"""simple docstring"""






import os

import pytest
from attr import dataclass


__lowerCAmelCase					: str        ="us-east-1"  # defaults region





@dataclass
class 				_A		:
      snake_case__    :     str
      snake_case__    :     Union[str, Any]													=			"arn:aws:iam::558105141721:role/sagemaker_execution_role"
      snake_case__    :     Any													=			{
          "task_name": "mnli",
          "per_device_train_batch_size": 16,
          "per_device_eval_batch_size": 16,
          "do_train": True,
          "do_eval": True,
          "do_predict": True,
          "output_dir": "/opt/ml/model",
          "overwrite_output_dir": True,
          "max_steps": 500,
          "save_steps": 5500,
      }
      snake_case__    :     List[str]													=			{**hyperparameters, "max_steps": 1000}

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.framework == "pytorch":
                  return [
                      {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                      {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                      {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
                  ]
            else:
                  return [
                      {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                      {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                      {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
                  ]

      @property
      def        A__ (		self		):







            """simple docstring"""



            return f'{self.framework}-transfromers-test'

      @property
      def        A__ (		self		):







            """simple docstring"""



            return f'./tests/sagemaker/scripts/{self.framework}'







      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.framework == "pytorch":
                  return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
            else:
                  return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"






@pytest.fixture(scope="""class"""	)
def     UpperCAmelCase__ (       lowerCAmelCase__	)				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			SageMakerTestEnvironment(framework=request.cls.framework	)




"""simple docstring"""






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Union[str, Any]													=			(IPNDMScheduler,)
      snake_case__    :     List[str]													=			(('num_inference_steps', 50),)

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			{"""num_train_timesteps""": 1000}
            config.update(**__lowerCAmelCase		)
            return config

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)
                  # copy over dummy past residuals
                  lowercase														=			dummy_past_residuals[:]

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(__lowerCAmelCase		)
                        lowercase														=			scheduler_class.from_pretrained(__lowerCAmelCase		)
                        new_scheduler.set_timesteps(__lowerCAmelCase		)
                        # copy over dummy past residuals
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self		):







            """simple docstring"""



            pass

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)

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

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

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

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.scheduler_classes[0]
            lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
            lowercase														=			scheduler_class(**__lowerCAmelCase		)

            lowercase														=			10
            lowercase														=			self.dummy_model()
            lowercase														=			self.dummy_sample_deter
            scheduler.set_timesteps(__lowerCAmelCase		)

            for i, t in enumerate(scheduler.timesteps		):
                  lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		).prev_sample

            for i, t in enumerate(scheduler.timesteps		):
                  lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		).prev_sample

            return sample

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)

            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)

                  lowercase														=			self.dummy_sample
                  lowercase														=			0.1 * sample

                  if num_inference_steps is not None and hasattr(__lowerCAmelCase	, """set_timesteps"""		):
                        scheduler.set_timesteps(__lowerCAmelCase		)
                  elif num_inference_steps is not None and not hasattr(__lowerCAmelCase	, """set_timesteps"""		):
                        lowercase														=			num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                  lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.timesteps[5]
                  lowercase														=			scheduler.timesteps[6]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self		):







            """simple docstring"""



            for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            for t, num_inference_steps in zip([1, 5, 10]	, [10, 50, 100]		):
                  self.check_over_forward(num_inference_steps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.full_loop()
            lowercase														=			torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_mean.item() - 254_0529		) < 10



