






'''simple docstring'''

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def 							snake_case					(     a_							: Union[str, Any]	)       ->				Optional[int]:





      """simple docstring"""

      if "model" in orig_key:
            UpperCamelCase_				:   Optional[int]					    =							orig_key.replace("""model."""    ,   """"""	)
      if "norm1" in orig_key:
            UpperCamelCase_				:   List[str]					    =							orig_key.replace("""norm1"""    ,   """attention.output.LayerNorm"""	)
      if "norm2" in orig_key:
            UpperCamelCase_				:   Union[str, Any]					    =							orig_key.replace("""norm2"""    ,   """output.LayerNorm"""	)
      if "norm" in orig_key:
            UpperCamelCase_				:   int					    =							orig_key.replace("""norm"""    ,   """LayerNorm"""	)
      if "transformer" in orig_key:
            UpperCamelCase_				:   List[Any]					    =							orig_key.split("""."""	)[0].split("""_"""	)[-1]
            UpperCamelCase_				:   Tuple					    =							orig_key.replace(f"transformer_{layer_num}"    ,   f"encoder.layer.{layer_num}"	)
      if "mha.attn" in orig_key:
            UpperCamelCase_				:   str					    =							orig_key.replace("""mha.attn"""    ,   """attention.self"""	)
      if "mha" in orig_key:
            UpperCamelCase_				:   int					    =							orig_key.replace("""mha"""    ,   """attention"""	)
      if "W_q" in orig_key:
            UpperCamelCase_				:   Tuple					    =							orig_key.replace("""W_q"""    ,   """self.query"""	)
      if "W_k" in orig_key:
            UpperCamelCase_				:   Union[str, Any]					    =							orig_key.replace("""W_k"""    ,   """self.key"""	)
      if "W_v" in orig_key:
            UpperCamelCase_				:   Dict					    =							orig_key.replace("""W_v"""    ,   """self.value"""	)
      if "ff1" in orig_key:
            UpperCamelCase_				:   List[Any]					    =							orig_key.replace("""ff1"""    ,   """intermediate.dense"""	)
      if "ff2" in orig_key:
            UpperCamelCase_				:   Tuple					    =							orig_key.replace("""ff2"""    ,   """output.dense"""	)
      if "ff" in orig_key:
            UpperCamelCase_				:   List[str]					    =							orig_key.replace("""ff"""    ,   """output.dense"""	)
      if "mlm_class" in orig_key:
            UpperCamelCase_				:   int					    =							orig_key.replace("""mlm.mlm_class"""    ,   """cls.predictions.decoder"""	)
      if "mlm" in orig_key:
            UpperCamelCase_				:   Union[str, Any]					    =							orig_key.replace("""mlm"""    ,   """cls.predictions.transform"""	)
      if "cls" not in orig_key:
            UpperCamelCase_				:   Optional[Any]					    =							"""yoso.""" + orig_key

      return orig_key



def 							snake_case					(     a_							: List[str]    ,   a_							: Any	)       ->				List[Any]:





      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            UpperCamelCase_				:   Optional[Any]					    =							orig_state_dict.pop(UpperCamelCase__	)

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  UpperCamelCase_				:   Dict					    =							val

      UpperCamelCase_				:   Any					    =							orig_state_dict["""cls.predictions.decoder.bias"""]
      UpperCamelCase_				:   Optional[Any]					    =							torch.arange(UpperCamelCase__	).expand((1, -1)	) + 2

      return orig_state_dict



def 							snake_case					(     a_							: Tuple    ,   a_							: Optional[Any]    ,   a_							: Union[str, Any]	)       ->				Any:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							torch.load(UpperCamelCase__    ,   map_location="""cpu"""	)["""model_state_dict"""]
      UpperCamelCase_				:   Optional[Any]					    =							YosoConfig.from_json_file(UpperCamelCase__	)
      UpperCamelCase_				:   Tuple					    =							YosoForMaskedLM(UpperCamelCase__	)

      UpperCamelCase_				:   int					    =							convert_checkpoint_helper(config.max_position_embeddings    ,   UpperCamelCase__	)

      print(model.load_state_dict(UpperCamelCase__	)	)
      model.eval()
      model.save_pretrained(UpperCamelCase__	)

      print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"	)


if __name__ == "__main__":
    UpperCamelCase							=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The json file for YOSO model config.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    UpperCamelCase							=parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)














'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

UpperCamelCase							="https://api.github.com"

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase							=BASE_URL + "/user"

# https://github.com/settings/tokens
UpperCamelCase							=os.environ.get("USER_TOKEN", "")



def 							snake_case					(     a_							: str	)       ->				dict[Any, Any]:





      """simple docstring"""

      UpperCamelCase_				:   Tuple					    =							{
          """Authorization""": f"token {auth_token}",
          """Accept""": """application/vnd.github.v3+json""",
      }
      return requests.get(a_    ,   headers=a_	).json()


if __name__ == "__main__":  # pragma: no cover
      if USER_TOKEN:
            for key, value in fetch_github_info(USER_TOKEN).items():
                  print(f"{key}: {value}")
      else:
            raise ValueError("'USER_TOKEN' field cannot be empty.")






