


"""simple docstring"""







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  (DDIMParallelScheduler,)
 __lowerCAmelCase 					=  (("eta", 0.0), ("num_inference_steps", 50))


 def       SCREAMING_SNAKE_CASE							(		self ,    **__A						)					->     Optional[Any]:
     a 		={
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0_001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''clip_sample''': True,
     }

     config.update(**__A						)
     return config


 def       SCREAMING_SNAKE_CASE							(		self ,    **__A						)					->     Tuple:
     a 		=self.scheduler_classes[0]
     a 		=self.get_scheduler_config(**__A						)
     a 		=scheduler_class(**__A						)

     a		,      a 		=10, 0.0

     a 		=self.dummy_model()
     a 		=self.dummy_sample_deter

     scheduler.set_timesteps(__A						)

     for t in scheduler.timesteps:
         a 		=model(__A ,    __A						)
         a 		=scheduler.step(__A ,    __A ,    __A ,    __A						).prev_sample

     return sample


 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     for timesteps in [100, 500, 1000]:
         self.check_over_configs(num_train_timesteps=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=__A						)

     a 		=self.scheduler_classes[0]
     a 		=self.get_scheduler_config(steps_offset=1						)
     a 		=scheduler_class(**__A						)
     scheduler.set_timesteps(5						)
     assert torch.equal(scheduler.timesteps ,    torch.LongTensor([801, 601, 401, 201, 1]						)						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] ,    [0.002, 0.02, 0.2, 2]						):
         self.check_over_configs(beta_start=__A ,    beta_end=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     self.check_over_configs(thresholding=__A						)
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(
                 thresholding=__A ,    prediction_type=__A ,    sample_max_value=__A ,    )


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     for t in [1, 10, 49]:
         self.check_over_forward(time_step=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     for t, num_inference_steps in zip([1, 10, 50] ,    [10, 50, 500]						):
         self.check_over_forward(time_step=__A ,    num_inference_steps=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     for t, eta in zip([1, 10, 49] ,    [0.0, 0.5, 1.0]						):
         self.check_over_forward(time_step=__A ,    eta=__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     a 		=self.scheduler_classes[0]
     a 		=self.get_scheduler_config()
     a 		=scheduler_class(**__A						)

     assert torch.sum(torch.abs(scheduler._get_variance(0 ,    0						) - 0.0						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(420 ,    400						) - 0.14_771						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980 ,    960						) - 0.32_460						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0 ,    0						) - 0.0						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487 ,    486						) - 0.00_979						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999 ,    998						) - 0.02						)						) < 1E-5


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     a 		=self.scheduler_classes[0]
     a 		=self.get_scheduler_config()
     a 		=scheduler_class(**__A						)

     a		,      a 		=10, 0.0
     scheduler.set_timesteps(__A						)

     a 		=self.dummy_model()
     a 		=self.dummy_sample_deter
     a 		=self.dummy_sample_deter + 0.1
     a 		=self.dummy_sample_deter - 0.1

     a 		=samplea.shape[0]
     a 		=torch.stack([samplea, samplea, samplea] ,    dim=0						)
     a 		=torch.arange(__A						)[0:3, None].repeat(1 ,    __A						)

     a 		=model(samples.flatten(0 ,    1						) ,    timesteps.flatten(0 ,    1						)						)
     a 		=scheduler.batch_step_no_noise(__A ,    timesteps.flatten(0 ,    1						) ,    samples.flatten(0 ,    1						) ,    __A						)

     a 		=torch.sum(torch.abs(__A						)						)
     a 		=torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 1_147.7_904						) < 1E-2
     assert abs(result_mean.item() - 0.4_982						) < 1E-3


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     a 		=self.full_loop()

     a 		=torch.sum(torch.abs(__A						)						)
     a 		=torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 172.0_067						) < 1E-2
     assert abs(result_mean.item() - 0.223_967						) < 1E-3


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     a 		=self.full_loop(prediction_type='''v_prediction'''						)

     a 		=torch.sum(torch.abs(__A						)						)
     a 		=torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 52.5_302						) < 1E-2
     assert abs(result_mean.item() - 0.0_684						) < 1E-3


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     # We specify different beta, so that the first alpha is 0.99
     a 		=self.full_loop(set_alpha_to_one=__A ,    beta_start=0.01						)
     a 		=torch.sum(torch.abs(__A						)						)
     a 		=torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 149.8_295						) < 1E-2
     assert abs(result_mean.item() - 0.1_951						) < 1E-3





 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     # We specify different beta, so that the first alpha is 0.99
     a 		=self.full_loop(set_alpha_to_one=__A ,    beta_start=0.01						)
     a 		=torch.sum(torch.abs(__A						)						)
     a 		=torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 149.0_784						) < 1E-2
     assert abs(result_mean.item() - 0.1_941						) < 1E-3






# 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



