



'''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""": 650, """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""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]						)
class 					SCREAMING_SNAKE_CASE					(    unittest.TestCase						):






    """simple docstring"""





    def 					A			(    self :   List[Any]					):


       """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 					A			(    self :   Tuple    ,		UpperCamelCase__ :   Optional[Any]=1					):


       """simple docstring"""







       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 					A			(    self :   List[Any]    ,		UpperCamelCase__ :   Union[str, Any]					):


       """simple docstring"""







       TrainingJobAnalytics(UpperCamelCase__					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)






    def 					A			(    self :   Any					):


       """simple docstring"""







       UpperCamelCase      							=    self.create_estimator()

       # run training
       estimator.fit()

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

       # extract kpis
       UpperCamelCase      							=    list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']					)
       UpperCamelCase      							=    list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']					)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       UpperCamelCase      							=    (
           Session().describe_training_job(estimator.latest_training_job.name					).get('TrainingTimeInSeconds'    ,		9_9_9_9_9_9					)
       )

       # 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__					)



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 							lowerCAmelCase__   ( unittest.TestCase						):

						@property
						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								torch.manual_seed(0					)
								_UpperCamelCase         =						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 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						self.dummy_uncond_unet
								_UpperCamelCase         =						PNDMScheduler()

								_UpperCamelCase         =						PNDMPipeline(unet=a							,				scheduler=a					)
								pndm.to(a					)
								pndm.set_progress_bar_config(disable=a					)

								_UpperCamelCase         =						torch.manual_seed(0					)
								_UpperCamelCase         =						pndm(generator=a							,				num_inference_steps=20							,				output_type="""numpy"""					).images

								_UpperCamelCase         =						torch.manual_seed(0					)
								_UpperCamelCase         =						pndm(generator=a							,				num_inference_steps=20							,				output_type="""numpy"""							,				return_dict=a					)[0]

								_UpperCamelCase         =						image[0, -3:, -3:, -1]
								_UpperCamelCase         =						image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								_UpperCamelCase         =						np.array([1.0, 1.0, 0.0, 1.0, 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 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						"""google/ddpm-cifar10-32"""

								_UpperCamelCase         =						UNetaDModel.from_pretrained(a					)
								_UpperCamelCase         =						PNDMScheduler()

								_UpperCamelCase         =						PNDMPipeline(unet=a							,				scheduler=a					)
								pndm.to(a					)
								pndm.set_progress_bar_config(disable=a					)
								_UpperCamelCase         =						torch.manual_seed(0					)
								_UpperCamelCase         =						pndm(generator=a							,				output_type="""numpy"""					).images

								_UpperCamelCase         =						image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								_UpperCamelCase         =						np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]					)

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



