'''simple docstring'''






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

import pytest
from parameterized import parameterized, 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.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_5_0, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_0_0, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_0_0, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]				)
class    a (       unittest.TestCase				):



  """simple docstring"""







  def    lowercase_  (							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=snake_case_   ,    )
    assert hasattr(self   ,    """env"""					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
    # distributed data settings
    __UpperCAmelCase:					Tuple		= {"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

    # 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=snake_case_   ,    instance_count=snake_case_   ,    instance_type=self.instance_type   ,    debugger_hook_config=snake_case_   ,    hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}   ,    metric_definitions=self.env.metric_definitions   ,    distribution=snake_case_   ,    py_version="""py36"""   ,    )







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    TrainingJobAnalytics(snake_case_					).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''					)







  @parameterized.expand([(2,)]					)
  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= self.create_estimator(snake_case_					)

    # run training
    estimator.fit()

    # result dataframe
    __UpperCAmelCase:					List[Any]		= TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

    # extract kpis
    __UpperCAmelCase:					Optional[int]		= list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]					)
    __UpperCAmelCase:					List[Any]		= list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]					)
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    __UpperCAmelCase:					Dict		= (
        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}   ,    snake_case_					)
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE_	   =			['small', 'medium', 'large']

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    parser.add_argument('--dialogpt_path', default='.', type=str)
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        SCREAMING_SNAKE_CASE_	   =			os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
        SCREAMING_SNAKE_CASE_	   =			F"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )