
import argparse
from pathlib import Path

import torch

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


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







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[str]:
       lowercase							:					str	     =				torch.load(_UpperCamelCase ,     map_location="""cpu"""    )
       if "model" in sd.keys():
              lowercase							:					Optional[Any]	     =				torch.load(_UpperCamelCase ,     map_location="""cpu"""    )["""model"""]

       # pop unnecessary weights
       lowercase							:					Optional[Any]	     =				[
           """decoder.version""",
           """decoder.output_projection.weight""",
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(_UpperCamelCase    )

       lowercase							:					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:
                     lowercase							:					List[Any]	     =				sd.pop(_UpperCamelCase    )

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

                     lowercase							:					int	     =				key.replace(""".qkv_proj.""" ,     """.q_proj."""    )
                     lowercase							:					Tuple	     =				key.replace(""".qkv_proj.""" ,     """.k_proj."""    )
                     lowercase							:					Any	     =				key.replace(""".qkv_proj.""" ,     """.v_proj."""    )

                     lowercase							:					Dict	     =				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
                     lowercase     ,      lowercase     ,      lowercase							:					Dict	     =				torch.split(_UpperCamelCase ,     depth // 3 ,     dim=0    )

                     lowercase							:					int	     =				q
                     lowercase							:					Any	     =				k
                     lowercase							:					Dict	     =				v
                     del sd[key]

       return sd







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=None    )			->   Dict:
       lowercase							:					List[Any]	     =				load_checkpoint(_UpperCamelCase    )

       if config is not None:
              lowercase							:					List[Any]	     =				OPTConfig.from_pretrained(_UpperCamelCase    )
       else:
              lowercase							:					List[Any]	     =				OPTConfig()

       lowercase							:					int	     =				OPTModel(_UpperCamelCase    ).half().eval()
       model.load_state_dict(_UpperCamelCase    )

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


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


from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat







def 						_snake_case(  )			->   tuple[list[int], int]:
       lowercase							:					List[Any]	     =				[randint(-1_000 ,     1_000    ) for i in range(10    )]
       lowercase							:					Tuple	     =				randint(-5_000 ,     5_000    )
       return (arr, r)


lowercase  : List[Any]						   =							make_dataset()







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, ...]:
       for triplet in permutations(SCREAMING_SNAKE_CASE__ ,     3    ):
              if sum(SCREAMING_SNAKE_CASE__    ) == target:
                     return tuple(sorted(SCREAMING_SNAKE_CASE__    )    )
       return (0, 0, 0)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, int, int]:
       arr.sort()
       lowercase							:					Optional[int]	     =				len(SCREAMING_SNAKE_CASE__    )
       for i in range(n - 1    ):
              lowercase     ,      lowercase							:					Optional[Any]	     =				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)







def 						_snake_case(  )			->   tuple[float, float]:
       lowercase							:					Dict	     =				"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
       lowercase							:					Tuple	     =				"""
triplet_sum1(*dataset)
"""
       lowercase							:					int	     =				"""
triplet_sum2(*dataset)
"""
       lowercase							:					str	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       lowercase							:					Dict	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       return (min(SCREAMING_SNAKE_CASE__    ), min(SCREAMING_SNAKE_CASE__    ))


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  lowercase  : Union[str, Any]						   =							solution_times()
  print(F'''The time for naive implementation is {times[0]}.''')
  print(F'''The time for optimized implementation is {times[1]}.''')
