


'''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_ :     Optional[int]								=      logging.get_logger(__name__)


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

 __A  :		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:
   __A  :		str       =			sd.pop(lowercase_			)

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

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

   __A  :		Optional[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  :		str       =			torch.split(lowercase_,				depth // 3,				dim=0			)

   __A  :		int       =			q
   __A  :		Optional[Any]       =			k
   __A  :		int       =			v
   del sd[key]

 return sd


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

 if config is not None:
  __A  :		Tuple       =			OPTConfig.from_pretrained(lowercase_			)
 else:
  __A  :		Union[str, Any]       =			OPTConfig()

 __A  :		List[Any]       =			OPTModel(lowercase_			).half().eval()
 model.load_state_dict(lowercase_			)

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


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










'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt


def 			_lowercase       (				UpperCamelCase__       :					int = 1000000,				UpperCamelCase__       :					int = 10			):
 __A  :		defaultdict       =			defaultdict(UpperCamelCase__			)

 for outer_width in range(3,				(t_limit // 4) + 2			):
  if outer_width * outer_width > t_limit:
   __A  :		Optional[int]       =			max(
       ceil(sqrt(outer_width * outer_width - t_limit			)			),				1			)
  else:
   __A  :		Optional[Any]       =			1

  hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

  for hole_width in range(UpperCamelCase__,				outer_width - 1,				2			):
   count[outer_width * outer_width - hole_width * hole_width] += 1

 return sum(1 for n in count.values() if 1 <= n <= 10			)


if __name__ == "__main__":
 print(f'''{solution() = }''')






