



"""simple docstring"""







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[int]	       =      logging.get_logger(__name__)



def 	a_						(      __a   ):
     A__      						= torch.load(__a		,				map_location='''cpu'''   )
     if "model" in sd.keys():
          A__      						= torch.load(__a		,				map_location='''cpu'''   )['''model''']

     # pop unnecessary weights
     A__      						= [
         '''decoder.version''',
         '''decoder.output_projection.weight''',
     ]
     for key in keys_to_delete:
          if key in sd:
               sd.pop(__a   )

     A__      						= {
         '''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__      						= sd.pop(__a   )

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

               A__      						= key.replace('''.qkv_proj.'''		,				'''.q_proj.'''   )
               A__      						= key.replace('''.qkv_proj.'''		,				'''.k_proj.'''   )
               A__      						= key.replace('''.qkv_proj.'''		,				'''.v_proj.'''   )

               A__      						= 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__      						= torch.split(__a		,				depth // 3		,				dim=0   )

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

     return sd



@torch.no_grad()
def 	a_						(      __a		,				__a		,				__a=None   ):
     A__      						= load_checkpoint(__a   )

     if config is not None:
          A__      						= OPTConfig.from_pretrained(__a   )
     else:
          A__      						= OPTConfig()

     A__      						= OPTModel(__a   ).half().eval()
     model.load_state_dict(__a   )

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


if __name__ == "__main__":
 __snake_case						:					List[str]	       =      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[int]	       =      parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




