






# 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      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):
				__magic_name__		:List[Any]        =      multiprocessing.Manager()
				__magic_name__		:Union[str, Any]        =      manager.list()

				__magic_name__		:Dict        =      multiprocessing.Process(target=snake_case,  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      __lowercase					( snake_case,  snake_case,  snake_case ):






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

								__magic_name__		:List[str]        =      shutil.rmtree
								__magic_name__		:Any        =      os.rmdir
								__magic_name__		:Optional[Any]        =      os.chdir

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

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

								# Needed for cleaning up.
								__magic_name__		:int        =      rmtree
								__magic_name__		:Optional[int]        =      rmdir
								__magic_name__		:List[str]        =      chdir

@contextlib.contextmanager
def      __lowercase					( snake_case ):






				def signal_handler(snake_case,  snake_case ):
								raise TimeoutException('''Timed out!''' )

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

@contextlib.contextmanager
def      __lowercase					( ):
				__magic_name__		:Union[str, Any]        =      WriteOnlyStringIO()
				with contextlib.redirect_stdout(snake_case ):
								with contextlib.redirect_stderr(snake_case ):
												with redirect_stdin(snake_case ):
																yield

@contextlib.contextmanager
def      __lowercase					( ):






				with tempfile.TemporaryDirectory() as dirname:
								with chdir(snake_case ):
												yield dirname
class 						lowerCamelCase_				(      lowerCamelCase ):
				pass







class 						lowerCamelCase_				(      io.StringIO ):







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								raise OSError







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								raise OSError







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								raise OSError







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return False







class 						lowerCamelCase_				(      contextlib._RedirectStream ):  # type: ignore
				a__					       =  '''stdin'''







@contextlib.contextmanager
def      __lowercase					( snake_case ):






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

def      __lowercase					( snake_case=None ):






				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

				__magic_name__		:Dict        =      None
				__magic_name__		:str        =      None

				import os

				__magic_name__		:Tuple        =      '''1'''

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

				import shutil

				__magic_name__		:str        =      None
				__magic_name__		:Any        =      None
				__magic_name__		:List[Any]        =      None

				import subprocess

				__magic_name__		:Union[str, Any]        =      None  # type: ignore

				__magic_name__		:Union[str, Any]        =      None

				import sys

				__magic_name__		:List[str]        =      None
				__magic_name__		:List[Any]        =      None
				__magic_name__		:str        =      None
				__magic_name__		:Optional[int]        =      None
				__magic_name__		:str        =      None














# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class 						lowerCamelCase_				:
				a__					       =  42

				# setable values
				a__					       =  42
				a__					       =  42
				a__					       =  None







				@classmethod
				def 			A	(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return cls(common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase     )







@dataclass
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  42







class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase ):
				a__					       =  [e.name for e in FlaxKarrasDiffusionSchedulers]

				a__					       =  42







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return True







				@register_to_config
				def __init__(						self       ,					__lowerCAmelCase = 1_0_0_0       ,					__lowerCAmelCase = 0.0001       ,					__lowerCAmelCase = 0.02       ,					__lowerCAmelCase = "linear"       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = "fixed_small"       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = "epsilon"       ,					__lowerCAmelCase = jnp.floataa       ,					):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      dtype







				def 			A	(						self       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								if common is None:
												__magic_name__		:Dict        =      CommonSchedulerState.create(self     )

								# standard deviation of the initial noise distribution
								__magic_name__		:Optional[Any]        =      jnp.array(1.0       ,					dtype=self.dtype     )

								__magic_name__		:str        =      jnp.arange(0       ,					self.config.num_train_timesteps     ).round()[::-1]

								return DDPMSchedulerState.create(
								    common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								return sample







