





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
  from sagemaker import Session, TrainingJobAnalytics
  from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""		,"""False"""	)	) is not True		,reason="""Skipping test because should only be run when releasing minor transformers version"""		,)
@pytest.mark.usefixtures("""sm_env"""	)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]	)
class 	__lowerCAmelCase      (       unittest.TestCase	):





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      if self.framework == "pytorch":
         subprocess.run(
             F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()       ,      encoding="utf-8"       ,      check=snake_case       ,      )
      assert hasattr(self       ,      "env"   )





   def 						_snake_case			(			self       ,      snake_case=1   )					->	Optional[int]:
      """simple docstring"""





      return HuggingFace(
          entry_point=self.script       ,      source_dir=self.env.test_path       ,      role=self.env.role       ,      image_uri=self.env.image_uri       ,      base_job_name=F"""{self.env.base_job_name}-single"""       ,      instance_count=snake_case       ,      instance_type=self.instance_type       ,      debugger_hook_config=snake_case       ,      hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,      metric_definitions=self.env.metric_definitions       ,      py_version="py36"       ,      )





   def 						_snake_case			(			self       ,      snake_case   )					->	Tuple:
      """simple docstring"""





      TrainingJobAnalytics(snake_case   ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    List[Any]   		=	self.create_estimator()

      # run training
      estimator.fit()

      # result dataframe
      a__						:    Union[str, Any]   		=	TrainingJobAnalytics(estimator.latest_training_job.name   ).dataframe()

      # extract kpis
      a__						:    Any   		=	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]   )
      a__						:    Tuple   		=	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]   )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      a__						:    int   		=	(
          Session().describe_training_job(estimator.latest_training_job.name   ).get("TrainingTimeInSeconds"       ,      999_999   )
      )

      # assert kpis
      assert train_runtime <= self.results["train_runtime"]
      assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy   )
      assert all(t <= self.results["eval_loss"] for t in eval_loss   )

      # dump tests result into json file to share in PR
      with open(F"""{estimator.latest_training_job.name}.json"""       ,      "w"   ) as outfile:
         json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}       ,      snake_case   )








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
  )

