

"""simple docstring"""




import os

import pytest
from attr import dataclass


__UpperCAmelCase			=					'us-east-1'  # defaults region







@dataclass
class   __lowercase      :
  snake_case_												=							42
  snake_case_												=							"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
  snake_case_												=							{
      """task_name""": """mnli""",
      """per_device_train_batch_size""": 1_6,
      """per_device_eval_batch_size""": 1_6,
      """do_train""": True,
      """do_eval""": True,
      """do_predict""": True,
      """output_dir""": """/opt/ml/model""",
      """overwrite_output_dir""": True,
      """max_steps""": 5_0_0,
      """save_steps""": 5_5_0_0,
  }
  snake_case_												=							{**hyperparameters, """max_steps""": 1_0_0_0}

  @property
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   if self.framework == "pytorch":
    return [
        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
        {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
        {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
    ]
   else:
    return [
        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
        {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
        {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
    ]

  @property
  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   return f"{self.framework}-transfromers-test"

  @property
  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   return f"./tests/sagemaker/scripts/{self.framework}"




  @property
  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   if self.framework == "pytorch":
    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
   else:
    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="""class"""   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[Any]        =       SageMakerTestEnvironment(framework=request.cls.framework   )












import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # 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(lowerCamelCase )

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


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

