       :					list			):
 if len(UpperCamelCase__			) <= 1:
  return input_list
 __A  :		Any       =			list(UpperCamelCase__			)

 # iteration for two-way merging
 __A  :		Dict       =			2
 while p <= len(UpperCamelCase__			):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0,				len(UpperCamelCase__			),				UpperCamelCase__			):
   __A  :		Union[str, Any]       =			i
   __A  :		Tuple       =			i + p - 1
   __A  :		Union[str, Any]       =			(low + high + 1) // 2
   __A  :		Tuple       =			merge(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  # final merge of last two parts
  if p * 2 >= len(UpperCamelCase__			):
   __A  :		Optional[int]       =			i
   __A  :		str       =			merge(UpperCamelCase__,				0,				UpperCamelCase__,				len(UpperCamelCase__			) - 1			)
   break
  p *= 2

 return input_list


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      input('Enter numbers separated by a comma:\n').strip()
 if user_input == "":
  UpperCAmelCase_ :     Union[str, Any]								=      []
 else:
  UpperCAmelCase_ :     str								=      [int(item.strip()) for item in user_input.split(',')]
 print(iter_merge_sort(unsorted))






