


'''simple docstring'''

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": 6_50, "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": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]			)
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """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=UpperCamelCase			,  )
   assert hasattr(self			,  "env"     )






  def   snake_case		(    self			,  UpperCamelCase=1     ):







   """simple docstring"""
   # creates estimator
   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=UpperCamelCase			,  instance_type=self.instance_type			,  debugger_hook_config=UpperCamelCase			,  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			,  UpperCamelCase     ):







   """simple docstring"""
   TrainingJobAnalytics(UpperCamelCase     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )







  def   snake_case		(    self     ):







   """simple docstring"""
   # create estimator
   lowerCamelCase_      =  self.create_estimator()

   # run training
   estimator.fit()

   # result dataframe
   lowerCamelCase_      =  TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

   # extract kpis
   lowerCamelCase_      =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
   lowerCamelCase_      =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
   # get train time from SageMaker job, this includes starting, preprocessing, stopping
   lowerCamelCase_      =  (
       Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds"			,  99_9999     )
   )

   # 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}			,  UpperCamelCase     )








"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

			# Load weights from numpy
			load_tf_weights_in_gpta(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			# Save pytorch-model
			lowerCamelCase__		:		List[str]			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			lowerCamelCase__		:		int			=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
			torch.save(model.state_dict()							,		__lowerCamelCase	)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''	)
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
		_lowercase : Optional[Any]      						=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--gpt2_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
		        "This specifies the model architecture."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




