






"""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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


       def 						_UpperCamelCase       (			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 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Optional[Any]							):
             '''simple docstring'''





             A__						:  Dict		  =  F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
             # distributed data settings
             A__						:  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 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Optional[int]							):
             '''simple docstring'''





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


       @parameterized.expand([(2,)]							)
       def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						str							):
             '''simple docstring'''





             A__						:  Dict		  =  self.create_estimator(snake_case							)

             # run training
             estimator.fit()

             # result dataframe
             A__						:  int		  =  TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

             # extract kpis
             A__						:  Tuple		  =  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]							)
             A__						:  Union[str, Any]		  =  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]							)
             # get train time from SageMaker job, this includes starting, preprocessing, stopping
             A__						:  Union[str, Any]		  =  (
                 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 unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							@property
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  Optional[Any]		  =  UNetaDModel(
													    block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=3			,    out_channels=3			,    down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,    up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,    )
													return model


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.dummy_uncond_unet
													A__						:  Dict		  =  ScoreSdeVeScheduler()

													A__						:  str		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  torch.manual_seed(0							)
													A__						:  Any		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  List[str]		  =  torch.manual_seed(0							)
													A__						:  int		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case			,    return_dict=snake_case							)[
													    0
													]

													A__						:  int		  =  image[0, -3:, -3:, -1]
													A__						:  Optional[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A__						:  Optional[Any]		  =  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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






@slow
@require_torch
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """google/ncsnpp-church-256"""
													A__						:  Tuple		  =  UNetaDModel.from_pretrained(snake_case							)

													A__						:  int		  =  ScoreSdeVeScheduler.from_pretrained(snake_case							)

													A__						:  List[Any]		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Dict		  =  torch.manual_seed(0							)
													A__						:  Optional[int]		  =  sde_ve(num_inference_steps=10			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  Optional[int]		  =  image[0, -3:, -3:, -1]

													assert image.shape == (1, 256, 256, 3)

													A__						:  Dict		  =  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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






