






import unittest

import torch

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


__UpperCAmelCase								=						False



class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=3_2   )	->							List[str]:
							set_seed(0   )
							UpperCAmelCase_    :      int            =						UNetaDModel(sample_size=__lowerCAmelCase				,		in_channels=3				,		out_channels=3   )
							UpperCAmelCase_    :      Dict            =						torch.optim.SGD(model.parameters()				,		lr=0.00_01   )
							return model, optimizer



		@slow
		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      Optional[Any]            =						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							UpperCAmelCase_    :      List[str]            =						DDPMScheduler(
							    num_train_timesteps=1_0_0_0				,		beta_start=0.00_01				,		beta_end=0.02				,		beta_schedule='linear'				,		clip_sample=__lowerCAmelCase				,		)
							UpperCAmelCase_    :      Optional[Any]            =						DDIMScheduler(
							    num_train_timesteps=1_0_0_0				,		beta_start=0.00_01				,		beta_end=0.02				,		beta_schedule='linear'				,		clip_sample=__lowerCAmelCase				,		)

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

							# shared batches for DDPM and DDIM
							set_seed(0   )
							UpperCAmelCase_    :      Tuple            =						[torch.randn((4, 3, 3_2, 3_2)   ).clip(-1				,		1   ).to(__lowerCAmelCase   ) for _ in range(4   )]
							UpperCAmelCase_    :      Optional[int]            =						[torch.randn((4, 3, 3_2, 3_2)   ).to(__lowerCAmelCase   ) for _ in range(4   )]
							UpperCAmelCase_    :      str            =						[torch.randint(0				,		1_0_0_0				,		(4,)   ).long().to(__lowerCAmelCase   ) for _ in range(4   )]

							# train with a DDPM scheduler
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						self.get_model_optimizer(resolution=3_2   )
							model.train().to(__lowerCAmelCase   )
							for i in range(4   ):
												optimizer.zero_grad()
												UpperCAmelCase_    :      Any            =						ddpm_scheduler.add_noise(clean_images[i]				,		noise[i]				,		timesteps[i]   )
												UpperCAmelCase_    :      List[Any]            =						model(__lowerCAmelCase				,		timesteps[i]   ).sample
												UpperCAmelCase_    :      Union[str, Any]            =						torch.nn.functional.mse_loss(__lowerCAmelCase				,		noise[i]   )
												loss.backward()
												optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						self.get_model_optimizer(resolution=3_2   )
							model.train().to(__lowerCAmelCase   )
							for i in range(4   ):
												optimizer.zero_grad()
												UpperCAmelCase_    :      int            =						ddim_scheduler.add_noise(clean_images[i]				,		noise[i]				,		timesteps[i]   )
												UpperCAmelCase_    :      Optional[int]            =						model(__lowerCAmelCase				,		timesteps[i]   ).sample
												UpperCAmelCase_    :      Union[str, Any]            =						torch.nn.functional.mse_loss(__lowerCAmelCase				,		noise[i]   )
												loss.backward()
												optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(__lowerCAmelCase				,		__lowerCAmelCase				,		atol=1E-5   )   )
							self.assertTrue(torch.allclose(__lowerCAmelCase				,		__lowerCAmelCase				,		atol=1E-5   )   )













# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile







def    __lowerCamelCase					(							__a   :List[str]    ,     __a   :List[Any]    ,     __a   :Union[str, Any]    ,     __a   :List[Any]    )		->				Dict:






		"""simple docstring"""

		A__        =   multiprocessing.Manager()
		A__        =   manager.list()

		A__        =   multiprocessing.Process(target=__a    ,     args=(check_program, result, timeout)    )
		p.start()
		p.join(timeout=timeout + 1    )
		if p.is_alive():
				p.kill()

		if not result:
				result.append("""timed out"""    )

		return {
		    "task_id": task_id,
		    "passed": result[0] == "passed",
		    "result": result[0],
		    "completion_id": completion_id,
		}







def    __lowerCamelCase					(							__a   :Optional[Any]    ,     __a   :Any    ,     __a   :List[Any]    )		->				Union[str, Any]:






