


from manim import *

class 					__magic_name__		(   a__):


     def    UpperCAmelCase__		(					self							:   Union[str, Any]	)     ->					Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__    :			List[Any]      				=      Rectangle(height=0.5							,		width=0.5	)
      UpperCamelCase__    :			Optional[int]      				=      Rectangle(height=0.46							,		width=0.46	).set_stroke(width=0	)

      UpperCamelCase__    :			Any      				=      [mem.copy() for i in range(6	)]
      UpperCamelCase__    :			Any      				=      [mem.copy() for i in range(6	)]
      UpperCamelCase__    :			Optional[int]      				=      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0	)
      UpperCamelCase__    :			Union[str, Any]      				=      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0	)
      UpperCamelCase__    :			str      				=      VGroup(__lowerCAmelCase							,		__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0	)
      UpperCamelCase__    :			str      				=      Text('''CPU'''							,		font_size=24	)
      UpperCamelCase__    :			Tuple      				=      Group(__lowerCAmelCase							,		__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0.5							,		aligned_edge=__lowerCAmelCase	)
      cpu.move_to([-2.5, -0.5, 0]	)
      self.add(__lowerCAmelCase	)

      UpperCamelCase__    :			Dict      				=      [mem.copy() for i in range(4	)]
      UpperCamelCase__    :			int      				=      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0	)
      UpperCamelCase__    :			Tuple      				=      Text('''GPU'''							,		font_size=24	)
      UpperCamelCase__    :			Optional[int]      				=      Group(__lowerCAmelCase							,		__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0.5							,		aligned_edge=__lowerCAmelCase	)
      gpu.move_to([-1, -1, 0]	)
      self.add(__lowerCAmelCase	)

      UpperCamelCase__    :			Union[str, Any]      				=      [mem.copy() for i in range(6	)]
      UpperCamelCase__    :			Union[str, Any]      				=      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0	)

      UpperCamelCase__    :			Tuple      				=      Text('''Model'''							,		font_size=24	)
      UpperCamelCase__    :			Dict      				=      Group(__lowerCAmelCase							,		__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0.5							,		aligned_edge=__lowerCAmelCase	)
      model.move_to([3, -1.0, 0]	)
      self.add(__lowerCAmelCase	)

      UpperCamelCase__    :			Dict      				=      []
      for i, rect in enumerate(__lowerCAmelCase	):
       rect.set_stroke(__lowerCAmelCase	)
       # target = fill.copy().set_fill(YELLOW, opacity=0.7)
       # target.move_to(rect)
       # self.add(target)

       UpperCamelCase__    :			Union[str, Any]      				=      Rectangle(height=0.46 / 4							,		width=0.46 / 3	).set_stroke(width=0.0	).set_fill(__lowerCAmelCase							,		opacity=0.7	)

       if i == 0:
        cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT	)							,		buff=0.02							,		direction=__lowerCAmelCase	)
        cpu_target.set_x(cpu_target.get_x() + 0.1	)
       elif i == 3:
        cpu_target.next_to(cpu_targs[0]							,		direction=__lowerCAmelCase							,		buff=0.0	)
       else:
        cpu_target.next_to(cpu_targs[i - 1]							,		direction=__lowerCAmelCase							,		buff=0.0	)
       self.add(__lowerCAmelCase	)
       cpu_targs.append(__lowerCAmelCase	)

      UpperCamelCase__    :			int      				=      [mem.copy() for i in range(6	)]
      UpperCamelCase__    :			int      				=      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase							,		buff=0	)

      UpperCamelCase__    :			Optional[int]      				=      Text('''Loaded Checkpoint'''							,		font_size=24	)
      UpperCamelCase__    :			Dict      				=      Group(__lowerCAmelCase							,		__lowerCAmelCase	).arrange(__lowerCAmelCase							,		aligned_edge=__lowerCAmelCase							,		buff=0.4	)
      checkpoint.move_to([3, 0.5, 0]	)

      UpperCamelCase__    :			Optional[Any]      				=      Square(side_length=2.2	)
      key.move_to([-5, 2, 0]	)

      UpperCamelCase__    :			List[str]      				=      MarkupText(
          F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"							,		font_size=18							,		)

      key_text.move_to([-5, 2.4, 0]	)

      self.add(__lowerCAmelCase							,		__lowerCAmelCase	)

      UpperCamelCase__    :			List[Any]      				=      MarkupText(
          F"<span fgcolor='{BLUE}'>●</span> Checkpoint"							,		font_size=18							,		)

      blue_text.next_to(__lowerCAmelCase							,		DOWN * 2.4							,		aligned_edge=key_text.get_left()	)

      UpperCamelCase__    :			int      				=      MarkupText(
          F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."							,		font_size=24							,		)
      step_a.move_to([2, 2, 0]	)
      self.play(Write(__lowerCAmelCase	)							,		Write(__lowerCAmelCase	)	)

      self.play(Write(__lowerCAmelCase							,		run_time=1	)							,		Create(__lowerCAmelCase							,		run_time=1	)	)

      UpperCamelCase__    :			int      				=      []
      UpperCamelCase__    :			Optional[int]      				=      []
      for i, rect in enumerate(__lowerCAmelCase	):
       UpperCamelCase__    :			int      				=      fill.copy().set_fill(__lowerCAmelCase							,		opacity=0.7	)
       target.move_to(__lowerCAmelCase	)
       first_animations.append(GrowFromCenter(__lowerCAmelCase							,		run_time=1	)	)

       UpperCamelCase__    :			List[str]      				=      target.copy()
       cpu_target.generate_target()
       if i < 5:
        cpu_target.target.move_to(cpu_left_col_base[i + 1]	)
       else:
        cpu_target.target.move_to(cpu_right_col_base[i - 5]	)
       second_animations.append(MoveToTarget(__lowerCAmelCase							,		run_time=1.5	)	)

      self.play(*__lowerCAmelCase	)
      self.play(*__lowerCAmelCase	)
      self.wait()



'''simple docstring'''

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	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        lowercase__				:    List[str]  =					model
        lowercase__				:    Dict  =					2
        lowercase__				:    Any  =					nn.Linear(self.model.config.hidden_size	,   self.num_labels )


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

 # init longformer question answering model
 lowercase__				:    Optional[int]  =					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__":
  __a: List[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."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

