






"""simple docstring"""




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

import black


_snake_case          =   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.
_snake_case          =   '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'






class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						List[Any]							)					->       Dict:
   _a  :       Any             =	tempfile.mkdtemp()
   os.makedirs(os.path.join(self.diffusers_dir							,						"""schedulers/"""							)							)
   _a  :       int             =	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 _lowercase	(     self		:						int							)					->       Dict:
   _a  :       List[str]             =	"""src/diffusers"""
   shutil.rmtree(self.diffusers_dir							)


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						Union[str, Any]=None							)					->       Dict:
   _a  :       int             =	comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
   if overwrite_result is not None:
     _a  :       str             =	comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
   _a  :       int             =	black.Mode(target_versions={black.TargetVersion.PYaa}							,						line_length=119							)
   _a  :       Optional[int]             =	black.format_str(UpperCAmelCase__							,						mode=UpperCAmelCase__							)
   _a  :       Optional[Any]             =	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 _lowercase	(     self		:						Any							)					->       Tuple:
   _a  :       List[str]             =	check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput"""							)
   self.assertEqual(UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						int							)					->       Optional[int]:
   # 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
   _a  :       List[Any]             =	"""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 tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					(IPNDMScheduler,)
 UpperCamelCase					:       int			=					(('''num_inference_steps''', 50),)


 def _lowercase	(     self		:						Union[str, Any]							,						**UpperCAmelCase__		:						Tuple							)					->       int:
   _a  :       Optional[int]             =	{"""num_train_timesteps""": 1000}
   config.update(**UpperCAmelCase__							)
   return config


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Any=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       Union[str, Any]:
   _a  :       Optional[int]             =	dict(self.forward_default_kwargs							)
   _a  :       Dict             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       Union[str, Any]             =	0.1 * sample
   _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config(**UpperCAmelCase__							)
     _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)
     # copy over dummy past residuals
     _a  :       Any             =	dummy_past_residuals[:]

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

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Union[str, Any]             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       new_scheduler.set_timesteps(UpperCAmelCase__							)
       # copy over dummy past residuals
       _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       str             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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


 def _lowercase	(     self		:						Tuple							)					->       List[str]:
   pass


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       List[Any]:
   _a  :       Optional[Any]             =	dict(self.forward_default_kwargs							)
   _a  :       Optional[Any]             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       List[Any]             =	0.1 * sample
   _a  :       Optional[Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Union[str, Any]             =	self.get_scheduler_config()
     _a  :       Optional[Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)

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

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

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

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

     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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

     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       int             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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


 def _lowercase	(     self		:						str							,						**UpperCAmelCase__		:						Any							)					->       List[str]:
   _a  :       Optional[int]             =	self.scheduler_classes[0]
   _a  :       Optional[Any]             =	self.get_scheduler_config(**UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       int             =	10
   _a  :       List[Any]             =	self.dummy_model()
   _a  :       str             =	self.dummy_sample_deter
   scheduler.set_timesteps(UpperCAmelCase__							)

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       str             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       Union[str, Any]             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       Any             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   return sample


 def _lowercase	(     self		:						int							)					->       str:
   _a  :       Dict             =	dict(self.forward_default_kwargs							)

   _a  :       int             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config()
     _a  :       Tuple             =	scheduler_class(**UpperCAmelCase__							)

     _a  :       Tuple             =	self.dummy_sample
     _a  :       Optional[Any]             =	0.1 * sample

     if num_inference_steps is not None and hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       scheduler.set_timesteps(UpperCAmelCase__							)
     elif num_inference_steps is not None and not hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       _a  :       List[str]             =	num_inference_steps

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

     _a  :       Optional[Any]             =	scheduler.timesteps[5]
     _a  :       str             =	scheduler.timesteps[6]

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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

     _a  :       Tuple             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for timesteps in [100, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for t, num_inference_steps in zip([1, 5, 10]							,						[10, 50, 100]							):
     self.check_over_forward(num_inference_steps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)







 def _lowercase	(     self		:						int							)					->       List[Any]:
   _a  :       str             =	self.full_loop()
   _a  :       List[Any]             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_mean.item() - 2540529							) < 10

