



'''simple docstring'''



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCAmelCase	:		Dict				=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCAmelCase	:		Optional[int]				=   '''    \"""
    Output class for the scheduler\'s step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"""

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
'''






class 					UpperCAmelCase__	(		unittest.TestCase ):



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  tempfile.mkdtemp()
						os.makedirs(os.path.join(self.diffusers_dir			,				"schedulers/"     )     )
						__lowerCAmelCase												=  self.diffusers_dir
						shutil.copy(
						    os.path.join(UpperCamelCase			,				"src/diffusers/schedulers/scheduling_ddpm.py"     )			,				os.path.join(self.diffusers_dir			,				"schedulers/scheduling_ddpm.py"     )			,				)



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  "src/diffusers"
						shutil.rmtree(self.diffusers_dir     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			Optional[Any]:
						__lowerCAmelCase												=  comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
						if overwrite_result is not None:
							__lowerCAmelCase												=  comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
						__lowerCAmelCase												=  black.Mode(target_versions={black.TargetVersion.PYaa}			,				line_length=119     )
						__lowerCAmelCase												=  black.format_str(UpperCamelCase			,				mode=UpperCamelCase     )
						__lowerCAmelCase												=  os.path.join(self.diffusers_dir			,				"new_code.py"     )
						with open(UpperCamelCase			,				"w"			,				newline="\n"     ) as f:
							f.write(UpperCamelCase     )
						if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase     )     ) == 0     )
						else:
							check_copies.is_copy_consistent(f.name			,				overwrite=UpperCamelCase     )
							with open(UpperCamelCase			,				"r"     ) as f:
								self.assertTrue(f.read()			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"     )
						self.assertEqual(UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						# Base copy consistency
						self.check_copy_consistency(
						    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"			,				"DDPMSchedulerOutput"			,				REFERENCE_CODE + "\n"			,				)

						# With no empty line at the end
						self.check_copy_consistency(
						    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"			,				"DDPMSchedulerOutput"			,				UpperCamelCase			,				)

						# Copy consistency with rename
						self.check_copy_consistency(
						    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"			,				"TestSchedulerOutput"			,				re.sub("DDPM"			,				"Test"			,				UpperCamelCase     )			,				)

						# Copy consistency with a really long name
						__lowerCAmelCase												=  "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
						self.check_copy_consistency(
						    F'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'''			,				F'''{long_class_name}SchedulerOutput'''			,				re.sub("Bert"			,				UpperCamelCase			,				UpperCamelCase     )			,				)

						# Copy consistency with overwrite
						self.check_copy_consistency(
						    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"			,				"TestSchedulerOutput"			,				UpperCamelCase			,				overwrite_result=re.sub("DDPM"			,				"Test"			,				UpperCamelCase     )			,				)




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

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

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

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



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

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

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

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

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )