




# 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(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			int				)    -> List[Any]:
							'''simple docstring'''


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

							__lowercase      =    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(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			Optional[int]				)    -> List[Any]:
							'''simple docstring'''


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

														__lowercase      =    shutil.rmtree
														__lowercase      =    os.rmdir
														__lowercase      =    os.chdir

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

														# Run program.
														try:
																					__lowercase      =    {}
																					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.
														__lowercase      =    rmtree
														__lowercase      =    rmdir
														__lowercase      =    chdir







@contextlib.contextmanager
def 		_A(       UpperCamelCase__							:			Union[str, Any]				)    -> Optional[int]:
							'''simple docstring'''


							def signal_handler(UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			Optional[Any]				):
														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(       )    -> Any:
							'''simple docstring'''


							__lowercase      =    WriteOnlyStringIO()
							with contextlib.redirect_stdout(UpperCamelCase__				):
														with contextlib.redirect_stderr(UpperCamelCase__				):
																					with redirect_stdin(UpperCamelCase__				):
																												yield







@contextlib.contextmanager
def 		_A(       )    -> Tuple:
							'''simple docstring'''


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







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						pass







class a (     io.StringIO  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     str				)    ->						Tuple:
													"""simple docstring"""


													raise OSError






						def     UpperCAmelCase_      ( self :     List[str]				,							*lowerCamelCase__ :     List[str]				,							**lowerCamelCase__ :     Dict				)    ->						List[Any]:
													"""simple docstring"""


													raise OSError






						def     UpperCAmelCase_      ( self :     List[str]				,							*lowerCamelCase__ :     Dict				,							**lowerCamelCase__ :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													raise OSError






						def     UpperCAmelCase_      ( self :     Tuple				,							*lowerCamelCase__ :     Optional[Any]				,							**lowerCamelCase__ :     Tuple				)    ->						Optional[Any]:
													"""simple docstring"""


													return False







class a (     contextlib._RedirectStream  ):  # type: ignore
						"""simple docstring"""

						UpperCamelCase_	:       str       	=		'stdin'







@contextlib.contextmanager
def 		_A(       UpperCamelCase__							:			int				)    -> Any:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			Optional[Any]=None				)    -> Any:
							'''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

							__lowercase      =    None
							__lowercase      =    None

							import os

							__lowercase      =    '''1'''

							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None

							import shutil

							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None

							import subprocess

							__lowercase      =    None  # type: ignore

							__lowercase      =    None

							import sys

							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None
							__lowercase      =    None












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       UNetaDModel
						UpperCamelCase_	:       KarrasVeScheduler






						def __init__( self :     Tuple				,							lowerCamelCase__ :     UNetaDModel				,							lowerCamelCase__ :     KarrasVeScheduler				)    ->						Optional[Any]:
													"""simple docstring"""


													super().__init__()
													self.register_modules(unet=lowerCamelCase__				,							scheduler=lowerCamelCase__				)






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							**lowerCamelCase__ :     int				,							)    ->						Union[Tuple, ImagePipelineOutput]:
													"""simple docstring"""


													__lowercase      =    self.unet.config.sample_size
													__lowercase      =    (batch_size, 3, img_size, img_size)

													__lowercase      =    self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													__lowercase      =    randn_tensor(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=self.device				) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(lowerCamelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				# here sigma_t == t_i from the paper
																				__lowercase      =    self.scheduler.schedule[t]
																				__lowercase      =    self.scheduler.schedule[t - 1] if t > 0 else 0

																				# 1. Select temporarily increased noise level sigma_hat
																				# 2. Add new noise to move from sample_i to sample_hat
																				__lowercase     ,     __lowercase      =    self.scheduler.add_noise_to_input(lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				)

																				# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																				# The model inputs and output are adjusted by following eq. (213) in [1].
																				__lowercase      =    (sigma_hat / 2) * model((sample_hat + 1) / 2				,							sigma_hat / 2				).sample

																				# 4. Evaluate dx/dt at sigma_hat
																				# 5. Take Euler step from sigma to sigma_prev
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

																				if sigma_prev != 0:
																											# 6. Apply 2nd order correction
																											# The model inputs and output are adjusted by following eq. (213) in [1].
																											__lowercase      =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,							sigma_prev / 2				).sample
																											__lowercase      =    self.scheduler.step_correct(
																											    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							step_output.prev_sample				,							step_output['''derivative''']				,							)
																				__lowercase      =    step_output.prev_sample

													__lowercase      =    (sample / 2 + 0.5).clamp(0				,							1				)
													__lowercase      =    sample.cpu().permute(0				,							2				,							3				,							1				).numpy()
													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)






