



'''simple docstring'''





import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Tuple				=      '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=0      )    ->		int:
      lowerCAmelCase__ :  Any							      =					np.random.RandomState(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					{
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 7.5,
          """output_type""": """numpy""",
      }
      return inputs




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  Optional[int]							      =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,provider="""CPUExecutionProvider"""      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  Optional[Any]							      =					pipe(**__UpperCAmelCase      ).images
      lowerCAmelCase__ :  Any							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      lowerCAmelCase__ :  Optional[int]							      =					np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  List[str]							      =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,provider="""CPUExecutionProvider"""      )
      lowerCAmelCase__ :  int							      =					PNDMScheduler.from_config(pipe.scheduler.config  ,skip_prk_steps=__UpperCAmelCase      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  Optional[int]							      =					pipe(**__UpperCAmelCase      ).images
      lowerCAmelCase__ :  List[str]							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      lowerCAmelCase__ :  str							      =					np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  List[str]							      =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,provider="""CPUExecutionProvider"""      )
      lowerCAmelCase__ :  Optional[Any]							      =					LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  Union[str, Any]							      =					pipe(**__UpperCAmelCase      ).images
      lowerCAmelCase__ :  Optional[int]							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      lowerCAmelCase__ :  int							      =					np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Tuple							      =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,provider="""CPUExecutionProvider"""      )
      lowerCAmelCase__ :  List[Any]							      =					EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Tuple							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  Union[str, Any]							      =					pipe(**__UpperCAmelCase      ).images
      lowerCAmelCase__ :  str							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      lowerCAmelCase__ :  Dict							      =					np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  str							      =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,provider="""CPUExecutionProvider"""      )
      lowerCAmelCase__ :  Tuple							      =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  Union[str, Any]							      =					pipe(**__UpperCAmelCase      ).images
      lowerCAmelCase__ :  Dict							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      lowerCAmelCase__ :  Optional[Any]							      =					np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  List[str]							      =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,provider="""CPUExecutionProvider"""      )
      lowerCAmelCase__ :  Any							      =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Union[str, Any]							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  Optional[Any]							      =					pipe(**__UpperCAmelCase      ).images
      lowerCAmelCase__ :  Any							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      lowerCAmelCase__ :  str							      =					np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  Tuple							      =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,provider="""CPUExecutionProvider"""      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[int]							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  str							      =					3 * [inputs["""prompt"""]]

      # forward
      lowerCAmelCase__ :  Union[str, Any]							      =					pipe(**__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					output.images[0, -3:, -3:, -1]

      lowerCAmelCase__ :  Union[str, Any]							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  Dict							      =					3 * [inputs.pop("""prompt"""      )]

      lowerCAmelCase__ :  str							      =					pipe.tokenizer(
          __UpperCAmelCase  ,padding="""max_length"""  ,max_length=pipe.tokenizer.model_max_length  ,truncation=__UpperCAmelCase  ,return_tensors="""np"""  ,)
      lowerCAmelCase__ :  Union[str, Any]							      =					text_inputs["""input_ids"""]

      lowerCAmelCase__ :  Any							      =					pipe.text_encoder(input_ids=text_inputs.astype(np.intaa      )      )[0]

      lowerCAmelCase__ :  Optional[int]							      =					prompt_embeds

      # forward
      lowerCAmelCase__ :  Optional[Any]							      =					pipe(**__UpperCAmelCase      )
      lowerCAmelCase__ :  int							      =					output.images[0, -3:, -3:, -1]

      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4






  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      lowerCAmelCase__ :  List[Any]							      =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,provider="""CPUExecutionProvider"""      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  Dict							      =					3 * ["""this is a negative prompt"""]
      lowerCAmelCase__ :  List[Any]							      =					negative_prompt
      lowerCAmelCase__ :  Tuple							      =					3 * [inputs["""prompt"""]]

      # forward
      lowerCAmelCase__ :  Optional[int]							      =					pipe(**__UpperCAmelCase      )
      lowerCAmelCase__ :  Any							      =					output.images[0, -3:, -3:, -1]

      lowerCAmelCase__ :  Union[str, Any]							      =					self.get_dummy_inputs()
      lowerCAmelCase__ :  List[str]							      =					3 * [inputs.pop("""prompt"""      )]

      lowerCAmelCase__ :  Tuple							      =					[]
      for p in [prompt, negative_prompt]:
          lowerCAmelCase__ :  Any							      =					pipe.tokenizer(
              __UpperCAmelCase  ,padding="""max_length"""  ,max_length=pipe.tokenizer.model_max_length  ,truncation=__UpperCAmelCase  ,return_tensors="""np"""  ,)
          lowerCAmelCase__ :  Any							      =					text_inputs["""input_ids"""]

          embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa      )      )[0]      )

      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					embeds

      # forward
      lowerCAmelCase__ :  Any							      =					pipe(**__UpperCAmelCase      )
      lowerCAmelCase__ :  Any							      =					output.images[0, -3:, -3:, -1]

      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4







@nightly
@require_onnxruntime
@require_torch_gpu
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Dict							      =					ort.SessionOptions()
      lowerCAmelCase__ :  Dict							      =					False
      return options




  def 							UpperCAmelCase_	(    self      )    ->		str:
      # using the PNDM scheduler by default
      lowerCAmelCase__ :  Union[str, Any]							      =					OnnxStableDiffusionPipeline.from_pretrained(
          """CompVis/stable-diffusion-v1-4"""  ,revision="""onnx"""  ,safety_checker=__UpperCAmelCase  ,feature_extractor=__UpperCAmelCase  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					"""A painting of a squirrel eating a burger"""
      np.random.seed(0      )
      lowerCAmelCase__ :  List[Any]							      =					sd_pipe([prompt]  ,guidance_scale=6.0  ,num_inference_steps=10  ,output_type="""np"""      )
      lowerCAmelCase__ :  Optional[int]							      =					output.images

      lowerCAmelCase__ :  Optional[Any]							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      lowerCAmelCase__ :  int							      =					np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  Dict							      =					DDIMScheduler.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""  ,subfolder="""scheduler"""  ,revision="""onnx"""      )
      lowerCAmelCase__ :  List[Any]							      =					OnnxStableDiffusionPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""  ,revision="""onnx"""  ,scheduler=__UpperCAmelCase  ,safety_checker=__UpperCAmelCase  ,feature_extractor=__UpperCAmelCase  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[int]							      =					"""open neural network exchange"""
      lowerCAmelCase__ :  Optional[int]							      =					np.random.RandomState(0      )
      lowerCAmelCase__ :  str							      =					sd_pipe([prompt]  ,guidance_scale=7.5  ,num_inference_steps=10  ,generator=__UpperCAmelCase  ,output_type="""np"""      )
      lowerCAmelCase__ :  Optional[int]							      =					output.images
      lowerCAmelCase__ :  Tuple							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      lowerCAmelCase__ :  int							      =					np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  str							      =					LMSDiscreteScheduler.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""  ,subfolder="""scheduler"""  ,revision="""onnx"""      )
      lowerCAmelCase__ :  Tuple							      =					OnnxStableDiffusionPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""  ,revision="""onnx"""  ,scheduler=__UpperCAmelCase  ,safety_checker=__UpperCAmelCase  ,feature_extractor=__UpperCAmelCase  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					"""open neural network exchange"""
      lowerCAmelCase__ :  List[Any]							      =					np.random.RandomState(0      )
      lowerCAmelCase__ :  Optional[int]							      =					sd_pipe([prompt]  ,guidance_scale=7.5  ,num_inference_steps=10  ,generator=__UpperCAmelCase  ,output_type="""np"""      )
      lowerCAmelCase__ :  str							      =					output.images
      lowerCAmelCase__ :  int							      =					image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      lowerCAmelCase__ :  List[Any]							      =					np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      lowerCAmelCase__ :  int							      =					0

      def test_callback_fn(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ) -> None:
          lowerCAmelCase__ :  int							      =					True
          nonlocal number_of_steps
          number_of_steps += 1
          if step == 0:
              assert latents.shape == (1, 4, 64, 64)
              lowerCAmelCase__ :  List[str]							      =					latents[0, -3:, -3:, -1]
              lowerCAmelCase__ :  int							      =					np.array(
                  [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]      )

              assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 1E-3
          elif step == 5:
              assert latents.shape == (1, 4, 64, 64)
              lowerCAmelCase__ :  List[Any]							      =					latents[0, -3:, -3:, -1]
              lowerCAmelCase__ :  Optional[Any]							      =					np.array(
                  [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]      )

              assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 1E-3

      lowerCAmelCase__ :  int							      =					False

      lowerCAmelCase__ :  Any							      =					OnnxStableDiffusionPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""  ,revision="""onnx"""  ,safety_checker=__UpperCAmelCase  ,feature_extractor=__UpperCAmelCase  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					"""Andromeda galaxy in a bottle"""

      lowerCAmelCase__ :  List[str]							      =					np.random.RandomState(0      )
      pipe(
          prompt=__UpperCAmelCase  ,num_inference_steps=5  ,guidance_scale=7.5  ,generator=__UpperCAmelCase  ,callback=__UpperCAmelCase  ,callback_steps=1  ,)
      assert test_callback_fn.has_been_called
      assert number_of_steps == 6






  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Any							      =					OnnxStableDiffusionPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""  ,revision="""onnx"""  ,safety_checker=__UpperCAmelCase  ,feature_extractor=__UpperCAmelCase  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
      assert isinstance(__UpperCAmelCase  ,__UpperCAmelCase      )
      assert pipe.safety_checker is None

      lowerCAmelCase__ :  Dict							      =					pipe("""example prompt"""  ,num_inference_steps=2      ).images[0]
      assert image is not None

      # check that there's no error when saving a pipeline with one of the models being None
      with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					OnnxStableDiffusionPipeline.from_pretrained(__UpperCAmelCase      )

      # sanity check that the pipeline still works
      assert pipe.safety_checker is None
      lowerCAmelCase__ :  Union[str, Any]							      =					pipe("""example prompt"""  ,num_inference_steps=2      ).images[0]
      assert image is not None







'''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        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Optional[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=__UpperCAmelCase  ,)
      assert hasattr(self  ,"""env"""      )




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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      TrainingJobAnalytics(__UpperCAmelCase      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )






  @parameterized.expand([(2,)]      )
  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      # create estimator
      lowerCAmelCase__ :  List[Any]							      =					self.create_estimator(__UpperCAmelCase      )

      # run training
      estimator.fit()

      # result dataframe
      lowerCAmelCase__ :  Any							      =					TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      lowerCAmelCase__ :  int							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]      )
      lowerCAmelCase__ :  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
      lowerCAmelCase__ :  List[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}  ,__UpperCAmelCase      )


