

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


A_			:					Tuple    			=     False



class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self       ,			A__=32     ):
						set_seed(0     )
						A__				:       Dict	  =						UNetaDModel(sample_size=A__       ,			in_channels=3       ,			out_channels=3     )
						A__				:       Dict	  =						torch.optim.SGD(model.parameters()       ,			lr=0.0_0_0_1     )
						return model, optimizer






					@slow
					def     __A     (					self     ):
						A__				:       int	  =						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
						A__				:       Any	  =						DDPMScheduler(
						    num_train_timesteps=1000       ,			beta_start=0.0_0_0_1       ,			beta_end=0.0_2       ,			beta_schedule="""linear"""       ,			clip_sample=A__       ,			)
						A__				:       Union[str, Any]	  =						DDIMScheduler(
						    num_train_timesteps=1000       ,			beta_start=0.0_0_0_1       ,			beta_end=0.0_2       ,			beta_schedule="""linear"""       ,			clip_sample=A__       ,			)

						assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

						# shared batches for DDPM and DDIM
						set_seed(0     )
						A__				:       Any	  =						[torch.randn((4, 3, 32, 32)     ).clip(-1       ,			1     ).to(A__     ) for _ in range(4     )]
						A__				:       List[Any]	  =						[torch.randn((4, 3, 32, 32)     ).to(A__     ) for _ in range(4     )]
						A__				:       List[Any]	  =						[torch.randint(0       ,			1000       ,			(4,)     ).long().to(A__     ) for _ in range(4     )]

						# train with a DDPM scheduler
						A__  ,  A__				:       Tuple	  =						self.get_model_optimizer(resolution=32     )
						model.train().to(A__     )
						for i in range(4     ):
							optimizer.zero_grad()
							A__				:       Dict	  =						ddpm_scheduler.add_noise(clean_images[i]       ,			noise[i]       ,			timesteps[i]     )
							A__				:       int	  =						model(A__       ,			timesteps[i]     ).sample
							A__				:       Optional[int]	  =						torch.nn.functional.mse_loss(A__       ,			noise[i]     )
							loss.backward()
							optimizer.step()
						del model, optimizer

						# recreate the model and optimizer, and retry with DDIM
						A__  ,  A__				:       str	  =						self.get_model_optimizer(resolution=32     )
						model.train().to(A__     )
						for i in range(4     ):
							optimizer.zero_grad()
							A__				:       int	  =						ddim_scheduler.add_noise(clean_images[i]       ,			noise[i]       ,			timesteps[i]     )
							A__				:       Optional[int]	  =						model(A__       ,			timesteps[i]     ).sample
							A__				:       Optional[Any]	  =						torch.nn.functional.mse_loss(A__       ,			noise[i]     )
							loss.backward()
							optimizer.step()
						del model, optimizer

						self.assertTrue(torch.allclose(A__       ,			A__       ,			atol=1e-5     )     )
						self.assertTrue(torch.allclose(A__       ,			A__       ,			atol=1e-5     )     )






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_50, '''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_00, '''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_00, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]							)
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						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=A__       ,			)
						assert hasattr(self       ,			"""env"""     )






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






					def     __A     (					self       ,			A__     ):
						TrainingJobAnalytics(A__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )






					@parameterized.expand([(2,)]     )
					def     __A     (					self       ,			A__     ):
						# create estimator
						A__				:       str	  =						self.create_estimator(A__     )

						# run training
						estimator.fit()

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

						# extract kpis
						A__				:       List[Any]	  =						list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]     )
						A__				:       Optional[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__				:       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}       ,			A__     )



