


"""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''': 650, '''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''': 600, '''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''': 600, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]				)
class 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      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=SCREAMING_SNAKE_CASE      ,   )
      assert hasattr(self      ,   """env""")





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Any   =		F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
      # distributed data settings
      _lowerCamelCase	:					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=SCREAMING_SNAKE_CASE      ,   instance_count=SCREAMING_SNAKE_CASE      ,   instance_type=self.instance_type      ,   debugger_hook_config=SCREAMING_SNAKE_CASE      ,   hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,   metric_definitions=self.env.metric_definitions      ,   distribution=SCREAMING_SNAKE_CASE      ,   py_version="""py36"""      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      TrainingJobAnalytics(SCREAMING_SNAKE_CASE).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv')





   @parameterized.expand([(2,)])
   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      # create estimator
      _lowerCamelCase	:					str   =		self.create_estimator(SCREAMING_SNAKE_CASE)

      # run training
      estimator.fit()

      # result dataframe
      _lowerCamelCase	:					str   =		TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

      # extract kpis
      _lowerCamelCase	:					int   =		list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
      _lowerCamelCase	:					str   =		list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      _lowerCamelCase	:					str   =		(
          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}      ,   SCREAMING_SNAKE_CASE)





"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  None:
      warnings.warn(
          """The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ImageGPTImageProcessor instead."""      ,   SCREAMING_SNAKE_CASE      ,   )
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

