


# 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       a_ (       SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Optional[int]	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =multiprocessing.Manager()
       _lowerCamelCase			:						Dict            =manager.list()

       _lowerCamelCase			:						List[Any]            =multiprocessing.Process(target=UpperCAmelCase_						,     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       a_ (       SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : Any	):
       '''simple docstring'''





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

              _lowerCamelCase			:						Optional[Any]            =shutil.rmtree
              _lowerCamelCase			:						int            =os.rmdir
              _lowerCamelCase			:						List[Any]            =os.chdir

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

              # Run program.
              try:
                     _lowerCamelCase			:						List[str]            ={}
                     with swallow_io():
                            with time_limit(UpperCAmelCase_	):
                                   exec(UpperCAmelCase_						,     UpperCAmelCase_	)
                     result.append('passed'	)
              except TimeoutException:
                     result.append('timed out'	)
              except BaseException as e:
                     result.append(F'''failed: {e}'''	)

              # Needed for cleaning up.
              _lowerCamelCase			:						Union[str, Any]            =rmtree
              _lowerCamelCase			:						str            =rmdir
              _lowerCamelCase			:						Optional[int]            =chdir




@contextlib.contextmanager
def       a_ (       SCREAMING_SNAKE_CASE__   : Dict	):
       '''simple docstring'''





       def signal_handler(SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Tuple	):
              raise TimeoutException('Timed out!'	)

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




@contextlib.contextmanager
def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =WriteOnlyStringIO()
       with contextlib.redirect_stdout(UpperCAmelCase_	):
              with contextlib.redirect_stderr(UpperCAmelCase_	):
                     with redirect_stdin(UpperCAmelCase_	):
                            yield




@contextlib.contextmanager
def       a_ (       ):
       '''simple docstring'''





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




class A    (  UpperCamelCase_	):
      pass




class A    (  io.StringIO	):






      def 			lowerCamelCase			(   self		:	List[str]     ,  *lowercase_		:	List[Any]     ,  **lowercase_		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             raise OSError






      def 			lowerCamelCase			(   self		:	Any     ,  *lowercase_		:	Tuple     ,  **lowercase_		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             raise OSError






      def 			lowerCamelCase			(   self		:	List[Any]     ,  *lowercase_		:	str     ,  **lowercase_		:	Tuple					)   ->		Optional[int]:
             """simple docstring"""




             raise OSError






      def 			lowerCamelCase			(   self		:	str     ,  *lowercase_		:	Any     ,  **lowercase_		:	int					)   ->		Union[str, Any]:
             """simple docstring"""




             return False




class A    (  contextlib._RedirectStream	):  # type: ignore
      UpperCamelCase__				:    Tuple ='stdin'




@contextlib.contextmanager
def       a_ (       SCREAMING_SNAKE_CASE__   : List[Any]	):
       '''simple docstring'''





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




def       a_ (       SCREAMING_SNAKE_CASE__   : Tuple=None	):
       '''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

       _lowerCamelCase			:						List[str]            =None
       _lowerCamelCase			:						List[str]            =None

       import os

       _lowerCamelCase			:						List[Any]            ='''1'''

       _lowerCamelCase			:						Dict            =None
       _lowerCamelCase			:						Dict            =None
       _lowerCamelCase			:						Dict            =None
       _lowerCamelCase			:						str            =None
       _lowerCamelCase			:						Optional[int]            =None
       _lowerCamelCase			:						List[Any]            =None
       _lowerCamelCase			:						Tuple            =None
       _lowerCamelCase			:						Optional[Any]            =None
       _lowerCamelCase			:						Tuple            =None
       _lowerCamelCase			:						Any            =None
       _lowerCamelCase			:						Tuple            =None
       _lowerCamelCase			:						Tuple            =None
       _lowerCamelCase			:						Dict            =None
       _lowerCamelCase			:						int            =None
       _lowerCamelCase			:						str            =None
       _lowerCamelCase			:						Any            =None
       _lowerCamelCase			:						int            =None
       _lowerCamelCase			:						Optional[Any]            =None
       _lowerCamelCase			:						Union[str, Any]            =None
       _lowerCamelCase			:						Optional[Any]            =None
       _lowerCamelCase			:						Dict            =None
       _lowerCamelCase			:						List[str]            =None
       _lowerCamelCase			:						List[str]            =None
       _lowerCamelCase			:						Any            =None
       _lowerCamelCase			:						List[str]            =None
       _lowerCamelCase			:						Tuple            =None
       _lowerCamelCase			:						Tuple            =None

       import shutil

       _lowerCamelCase			:						str            =None
       _lowerCamelCase			:						str            =None
       _lowerCamelCase			:						Tuple            =None

       import subprocess

       _lowerCamelCase			:						Any            =None  # type: ignore

       _lowerCamelCase			:						List[str]            =None

       import sys

       _lowerCamelCase			:						List[str]            =None
       _lowerCamelCase			:						Tuple            =None
       _lowerCamelCase			:						Dict            =None
       _lowerCamelCase			:						List[Any]            =None
       _lowerCamelCase			:						Any            =None




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DDIMParallelScheduler,)
       __lowerCamelCase				       =						(("eta", 0.0), ("num_inference_steps", 5_0))


       def 	_snake_case				(    self						,       **__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				str		=self.get_scheduler_config(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=10, 0.0

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_sample_deter

              scheduler.set_timesteps(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.step(__A						,       __A						,       __A						,       __A				).prev_sample

              return sample


       def 	_snake_case				(    self				)						->    Optional[Any]:
              for timesteps in [100, 500, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=__A				)

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


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


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


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


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


       def 	_snake_case				(    self				)						->    Tuple:
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=__A				)


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


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


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=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 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				str		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=10, 0.0
              scheduler.set_timesteps(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				str		=self.dummy_sample_deter
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter + 0.1
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_sample_deter - 0.1

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=samplea.shape[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.stack([samplea, samplea, samplea]						,       dim=0				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.arange(__A				)[0:3, None].repeat(1						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=model(samples.flatten(0						,       1				)						,       timesteps.flatten(0						,       1				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=scheduler.batch_step_no_noise(__A						,       timesteps.flatten(0						,       1				)						,       samples.flatten(0						,       1				)						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=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 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.full_loop(prediction_type='''v_prediction'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				str		=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 	_snake_case				(    self				)						->    Dict:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=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 	_snake_case				(    self				)						->    Optional[Any]:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=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
