

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

import black


lowerCAmelCase__								=		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__								=		"""    \"\"\"
    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 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						tempfile.mkdtemp()
			os.makedirs(os.path.join(self.diffusers_dir	,       'schedulers/'))
			_UpperCamelCase   :		List[str]			=						self.diffusers_dir
			shutil.copy(
			    os.path.join(__snake_case	,       'src/diffusers/schedulers/scheduling_ddpm.py')	,       os.path.join(self.diffusers_dir	,       'schedulers/scheduling_ddpm.py')	,       )


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'src/diffusers'
			shutil.rmtree(self.diffusers_dir)


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case=None):
			_UpperCamelCase   :		List[str]			=						comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
			if overwrite_result is not None:
				_UpperCamelCase   :		Union[str, Any]			=						comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
			_UpperCamelCase   :		int			=						black.Mode(target_versions={black.TargetVersion.PYaa}	,       line_length=1_19)
			_UpperCamelCase   :		List[Any]			=						black.format_str(__snake_case	,       mode=__snake_case)
			_UpperCamelCase   :		Dict			=						os.path.join(self.diffusers_dir	,       'new_code.py')
			with open(__snake_case	,       'w'	,       newline='\n') as f:
				f.write(__snake_case)
			if overwrite_result is None:
				self.assertTrue(len(check_copies.is_copy_consistent(__snake_case)) == 0)
			else:
				check_copies.is_copy_consistent(f.name	,       overwrite=__snake_case)
				with open(__snake_case	,       'r') as f:
					self.assertTrue(f.read()	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput')
			self.assertEqual(__snake_case	,       __snake_case)






		def 		A__					(				self):
			# 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'	,       __snake_case	,       )

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

			# Copy consistency with a really long name
			_UpperCamelCase   :		Dict			=						'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'	,       __snake_case	,       __snake_case)	,       )

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







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

			# train with a DDPM scheduler
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		int			=						ddpm_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Any			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		str			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		Dict			=						ddim_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		Tuple			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))
			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))




