





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		UpperCamelCase__			(				pl.LightningModule       ):



     '''simple docstring'''

     def __init__(							self						:   Any						,lowerCamelCase__						:   List[str]	)					->		Optional[int]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						model
           SCREAMING_SNAKE_CASE		=						2
           SCREAMING_SNAKE_CASE		=						nn.Linear(self.model.config.hidden_size						,self.num_labels	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		int:




           '''simple docstring'''


           pass



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						LongformerModel.from_pretrained(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						LightningModel(_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=						torch.load(_SCREAMING_SNAKE_CASE			,						map_location=torch.device("""cpu"""     )     )
      lightning_model.load_state_dict(ckpt["""state_dict"""]     )

      # init longformer question answering model
      SCREAMING_SNAKE_CASE		=						LongformerForQuestionAnswering.from_pretrained(_SCREAMING_SNAKE_CASE     )

      # transfer weights
      longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()     )
      longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()     )
      longformer_for_qa.eval()

      # save model
      longformer_for_qa.save_pretrained(_SCREAMING_SNAKE_CASE     )

      print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""     )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--longformer_model""",
          default=None,
          type=str,
          required=True,
          help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
      )
      parser.add_argument(
          """--longformer_question_answering_ckpt_path""",
          default=None,
          type=str,
          required=True,
          help="""Path the official PyTorch Lightning Checkpoint.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
      convert_longformer_qa_checkpoint_to_pytorch(
          args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
      )









import json
import sys



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


      with open(_SCREAMING_SNAKE_CASE			,						encoding="""utf-8"""     ) as f:
            SCREAMING_SNAKE_CASE		=						json.load(_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=						["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

      for benchmark_name in sorted(_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						results[benchmark_name]

            SCREAMING_SNAKE_CASE		=						benchmark_name.split("""/"""     )[-1]
            output_md.append(F"""### Benchmark: {benchmark_file_name}"""     )

            SCREAMING_SNAKE_CASE		=						"""| metric |"""
            SCREAMING_SNAKE_CASE		=						"""|--------|"""
            SCREAMING_SNAKE_CASE		=						"""| new / old (diff) |"""
            for metric_name in sorted(_SCREAMING_SNAKE_CASE     ):
                  SCREAMING_SNAKE_CASE		=						benchmark_res[metric_name]
                  SCREAMING_SNAKE_CASE		=						metric_vals["""new"""]
                  SCREAMING_SNAKE_CASE		=						metric_vals.get("""old"""			,						_SCREAMING_SNAKE_CASE     )
                  SCREAMING_SNAKE_CASE		=						metric_vals.get("""diff"""			,						_SCREAMING_SNAKE_CASE     )

                  SCREAMING_SNAKE_CASE		=						F""" {new_val:f}""" if isinstance(_SCREAMING_SNAKE_CASE			,						(int, float)     ) else """None"""

                  if old_val is not None:
                        val_str += F""" / {old_val:f}""" if isinstance(_SCREAMING_SNAKE_CASE			,						(int, float)     ) else "None"
                  if dif_val is not None:
                        val_str += F""" ({dif_val:f})""" if isinstance(_SCREAMING_SNAKE_CASE			,						(int, float)     ) else "None"

                  title += " " + metric_name + " |"
                  lines += "---|"
                  value += val_str + " |"

            output_md += [title, lines, value, " "]

      output_md.append("""</details>"""     )

      with open(_SCREAMING_SNAKE_CASE			,						"""w"""			,						encoding="""utf-8"""     ) as f:
            f.writelines("""\n""".join(_SCREAMING_SNAKE_CASE     )     )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	sys.argv[1]
      SCREAMING_SNAKE_CASE_						       =	sys.argv[2]

      format_json_to_md(input_json_file, output_md_file)


