
# 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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Optional[int]				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				List[Any]		=multiprocessing.Manager()
       SCREAMING_SNAKE_CASE_							:				Dict		=manager.list()

       SCREAMING_SNAKE_CASE_							:				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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     Any				)			->						List[str]:
       with create_tempdir():
              # These system calls are needed when cleaning up tempdir.
              import os
              import shutil

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

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

              # Run program.
              try:
                     SCREAMING_SNAKE_CASE_							:				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.
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=rmtree
              SCREAMING_SNAKE_CASE_							:				str		=rmdir
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=chdir
@contextlib.contextmanager
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict				)			->						int:
       def signal_handler(UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     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 							SCREAMING_SNAKE_CASE_	(					)			->						int:
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=WriteOnlyStringIO()
       with contextlib.redirect_stdout(UpperCAmelCase_				):
              with contextlib.redirect_stderr(UpperCAmelCase_				):
                     with redirect_stdin(UpperCAmelCase_				):
                            yield
@contextlib.contextmanager
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[Any]:
       with tempfile.TemporaryDirectory() as dirname:
              with chdir(UpperCAmelCase_				):
                     yield dirname


class 			lowercase_		(      A       ):
       pass






class 			lowercase_		(      io.StringIO       ):


       def 	_snake_case				(    self						,       *__A						,       **__A				)						->    Dict:
              raise OSError


       def 	_snake_case				(    self						,       *__A						,       **__A				)						->    int:
              raise OSError


       def 	_snake_case				(    self						,       *__A						,       **__A				)						->    Optional[int]:
              raise OSError





       def 	_snake_case				(    self						,       *__A						,       **__A				)						->    Union[str, Any]:
              return False






class 			lowercase_		(      contextlib._RedirectStream       ):  # type: ignore
       __lowerCamelCase				       =						"stdin"






@contextlib.contextmanager
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[Any]				)			->						Optional[int]:
       if root == ".":
              yield
              return
       SCREAMING_SNAKE_CASE_							:				Tuple		=os.getcwd()
       os.chdir(UpperCAmelCase_				)
       try:
              yield
       except BaseException as exc:
              raise exc
       finally:
              os.chdir(UpperCAmelCase_				)

def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple=None				)			->						List[Any]:

       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

       SCREAMING_SNAKE_CASE_							:				List[str]		=None
       SCREAMING_SNAKE_CASE_							:				List[str]		=None

       import os

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

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

       import shutil

       SCREAMING_SNAKE_CASE_							:				str		=None
       SCREAMING_SNAKE_CASE_							:				str		=None
       SCREAMING_SNAKE_CASE_							:				Tuple		=None

       import subprocess

       SCREAMING_SNAKE_CASE_							:				Any		=None  # type: ignore

       SCREAMING_SNAKE_CASE_							:				List[str]		=None

       import sys

       SCREAMING_SNAKE_CASE_							:				List[str]		=None
       SCREAMING_SNAKE_CASE_							:				Tuple		=None
       SCREAMING_SNAKE_CASE_							:				Dict		=None
       SCREAMING_SNAKE_CASE_							:				List[Any]		=None
       SCREAMING_SNAKE_CASE_							:				Any		=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 			lowercase_		(      A       ):
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42


       def __init__(    self						,       __A						,       __A				)						->    List[str]:
              super().__init__()
              self.register_modules(unet=__A						,       scheduler=__A				)





       @torch.no_grad()
       def __call__(    self						,       __A = 1						,       __A = 50						,       __A = None						,       __A = "pil"						,       __A = True						,       **__A						,       )						->    Union[Tuple, ImagePipelineOutput]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.unet.config.sample_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=(batch_size, 3, img_size, img_size)

              SCREAMING_SNAKE_CASE_							:				int		=self.unet

              # sample x_0 ~ N(0, sigma_0^2 * I)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(__A						,       generator=__A						,       device=self.device				) * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(__A				)

              for t in self.progress_bar(self.scheduler.timesteps				):
                     # here sigma_t == t_i from the paper
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler.schedule[t]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=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
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.add_noise_to_input(__A						,       __A						,       generator=__A				)

                     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     SCREAMING_SNAKE_CASE_							:				Tuple		=(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
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.step(__A						,       __A						,       __A						,       __A				)

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

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(sample / 2 + 0.5).clamp(0						,       1				)
              SCREAMING_SNAKE_CASE_							:				Any		=sample.cpu().permute(0						,       2						,       3						,       1				).numpy()
              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)
