


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 						_UpperCamelCase						(     _a	):
							'''simple docstring'''







							__UpperCAmelCase		:					str          =(DDIMParallelScheduler,)
							__UpperCAmelCase		:					Any          =(("""eta""", 0.0), ("""num_inference_steps""", 5_0))

							def     snake_case       (  self					,     **__a			):
										__lowerCAmelCase       					=    {
										    "num_train_timesteps": 10_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "clip_sample": True,
										}

										config.update(**_A			)
										return config

							def     snake_case       (  self					,     **__a			):
										__lowerCAmelCase       					=    self.scheduler_classes[0]
										__lowerCAmelCase       					=    self.get_scheduler_config(**_A			)
										__lowerCAmelCase       					=    scheduler_class(**_A			)

										__lowerCAmelCase					,			__lowerCAmelCase       					=    10, 0.0

										__lowerCAmelCase       					=    self.dummy_model()
										__lowerCAmelCase       					=    self.dummy_sample_deter

										scheduler.set_timesteps(_A			)

										for t in scheduler.timesteps:
													__lowerCAmelCase       					=    model(_A					,     _A			)
													__lowerCAmelCase       					=    scheduler.step(_A					,     _A					,     _A					,     _A			).prev_sample

										return sample

							def     snake_case       (  self			):
										for timesteps in [1_00, 5_00, 10_00]:
													self.check_over_configs(num_train_timesteps=_A			)

							def     snake_case       (  self			):
										for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=_A			)

										__lowerCAmelCase       					=    self.scheduler_classes[0]
										__lowerCAmelCase       					=    self.get_scheduler_config(steps_offset=1			)
										__lowerCAmelCase       					=    scheduler_class(**_A			)
										scheduler.set_timesteps(5			)
										assert torch.equal(scheduler.timesteps					,     torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]			)			)

							def     snake_case       (  self			):
										for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,     [0.0_0_2, 0.0_2, 0.2, 2]			):
													self.check_over_configs(beta_start=_A					,     beta_end=_A			)

							def     snake_case       (  self			):
										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_A			)

							def     snake_case       (  self			):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_A			)

							def     snake_case       (  self			):
										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_A			)

							def     snake_case       (  self			):
										for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=_A			)

							def     snake_case       (  self			):
										for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=_A			)

							def     snake_case       (  self			):
										self.check_over_configs(thresholding=_A			)
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=_A					,     prediction_type=_A					,     sample_max_value=_A					,     )

							def     snake_case       (  self			):
										for t in [1, 10, 49]:
													self.check_over_forward(time_step=_A			)

							def     snake_case       (  self			):
										for t, num_inference_steps in zip([1, 10, 50]					,     [10, 50, 5_00]			):
													self.check_over_forward(time_step=_A					,     num_inference_steps=_A			)

							def     snake_case       (  self			):
										for t, eta in zip([1, 10, 49]					,     [0.0, 0.5, 1.0]			):
													self.check_over_forward(time_step=_A					,     eta=_A			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.scheduler_classes[0]
										__lowerCAmelCase       					=    self.get_scheduler_config()
										__lowerCAmelCase       					=    scheduler_class(**_A			)

										assert torch.sum(torch.abs(scheduler._get_variance(0					,     0			) - 0.0			)			) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_20					,     4_00			) - 0.1_4_7_7_1			)			) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_80					,     9_60			) - 0.3_2_4_6_0			)			) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(0					,     0			) - 0.0			)			) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87					,     4_86			) - 0.0_0_9_7_9			)			) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99					,     9_98			) - 0.0_2			)			) < 1e-5

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.scheduler_classes[0]
										__lowerCAmelCase       					=    self.get_scheduler_config()
										__lowerCAmelCase       					=    scheduler_class(**_A			)

										__lowerCAmelCase					,			__lowerCAmelCase       					=    10, 0.0
										scheduler.set_timesteps(_A			)

										__lowerCAmelCase       					=    self.dummy_model()
										__lowerCAmelCase       					=    self.dummy_sample_deter
										__lowerCAmelCase       					=    self.dummy_sample_deter + 0.1
										__lowerCAmelCase       					=    self.dummy_sample_deter - 0.1

