


"""simple docstring"""







import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowerCAmelCase__      				=   logging.get_logger(__name__)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location="cpu"				)
  if "model" in sd.keys():
    lowerCAmelCase     :     Union[str, Any]								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location="cpu"				)["model"]

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

  lowerCAmelCase     :     List[str]								=							{
      "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:
      lowerCAmelCase     :     Any								=							sd.pop(SCREAMING_SNAKE_CASE				)

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

      lowerCAmelCase     :     Optional[Any]								=							key.replace(".qkv_proj."				,      ".q_proj."				)
      lowerCAmelCase     :     str								=							key.replace(".qkv_proj."				,      ".k_proj."				)
      lowerCAmelCase     :     Tuple								=							key.replace(".qkv_proj."				,      ".v_proj."				)

      lowerCAmelCase     :     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
      lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Optional[Any]								=							torch.split(SCREAMING_SNAKE_CASE				,      depth // 3				,      dim=0				)

      lowerCAmelCase     :     List[Any]								=							q
      lowerCAmelCase     :     str								=							k
      lowerCAmelCase     :     Any								=							v
      del sd[key]

  return sd
@torch.no_grad()
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    Tuple=None				):


  '''simple docstring'''







  lowerCAmelCase     :     Any								=							load_checkpoint(SCREAMING_SNAKE_CASE				)

  if config is not None:
    lowerCAmelCase     :     Optional[Any]								=							OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
  else:
    lowerCAmelCase     :     Optional[int]								=							OPTConfig()

  lowerCAmelCase     :     Any								=							OPTModel(SCREAMING_SNAKE_CASE				).half().eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE				)

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


if __name__ == "__main__":
     lowerCAmelCase__      				=   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.''')
     lowerCAmelCase__      				=   parser.parse_args()
     convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










"""simple docstring"""






from __future__ import annotations

_lowercase : Dict      						=						1.6_021E-19  # units = C






def      snake_case__   (		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		):
			"""simple docstring"""
			if (conductivity, electron_conc, mobility).count(0	) != 1:
						raise ValueError('''You cannot supply more or less than 2 values'''	)
			elif conductivity < 0:
						raise ValueError('''Conductivity cannot be negative'''	)
			elif electron_conc < 0:
						raise ValueError('''Electron concentration cannot be negative'''	)
			elif mobility < 0:
						raise ValueError('''mobility cannot be negative'''	)
			elif conductivity == 0:
						return (
						    "conductivity",
						    mobility * electron_conc * ELECTRON_CHARGE,
						)
			elif electron_conc == 0:
						return (
						    "electron_conc",
						    conductivity / (mobility * ELECTRON_CHARGE),
						)
			else:
						return (
						    "mobility",
						    conductivity / (electron_conc * ELECTRON_CHARGE),
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




