


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			List[Any]      = (DDIMParallelScheduler,)
     lowerCamelCase			:			Union[str, Any]      = (('''eta''', 0.0), ('''num_inference_steps''', 50))







     def 							lowercase__       (						self :						Optional[int]   ,   **_lowercase :						Any      ):
           SCREAMING_SNAKE_CASE__       :  int						     =		{
               '''num_train_timesteps''': 10_00,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''clip_sample''': True,
           }

           config.update(**_lowercase      )
           return config







     def 							lowercase__       (						self :						Optional[Any]   ,   **_lowercase :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  str						     =		self.get_scheduler_config(**_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		10, 0.0

           SCREAMING_SNAKE_CASE__       :  str						     =		self.dummy_model()
           SCREAMING_SNAKE_CASE__       :  str						     =		self.dummy_sample_deter

           scheduler.set_timesteps(_lowercase      )

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		model(_lowercase   ,   _lowercase      )
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		scheduler.step(_lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase      ).prev_sample

           return sample







     def 							lowercase__       (						self :						List[str]      ):
           for timesteps in [1_00, 5_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.get_scheduler_config(steps_offset=1      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		scheduler_class(**_lowercase      )
           scheduler.set_timesteps(5      )
           assert torch.equal(scheduler.timesteps   ,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,   [0.002, 0.02, 0.2, 2]      ):
                 self.check_over_configs(beta_start=_lowercase   ,   beta_end=_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_lowercase      )







     def 							lowercase__       (						self :						List[Any]      ):
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_lowercase      )







     def 							lowercase__       (						self :						List[str]      ):
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_lowercase      )







     def 							lowercase__       (						self :						Optional[int]      ):
           for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=_lowercase      )







     def 							lowercase__       (						self :						Union[str, Any]      ):
           for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=_lowercase      )







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







     def 							lowercase__       (						self :						List[Any]      ):
           for t in [1, 10, 49]:
                 self.check_over_forward(time_step=_lowercase      )







     def 							lowercase__       (						self :						Union[str, Any]      ):
           for t, num_inference_steps in zip([1, 10, 50]   ,   [10, 50, 5_00]      ):
                 self.check_over_forward(time_step=_lowercase   ,   num_inference_steps=_lowercase      )







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







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		scheduler_class(**_lowercase      )

           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      ) - 0.14771      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_80   ,   9_60      ) - 0.32460      )      ) < 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      ) - 0.00979      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_99   ,   9_98      ) - 0.02      )      ) < 1E-5







     def 							lowercase__       (						self :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  int						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  int						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Dict						     =		10, 0.0
           scheduler.set_timesteps(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		self.dummy_model()
           SCREAMING_SNAKE_CASE__       :  str						     =		self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.dummy_sample_deter + 0.1
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.dummy_sample_deter - 0.1

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		samplea.shape[0]
           SCREAMING_SNAKE_CASE__       :  int						     =		torch.stack([samplea, samplea, samplea]   ,   dim=0      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		torch.arange(_lowercase      )[0:3, None].repeat(1   ,   _lowercase      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		model(samples.flatten(0   ,   1      )   ,   timesteps.flatten(0   ,   1      )      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		scheduler.batch_step_no_noise(_lowercase   ,   timesteps.flatten(0   ,   1      )   ,   samples.flatten(0   ,   1      )   ,   _lowercase      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  int						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 1147.7904      ) < 1E-2
           assert abs(result_mean.item() - 0.4982      ) < 1E-3







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.full_loop()

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 172.0067      ) < 1E-2
           assert abs(result_mean.item() - 0.223967      ) < 1E-3







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.full_loop(prediction_type='''v_prediction'''      )

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 52.5302      ) < 1E-2
           assert abs(result_mean.item() - 0.0684      ) < 1E-3







     def 							lowercase__       (						self :						Optional[Any]      ):
           # We specify different beta, so that the first alpha is 0.99
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.full_loop(set_alpha_to_one=_lowercase   ,   beta_start=0.01      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  int						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 149.8295      ) < 1E-2
           assert abs(result_mean.item() - 0.1951      ) < 1E-3







     def 							lowercase__       (						self :						Tuple      ):
           # We specify different beta, so that the first alpha is 0.99
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.full_loop(set_alpha_to_one=_lowercase   ,   beta_start=0.01      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 149.0784      ) < 1E-2
           assert abs(result_mean.item() - 0.1941      ) < 1E-3








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    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      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=__UpperCamelCase				,			)
									assert hasattr(self				,			"""env"""  )






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

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

									_lowercase					       =					"""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=__UpperCamelCase				,			instance_type=self.instance_type				,			debugger_hook_config=__UpperCamelCase				,			hyperparameters={
									        **self.env.hyperparameters,
									        """model_name_or_path""": self.model_name_or_path,
									        """max_steps""": 500,
									    }				,			metric_definitions=self.env.metric_definitions				,			distribution=__UpperCamelCase				,			py_version="""py36"""				,			)






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






			@parameterized.expand([(1,)]  )
			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# create estimator
									_lowercase					       =					self.create_estimator(__UpperCamelCase  )

									# run training
									estimator.fit()

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

									# extract kpis
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]  )
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]  )
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									_lowercase					       =					(
									    Session().describe_training_job(estimator.latest_training_job.name  ).get("""TrainingTimeInSeconds"""				,			999_999  )
									)

									# 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  )