




import os
import string
import sys


lowercase__				:      Optional[int]	   = 1 << 8

lowercase__				:      Dict	   = {
    "tab": ord("\t"),
    "newline": ord("\r"),
    "esc": 27,
    "up": 65 + ARROW_KEY_FLAG,
    "down": 66 + ARROW_KEY_FLAG,
    "right": 67 + ARROW_KEY_FLAG,
    "left": 68 + ARROW_KEY_FLAG,
    "mod_int": 91,
    "undefined": sys.maxsize,
    "interrupt": 3,
    "insert": 50,
    "delete": 51,
    "pg_up": 53,
    "pg_down": 54,
}

lowercase__				:      Optional[int]	   = KEYMAP["up"]
lowercase__				:      int	   = KEYMAP["left"]

if sys.platform == "win32":
   lowercase__				:      str	   = []
   lowercase__				:      str	   = {
       B"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
       B"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
       B"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
       B"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
       B"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
       B"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
       B"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
       B"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
   }

for i in range(10):
   lowercase__				:      Optional[int]	   = ord(str(i))

def  SCREAMING_SNAKE_CASE							(       )      ->					str:




     if os.name == "nt":
          import msvcrt

          a     =				"mbcs"
          # Flush the keyboard buffer
          while msvcrt.kbhit():
               msvcrt.getch()
          if len(__UpperCamelCase) == 0:
               # Read the keystroke
               a     =				msvcrt.getch()

               # If it is a prefix char, get second part
               if ch in (b"\x00", b"\xe0"):
                    a     =				ch + msvcrt.getch()
                    # Translate actual Win chars to bullet char types
                    try:
                         a     =				chr(WIN_KEYMAP[cha])
                         WIN_CH_BUFFER.append(chr(KEYMAP["mod_int"]))
                         WIN_CH_BUFFER.append(__UpperCamelCase)
                         if ord(__UpperCamelCase) in (
                             KEYMAP["insert"] - 1 << 9,
                             KEYMAP["delete"] - 1 << 9,
                             KEYMAP["pg_up"] - 1 << 9,
                             KEYMAP["pg_down"] - 1 << 9,
                         ):
                              WIN_CH_BUFFER.append(chr(1_26))
                         a     =				chr(KEYMAP["esc"])
                    except KeyError:
                         a     =				cha[1]
               else:
                    a     =				ch.decode(__UpperCamelCase)
          else:
               a     =				WIN_CH_BUFFER.pop(0)
     elif os.name == "posix":
          import termios
          import tty

          a     =				sys.stdin.fileno()
          a     =				termios.tcgetattr(__UpperCamelCase)
          try:
               tty.setraw(__UpperCamelCase)
               a     =				sys.stdin.read(1)
          finally:
               termios.tcsetattr(__UpperCamelCase		, termios.TCSADRAIN		, __UpperCamelCase)
     return ch





def  SCREAMING_SNAKE_CASE							(       )      ->					Dict:
     a     =				get_raw_chars()
     if ord(__UpperCamelCase) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
          return char

     elif ord(__UpperCamelCase) == KEYMAP["esc"]:
          a     =				get_raw_chars()
          if ord(__UpperCamelCase) == KEYMAP["mod_int"]:
               a     =				get_raw_chars()
               if ord(__UpperCamelCase) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__UpperCamelCase) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
                    return chr(ord(__UpperCamelCase) + ARROW_KEY_FLAG)
               else:
                    return KEYMAP["undefined"]
          else:
               return get_raw_chars()

     else:
          if char in string.printable:
               return char
          else:
               return KEYMAP["undefined"]









import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class       a__	( pl.LightningModule				):







     def __init__(     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

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

     # init longformer question answering model
     a     =				LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase)

     # 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(__UpperCamelCase)

     print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''')


if __name__ == "__main__":
   lowercase__				:      Optional[Any]	   = 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."
   )
   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )

