






import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
snake_case__      :	Optional[Any]       	=      logging.get_logger(__name__)






def     _snake_case    (__lowercase):
   UpperCamelCase_									=     torch.load(__lowercase    ,      map_location='cpu')
   if "model" in sd.keys():
      UpperCamelCase_									=     torch.load(__lowercase    ,      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(__lowercase)

   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(__lowercase)

   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(__lowercase    ,      depth // 3    ,      dim=0)

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

   return sd






@torch.no_grad()
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase=None):
   UpperCamelCase_									=     load_checkpoint(__lowercase)

   if config is not None:
      UpperCamelCase_									=     OPTConfig.from_pretrained(__lowercase)
   else:
      UpperCamelCase_									=     OPTConfig()

   UpperCamelCase_									=     OPTModel(__lowercase).half().eval()
   model.load_state_dict(__lowercase)

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


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













snake_case__      :	List[Any]       	=      """Tobias Carryer"""

from time import time


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=int(time() ) )			-> Tuple:  # noqa: B008
     UpperCamelCase_									=     multiplier
     UpperCamelCase_									=     increment
     UpperCamelCase_									=     modulo
     UpperCamelCase_									=     seed




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     (self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__      :	List[Any]       	=      LinearCongruentialGenerator(1_6_6_4_5_2_5, 1_0_1_3_9_0_4_2_2_3, 2 << 3_1)
 while True:
  print(lcg.next_number())