										__lowerCAmelCase       					=    samplea.shape[0]
										__lowerCAmelCase       					=    torch.stack([samplea, samplea, samplea]					,     dim=0			)
										__lowerCAmelCase       					=    torch.arange(_A			)[0:3, None].repeat(1					,     _A			)

										__lowerCAmelCase       					=    model(samples.flatten(0					,     1			)					,     timesteps.flatten(0					,     1			)			)
										__lowerCAmelCase       					=    scheduler.batch_step_no_noise(_A					,     timesteps.flatten(0					,     1			)					,     samples.flatten(0					,     1			)					,     _A			)

										__lowerCAmelCase       					=    torch.sum(torch.abs(_A			)			)
										__lowerCAmelCase       					=    torch.mean(torch.abs(_A			)			)

										assert abs(result_sum.item() - 1_1_4_7.7_9_0_4			) < 1e-2
										assert abs(result_mean.item() - 0.4_9_8_2			) < 1e-3

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.full_loop()

										__lowerCAmelCase       					=    torch.sum(torch.abs(_A			)			)
										__lowerCAmelCase       					=    torch.mean(torch.abs(_A			)			)

										assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1e-2
										assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1e-3

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.full_loop(prediction_type="v_prediction"			)

										__lowerCAmelCase       					=    torch.sum(torch.abs(_A			)			)
										__lowerCAmelCase       					=    torch.mean(torch.abs(_A			)			)

										assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1e-2
										assert abs(result_mean.item() - 0.0_6_8_4			) < 1e-3

							def     snake_case       (  self			):
										# We specify different beta, so that the first alpha is 0.99
										__lowerCAmelCase       					=    self.full_loop(set_alpha_to_one=_A					,     beta_start=0.0_1			)
										__lowerCAmelCase       					=    torch.sum(torch.abs(_A			)			)
										__lowerCAmelCase       					=    torch.mean(torch.abs(_A			)			)

										assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1e-2
										assert abs(result_mean.item() - 0.1_9_5_1			) < 1e-3



							def     snake_case       (  self			):
										# We specify different beta, so that the first alpha is 0.99
										__lowerCAmelCase       					=    self.full_loop(set_alpha_to_one=_A					,     beta_start=0.0_1			)
										__lowerCAmelCase       					=    torch.sum(torch.abs(_A			)			)
										__lowerCAmelCase       					=    torch.mean(torch.abs(_A			)			)

										assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1e-2
										assert abs(result_mean.item() - 0.1_9_4_1			) < 1e-3








"""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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]	)
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  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     snake_case       (  self					,     __a			):
										# configuration for running training on smdistributed Model Parallel
										__lowerCAmelCase       					=    {
										    "enabled": True,
										    "processes_per_host": 8,
										}
										__lowerCAmelCase       					=    {
										    "enabled": True,
										    "parameters": {
										        "microbatches": 4,
										        "placement_strategy": "spread",
										        "pipeline": "interleaved",
										        "optimize": "speed",
										        "partitions": 4,
										        "ddp": True,
										    },
										}

										__lowerCAmelCase       					=    {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

										__lowerCAmelCase       					=    "trainer" if self.script == "run_glue.py" else "smtrainer"
										# 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=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"					,     instance_count=__a					,     instance_type=self.instance_type					,     debugger_hook_config=__a					,     hyperparameters={
										        **self.env.hyperparameters,
										        "model_name_or_path": self.model_name_or_path,
										        "max_steps": 5_00,
										    }					,     metric_definitions=self.env.metric_definitions					,     distribution=__a					,     py_version="py36"					,     )

							def     snake_case       (  self					,     __a			):
										TrainingJobAnalytics(__a			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)



							@parameterized.expand([(1,)]			)
							def     snake_case       (  self					,     __a			):
										# create estimator
										__lowerCAmelCase       					=    self.create_estimator(__a			)

										# run training
										estimator.fit()

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

										# extract kpis
										__lowerCAmelCase       					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
										__lowerCAmelCase       					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]			)
										# get train time from SageMaker job, this includes starting, preprocessing, stopping
										__lowerCAmelCase       					=    (
										    Session().describe_training_job(estimator.latest_training_job.name			).get("TrainingTimeInSeconds"					,     99_99_99			)
										)

										# 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			)