		"""simple docstring"""

		with create_tempdir():
				# These system calls are needed when cleaning up tempdir.
				import os
				import shutil

				A__        =   shutil.rmtree
				A__        =   os.rmdir
				A__        =   os.chdir

				# Disable functionalities that can make destructive changes to the test.
				reliability_guard()

				# Run program.
				try:
						A__        =   {}
						with swallow_io():
								with time_limit(__a    ):
										exec(__a    ,     __a    )
						result.append("""passed"""    )
				except TimeoutException:
						result.append("""timed out"""    )
				except BaseException as e:
						result.append(F'failed: {e}'    )

				# Needed for cleaning up.
				A__        =   rmtree
				A__        =   rmdir
				A__        =   chdir







@contextlib.contextmanager
def    __lowerCamelCase					(							__a   :List[str]    )		->				Dict:






		"""simple docstring"""

		def signal_handler(__a   :List[Any]    ,     __a   :Optional[Any]    ):
				raise TimeoutException("""Timed out!"""    )

		signal.setitimer(signal.ITIMER_REAL    ,     __a    )
		signal.signal(signal.SIGALRM    ,     __a    )
		try:
				yield
		finally:
				signal.setitimer(signal.ITIMER_REAL    ,     0    )







@contextlib.contextmanager
def    __lowerCamelCase					(							)		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   WriteOnlyStringIO()
		with contextlib.redirect_stdout(__a    ):
				with contextlib.redirect_stderr(__a    ):
						with redirect_stdin(__a    ):
								yield







@contextlib.contextmanager
def    __lowerCamelCase					(							)		->				Dict:






		"""simple docstring"""

		with tempfile.TemporaryDirectory() as dirname:
				with chdir(__a    ):
						yield dirname


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			(io.StringIO    ):
			'''simple docstring'''




			def    a_       (			self   :    Any							,	*__lowerCAmelCase   :    List[str]							,	**__lowerCAmelCase   :    str   )	->	Dict:







					"""simple docstring"""






					raise OSError






			def    a_       (			self   :    Optional[Any]							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Optional[int]   )	->	str:







					"""simple docstring"""






					raise OSError






			def    a_       (			self   :    Optional[Any]							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Any   )	->	int:







					"""simple docstring"""






					raise OSError







			def    a_       (			self   :    str							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Union[str, Any]   )	->	int:







					"""simple docstring"""






					return False


class   A			(contextlib._RedirectStream    ):  # type: ignore
			'''simple docstring'''




			__lowerCamelCase	:       Union[str, Any]							=    '''stdin'''







@contextlib.contextmanager
def    __lowerCamelCase					(							__a   :Union[str, Any]    )		->				List[str]:






		"""simple docstring"""

		if root == ".":
				yield
				return
		A__        =   os.getcwd()
		os.chdir(__a    )
		try:
				yield
		except BaseException as exc:
				raise exc
		finally:
				os.chdir(__a    )







def    __lowerCamelCase					(							__a   :Union[str, Any]=None    )		->				Dict:






		"""simple docstring"""

		if maximum_memory_bytes is not None:
				import resource

				resource.setrlimit(resource.RLIMIT_AS    ,     (maximum_memory_bytes, maximum_memory_bytes)    )
				resource.setrlimit(resource.RLIMIT_DATA    ,     (maximum_memory_bytes, maximum_memory_bytes)    )
				if not platform.uname().system == "Darwin":
						resource.setrlimit(resource.RLIMIT_STACK    ,     (maximum_memory_bytes, maximum_memory_bytes)    )

		faulthandler.disable()

		import builtins

		A__        =   None
		A__        =   None

		import os

		A__        =   """1"""

		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None

		import shutil

		A__        =   None
		A__        =   None
		A__        =   None

		import subprocess

		A__        =   None  # type: ignore

		A__        =   None

		import sys

		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None



