
import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


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

       return orig_key


def     UpperCamelCase      (					__lowerCamelCase  :				Tuple       ,      __lowerCamelCase  :				List[Any]					):
       for key in orig_state_dict.copy().keys():
              snake_case :     List[Any]           =   orig_state_dict.pop(__lowerCamelCase					)

              if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
                     continue
              else:
                     snake_case :     Tuple           =   val

       snake_case :     List[Any]           =   orig_state_dict["cls.predictions.decoder.bias"]
       snake_case :     Optional[int]           =   (
           torch.arange(config.max_position_embeddings					).expand((1, -1)					) + 2
       )

       return orig_state_dict


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				Optional[int]					):
       snake_case :     Any           =   torch.load(__lowerCamelCase       ,      map_location="cpu"					)["model_state_dict"]
       snake_case :     List[Any]           =   NystromformerConfig.from_json_file(__lowerCamelCase					)
       snake_case :     int           =   NystromformerForMaskedLM(__lowerCamelCase					)

       snake_case :     List[str]           =   convert_checkpoint_helper(__lowerCamelCase       ,      __lowerCamelCase					)

       model.load_state_dict(__lowerCamelCase					)
       model.eval()
       model.save_pretrained(__lowerCamelCase					)

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


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







import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand


def     UpperCamelCase      (					__lowerCamelCase  :				int					):
       return EnvironmentCommand()






class 						UpperCAmelCase	(		A_				):



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      ArgumentParser				)	->			Any:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   parser.add_parser("env"					,		help="Print relevant system environment info."				)
          download_parser.set_defaults(func=snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     List[str]           =   {
              "`datasets` version": version,
              "Platform": platform.platform(),
              "Python version": platform.python_version(),
              "Huggingface_hub version": huggingface_hub.__version__,
              "PyArrow version": pyarrow.__version__,
              "Pandas version": pandas.__version__,
          }

          print("\nCopy-and-paste the text below in your GitHub issue.\n"				)
          print(self.format_dict(snake_case__				)				)

          return info



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Tuple				)	->			Any:


          '''simple docstring'''


          return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]				) + "\n"





