



'''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_600, """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_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]		)
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    def  A       (  self      :					str ):


       """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      :					Optional[Any]  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  {
           'enabled': True,
           'processes_per_host': 8,
       }
       UpperCamelCase					=  {
           'enabled': True,
           'parameters': {
               'microbatches': 4,
               'placement_strategy': 'spread',
               'pipeline': 'interleaved',
               'optimize': 'speed',
               'partitions': 4,
               'ddp': True,
           },
       }

       UpperCamelCase					=  {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

       UpperCamelCase					=  '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': 5_0_0,
           }  ,						metric_definitions=self.env.metric_definitions  ,						distribution=UpperCamelCase__  ,						py_version='py36'  ,						)






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					int ):


       """simple docstring"""







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






    @parameterized.expand([(1,)] )
    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.create_estimator(UpperCamelCase__ )

       # 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__ )






'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  (IPNDMScheduler,)
 UpperCAmelCase							=  (("""num_inference_steps""", 50),)







 def _snake_case (							self  ,**a_							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = {"""num_train_timesteps""": 1_000}
        config.update(**a_							)
        return config







 def _snake_case (							self  ,a_=0  ,**a_							)		->   List[str]:
        _UpperCAmelCase  :						Any			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Any			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = self.dummy_sample
        _UpperCAmelCase  :						Union[str, Any]			     = 0.1 * sample
        _UpperCAmelCase  :						List[str]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						str			     = self.get_scheduler_config(**a_							)
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)
               # copy over dummy past residuals
               _UpperCAmelCase  :						str			     = dummy_past_residuals[:]

               if time_step is None:
                      _UpperCAmelCase  :						List[str]			     = scheduler.timesteps[len(scheduler.timesteps							) // 2]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a_							)
                      _UpperCAmelCase  :						List[str]			     = scheduler_class.from_pretrained(a_							)
                      new_scheduler.set_timesteps(a_							)
                      # copy over dummy past residuals
                      _UpperCAmelCase  :						Tuple			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"







 def _snake_case (							self							)		->   Any:
        pass







 def _snake_case (							self  ,a_=0  ,**a_							)		->   Dict:
        _UpperCAmelCase  :						str			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Union[str, Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
        _UpperCAmelCase  :						Tuple			     = 0.1 * sample
        _UpperCAmelCase  :						Optional[Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						Optional[Any]			     = self.get_scheduler_config()
               _UpperCAmelCase  :						Dict			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)

               # copy over dummy past residuals (must be after setting timesteps)
               _UpperCAmelCase  :						Any			     = dummy_past_residuals[:]

               if time_step is None:
                      _UpperCAmelCase  :						Optional[int]			     = scheduler.timesteps[len(scheduler.timesteps							) // 2]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a_							)
                      _UpperCAmelCase  :						str			     = scheduler_class.from_pretrained(a_							)
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(a_							)

                      # copy over dummy past residual (must be after setting timesteps)
                      _UpperCAmelCase  :						Dict			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"







 def _snake_case (							self  ,**a_							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						List[str]			     = self.get_scheduler_config(**a_							)
        _UpperCAmelCase  :						List[Any]			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						List[Any]			     = 10
        _UpperCAmelCase  :						Dict			     = self.dummy_model()
        _UpperCAmelCase  :						Dict			     = self.dummy_sample_deter
        scheduler.set_timesteps(a_							)

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						Optional[Any]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						List[str]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						int			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        return sample







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						int			     = dict(self.forward_default_kwargs							)

        _UpperCAmelCase  :						Optional[Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						int			     = self.get_scheduler_config()
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)

               _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
               _UpperCAmelCase  :						Tuple			     = 0.1 * sample

               if num_inference_steps is not None and hasattr(a_  ,"""set_timesteps"""							):
                      scheduler.set_timesteps(a_							)
               elif num_inference_steps is not None and not hasattr(a_  ,"""set_timesteps"""							):
                      _UpperCAmelCase  :						Optional[Any]			     = num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               _UpperCAmelCase  :						Union[str, Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
               _UpperCAmelCase  :						Optional[int]			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.timesteps[5]
               _UpperCAmelCase  :						Optional[Any]			     = scheduler.timesteps[6]

               _UpperCAmelCase  :						Optional[Any]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Optional[int]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               self.assertEqual(output_a.shape  ,sample.shape							)
               self.assertEqual(output_a.shape  ,output_a.shape							)

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               self.assertEqual(output_a.shape  ,sample.shape							)
               self.assertEqual(output_a.shape  ,output_a.shape							)







 def _snake_case (							self							)		->   List[Any]:
        for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   int:
        for t, num_inference_steps in zip([1, 5, 10]  ,[10, 50, 100]							):
               self.check_over_forward(num_inference_steps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = self.full_loop()
        _UpperCAmelCase  :						Any			     = torch.mean(torch.abs(a_							)							)

        assert abs(result_mean.item() - 2_540_529							) < 10



