

# 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							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       Union[str, Any]:
				_UpperCAmelCase       :		List[str] 		=    multiprocessing.Manager()
				_UpperCAmelCase       :		Optional[Any] 		=    manager.list()

				_UpperCAmelCase       :		int 		=    multiprocessing.Process(target=lowerCAmelCase  ,      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							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  str )   ->       Tuple:
				with create_tempdir():
								# These system calls are needed when cleaning up tempdir.
								import os
								import shutil

								_UpperCAmelCase       :		Dict 		=    shutil.rmtree
								_UpperCAmelCase       :		Tuple 		=    os.rmdir
								_UpperCAmelCase       :		int 		=    os.chdir

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

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

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

@contextlib.contextmanager
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       List[str]:
				def signal_handler(lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Union[str, Any] ):
								raise TimeoutException("Timed out!" )

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

@contextlib.contextmanager
def 							__SCREAMING_SNAKE_CASE							(				)   ->       Optional[Any]:
				_UpperCAmelCase       :		List[str] 		=    WriteOnlyStringIO()
				with contextlib.redirect_stdout(lowerCAmelCase ):
								with contextlib.redirect_stderr(lowerCAmelCase ):
												with redirect_stdin(lowerCAmelCase ):
																yield

@contextlib.contextmanager
def 							__SCREAMING_SNAKE_CASE							(				)   ->       Optional[int]:
				with tempfile.TemporaryDirectory() as dirname:
								with chdir(lowerCAmelCase ):
												yield dirname

class 				a  (	UpperCAmelCase				):
						pass







class 				a  (	io.StringIO				):


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										raise OSError


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										raise OSError


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										raise OSError





						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										return False







class 				a  (	contextlib._RedirectStream				):  # type: ignore
						_lowercase    						=			"stdin"







@contextlib.contextmanager
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any] )   ->       int:
				if root == ".":
								yield
								return
				_UpperCAmelCase       :		Union[str, Any] 		=    os.getcwd()
				os.chdir(lowerCAmelCase )
				try:
								yield
				except BaseException as exc:
								raise exc
				finally:
								os.chdir(lowerCAmelCase )





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]=None )   ->       Union[str, 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

				_UpperCAmelCase       :		Union[str, Any] 		=    None
				_UpperCAmelCase       :		Tuple 		=    None

				import os

				_UpperCAmelCase       :		Optional[int] 		=    "1"

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

				import shutil

				_UpperCAmelCase       :		Optional[int] 		=    None
				_UpperCAmelCase       :		Any 		=    None
				_UpperCAmelCase       :		Optional[Any] 		=    None

				import subprocess

				_UpperCAmelCase       :		Dict 		=    None  # type: ignore

				_UpperCAmelCase       :		Optional[Any] 		=    None

				import sys

				_UpperCAmelCase       :		List[str] 		=    None
				_UpperCAmelCase       :		Optional[Any] 		=    None
				_UpperCAmelCase       :		str 		=    None
				_UpperCAmelCase       :		str 		=    None
				_UpperCAmelCase       :		Tuple 		=    None





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity