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = ()     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.config.num_train_timesteps // num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# rounding to avoid issues when num_inference_step is power of 3
								__magic_name__		:List[Any]        =      (jnp.arange(0       ,					__lowerCAmelCase     ) * step_ratio).round()[::-1]

								return state.replace(
								    num_inference_steps=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      state.common.alphas_cumprod[t]
								__magic_name__		:Optional[Any]        =      jnp.where(t > 0       ,					state.common.alphas_cumprod[t - 1]       ,					jnp.array(1.0       ,					dtype=self.dtype     )     )

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
								# and sample from it to get previous sample
								# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
								__magic_name__		:Tuple        =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

								if variance_type is None:
												__magic_name__		:Optional[Any]        =      self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small":
												__magic_name__		:Optional[Any]        =      jnp.clip(__lowerCAmelCase       ,					a_min=1E-20     )
								# for rl-diffuser https://arxiv.org/abs/2205.09991
								elif variance_type == "fixed_small_log":
												__magic_name__		:Dict        =      jnp.log(jnp.clip(__lowerCAmelCase       ,					a_min=1E-20     )     )
								elif variance_type == "fixed_large":
												__magic_name__		:Tuple        =      state.common.betas[t]
								elif variance_type == "fixed_large_log":
												# Glide max_log
												__magic_name__		:Optional[Any]        =      jnp.log(state.common.betas[t]     )
								elif variance_type == "learned":
												return predicted_variance
								elif variance_type == "learned_range":
												__magic_name__		:Union[str, Any]        =      variance
												__magic_name__		:List[str]        =      state.common.betas[t]
												__magic_name__		:Any        =      (predicted_variance + 1) / 2
												__magic_name__		:str        =      frac * max_log + (1 - frac) * min_log

								return variance







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = True       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      timestep

								if key is None:
												__magic_name__		:Union[str, Any]        =      jax.random.PRNGKey(0     )

								if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
												__magic_name__     ,				__magic_name__		:Dict        =      jnp.split(__lowerCAmelCase       ,					sample.shape[1]       ,					axis=1     )
								else:
												__magic_name__		:Optional[int]        =      None

								# 1. compute alphas, betas
								__magic_name__		:Any        =      state.common.alphas_cumprod[t]
								__magic_name__		:int        =      jnp.where(t > 0       ,					state.common.alphas_cumprod[t - 1]       ,					jnp.array(1.0       ,					dtype=self.dtype     )     )
								__magic_name__		:Optional[int]        =      1 - alpha_prod_t
								__magic_name__		:Union[str, Any]        =      1 - alpha_prod_t_prev

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								if self.config.prediction_type == "epsilon":
												__magic_name__		:List[Any]        =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
												__magic_name__		:Tuple        =      model_output
								elif self.config.prediction_type == "v_prediction":
												__magic_name__		:Dict        =      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
								else:
												raise ValueError(
												    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
												    ''' for the FlaxDDPMScheduler.'''     )

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
												__magic_name__		:Union[str, Any]        =      jnp.clip(__lowerCAmelCase       ,					-1       ,					1     )

								# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__magic_name__		:Union[str, Any]        =      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
								__magic_name__		:Union[str, Any]        =      state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

								# 5. Compute predicted previous sample µ_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__magic_name__		:Union[str, Any]        =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								def random_variance():
												__magic_name__		:Tuple        =      jax.random.split(__lowerCAmelCase       ,					num=1     )
												__magic_name__		:Dict        =      jax.random.normal(__lowerCAmelCase       ,					shape=model_output.shape       ,					dtype=self.dtype     )
												return (self._get_variance(__lowerCAmelCase       ,					__lowerCAmelCase       ,					predicted_variance=__lowerCAmelCase     ) ** 0.5) * noise

								__magic_name__		:List[str]        =      jnp.where(t > 0       ,					random_variance()       ,					jnp.zeros(model_output.shape       ,					dtype=self.dtype     )     )

								__magic_name__		:int        =      pred_prev_sample + variance

								if not return_dict:
												return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase       ,					state=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return add_noise_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return get_velocity_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def __len__(						self     ):
								"""simple docstring"""
								return self.config.num_train_timesteps






