



"""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     =			logging.get_logger(__name__)





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=torch.load(lowercase							,			map_location="""cpu""" )
   if "model" in sd.keys():
      SCREAMING_SNAKE_CASE_: Tuple     			=torch.load(lowercase							,			map_location="""cpu""" )["""model"""]

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

   SCREAMING_SNAKE_CASE_: 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:
         SCREAMING_SNAKE_CASE_: Dict     			=sd.pop(lowercase )

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

         SCREAMING_SNAKE_CASE_: Dict     			=key.replace(""".qkv_proj."""							,			""".q_proj.""" )
         SCREAMING_SNAKE_CASE_: List[str]     			=key.replace(""".qkv_proj."""							,			""".k_proj.""" )
         SCREAMING_SNAKE_CASE_: List[str]     			=key.replace(""".qkv_proj."""							,			""".v_proj.""" )

         SCREAMING_SNAKE_CASE_: List[str]     			=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
         SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=torch.split(lowercase							,			depth // 3							,			dim=0 )

         SCREAMING_SNAKE_CASE_: Tuple     			=q
         SCREAMING_SNAKE_CASE_: Optional[int]     			=k
         SCREAMING_SNAKE_CASE_: Dict     			=v
         del sd[key]

   return sd





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=None ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=load_checkpoint(lowercase )

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

   SCREAMING_SNAKE_CASE_: 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     =			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)








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   if upper_limit < 0:
      raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )

   SCREAMING_SNAKE_CASE_: Tuple     			=[0] * (upper_limit + 1)

   # Base case: C(0) = C(1) = 1
   SCREAMING_SNAKE_CASE_: Any     			=1
   if upper_limit > 0:
      SCREAMING_SNAKE_CASE_: List[str]     			=1

   # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
   for i in range(2							,			upper_limit + 1 ):
      for j in range(lowercase ):
         catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

   return catalan_list


if __name__ == "__main__":
   print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
   print("""\n*** Enter -1 at any time to quit ***""")
   print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
   try:
      while True:
         _UpperCAmelCase     =			int(input().strip())
         if N < 0:
            print("""\n********* Goodbye!! ************""")
            break
         else:
            print(f"""The Catalan numbers from 0 through {N} are:""")
            print(catalan_numbers(N))
            print("""Try another upper limit for the sequence: """, end="""""")
   except (NameError, ValueError):
      print("""\n********* Invalid input, goodbye! ************\n""")

   import doctest

   doctest.testmod()



