


"""simple docstring"""




# 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	(			_lowercase		:					int					, _lowercase		:					Optional[Any]					, _lowercase		:					Dict					, _lowercase		:					Optional[int]      )				->Optional[int]:






	'''simple docstring'''






	a   :	Any			=       multiprocessing.Manager()
	a   :	int			=       manager.list()

	a   :	List[Any]			=       multiprocessing.Process(target=lowercase__					, 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	(			_lowercase		:					List[Any]					, _lowercase		:					Any					, _lowercase		:					str      )				->Any:






	'''simple docstring'''






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

		a   :	int			=       shutil.rmtree
		a   :	int			=       os.rmdir
		a   :	Optional[int]			=       os.chdir

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

		# Run program.
		try:
			a   :	int			=       {}
			with swallow_io():
				with time_limit(lowercase__      ):
					exec(lowercase__					, lowercase__      )
			result.append("passed"      )
		except TimeoutException:
			result.append("timed out"      )
		except BaseException as e:
			result.append(F"""failed: {e}"""      )

		# Needed for cleaning up.
		a   :	Tuple			=       rmtree
		a   :	List[str]			=       rmdir
		a   :	str			=       chdir






@contextlib.contextmanager
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->int:






	'''simple docstring'''






	def signal_handler(_lowercase		:					List[Any]					, _lowercase		:					List[Any]      ):
		raise TimeoutException("Timed out!"      )

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






@contextlib.contextmanager
def    _SCREAMING_SNAKE_CASE	(			)				->Any:






	'''simple docstring'''






	a   :	str			=       WriteOnlyStringIO()
	with contextlib.redirect_stdout(lowercase__      ):
		with contextlib.redirect_stderr(lowercase__      ):
			with redirect_stdin(lowercase__      ):
				yield






@contextlib.contextmanager
def    _SCREAMING_SNAKE_CASE	(			)				->int:






	'''simple docstring'''






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


class  __UpperCamelCase       (		__snake_case		):
							pass


class  __UpperCamelCase       (		io.StringIO		):






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				int:
								raise OSError






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Optional[Any]:
								raise OSError






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Optional[int]:
								raise OSError






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Optional[Any]:
								return False


class  __UpperCamelCase       (		contextlib._RedirectStream		):  # type: ignore
							lowerCamelCase     :      Any         ="""stdin"""






@contextlib.contextmanager
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->Dict:






	'''simple docstring'''






	if root == ".":
		yield
		return
	a   :	Optional[int]			=       os.getcwd()
	os.chdir(lowercase__      )
	try:
		yield
	except BaseException as exc:
		raise exc
	finally:
		os.chdir(lowercase__      )







def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]=None      )				->Union[str, 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

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

	import os

	a   :	Tuple			=       "1"

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

	import shutil

	a   :	Optional[int]			=       None
	a   :	int			=       None
	a   :	int			=       None

	import subprocess

	a   :	int			=       None  # type: ignore

	a   :	List[str]			=       None

	import sys

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









"""simple docstring"""




import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


a		:				Any  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				str:
								logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."   )
								a   :	Optional[int]			=       model
								a   :	int			=       kwargs.get("model_save_dir"   ,      lowerCAmelCase__   )
								a   :	Tuple			=       kwargs.get("latest_model_name"   ,      lowerCAmelCase__   )






							def __call__(     self   ,      **lowerCAmelCase__   )			->				Dict:
								a   :	List[str]			=       {k: np.array(lowerCAmelCase__   ) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if provider is None:
									logger.info("No onnxruntime provider specified, using CPUExecutionProvider"   )
									a   :	List[str]			=       "CPUExecutionProvider"

								return ort.InferenceSession(lowerCAmelCase__   ,      providers=[provider]   ,      sess_options=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   )			->				int:
								a   :	List[str]			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

								a   :	Optional[int]			=       self.model_save_dir.joinpath(self.latest_model_name   )
								a   :	List[str]			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
								try:
									shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
								except shutil.SameFileError:
									pass

								# copy external weights (for models >2GB)
								a   :	str			=       self.model_save_dir.joinpath(lowerCAmelCase__   )
								if src_path.exists():
									a   :	Any			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
									try:
										shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
									except shutil.SameFileError:
										pass






							def 					__a     (     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )			->				str:
								if os.path.isfile(lowerCAmelCase__   ):
									logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
									return

								os.makedirs(lowerCAmelCase__   ,      exist_ok=lowerCAmelCase__   )

								# saving model weights/files
								self._save_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Optional[int]:
								a   :	Tuple			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__   ):
									a   :	Tuple			=       OnnxRuntimeModel.load_model(
									    os.path.join(lowerCAmelCase__   ,      lowerCAmelCase__   )   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
									a   :	Tuple			=       Path(lowerCAmelCase__   )
								# load model from hub
								else:
									# download model
									a   :	Optional[Any]			=       hf_hub_download(
									    repo_id=lowerCAmelCase__   ,      filename=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      )
									a   :	Optional[int]			=       Path(lowerCAmelCase__   ).parent
									a   :	List[Any]			=       Path(lowerCAmelCase__   ).name
									a   :	int			=       OnnxRuntimeModel.load_model(lowerCAmelCase__   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
								return cls(model=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								a   :	Any			=       None
								if len(str(lowerCAmelCase__   ).split("@"   )   ) == 2:
									a, a   :	Tuple			=       model_id.split("@"   )

								return cls._from_pretrained(
								    model_id=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )


