




"""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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Any    =    multiprocessing.Manager()
				_lowerCamelCase     :			str    =    manager.list()

				_lowerCamelCase     :			int    =    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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				with create_tempdir():
								# These system calls are needed when cleaning up tempdir.
								import os
								import shutil

								_lowerCamelCase     :			Optional[Any]    =    shutil.rmtree
								_lowerCamelCase     :			Dict    =    os.rmdir
								_lowerCamelCase     :			int    =    os.chdir

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

								# Run program.
								try:
												_lowerCamelCase     :			Dict    =    {}
												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.
								_lowerCamelCase     :			int    =    rmtree
								_lowerCamelCase     :			Tuple    =    rmdir
								_lowerCamelCase     :			List[Any]    =    chdir







@contextlib.contextmanager
def      _snake_case			(				lowercase__		):
				def signal_handler(lowercase__							,    lowercase__		):
								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      _snake_case			(				):
				_lowerCamelCase     :			Tuple    =    WriteOnlyStringIO()
				with contextlib.redirect_stdout(lowercase__		):
								with contextlib.redirect_stderr(lowercase__		):
												with redirect_stdin(lowercase__		):
																yield







@contextlib.contextmanager
def      _snake_case			(				):
				with tempfile.TemporaryDirectory() as dirname:
								with chdir(lowercase__		):
												yield dirname






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			pass






class lowerCAmelCase__	(     io.StringIO						):






			'''simple docstring'''



			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							raise OSError
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							raise OSError
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							raise OSError






			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return False






class lowerCAmelCase__	(     contextlib._RedirectStream						):  # type: ignore






			'''simple docstring'''



			lowerCamelCase__      =			"""stdin"""







@contextlib.contextmanager
def      _snake_case			(				lowercase__		):
				if root == ".":
								yield
								return
				_lowerCamelCase     :			List[str]    =    os.getcwd()
				os.chdir(lowercase__		)
				try:
								yield
				except BaseException as exc:
								raise exc
				finally:
								os.chdir(lowercase__		)







def      _snake_case			(				lowercase__=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

				_lowerCamelCase     :			Dict    =    None
				_lowerCamelCase     :			Any    =    None

				import os

				_lowerCamelCase     :			Optional[Any]    =    '1'

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

				import shutil

				_lowerCamelCase     :			str    =    None
				_lowerCamelCase     :			int    =    None
				_lowerCamelCase     :			Any    =    None

				import subprocess

				_lowerCamelCase     :			List[Any]    =    None  # type: ignore

				_lowerCamelCase     :			Union[str, Any]    =    None

				import sys

				_lowerCamelCase     :			Optional[int]    =    None
				_lowerCamelCase     :			Dict    =    None
				_lowerCamelCase     :			str    =    None
				_lowerCamelCase     :			List[Any]    =    None
				_lowerCamelCase     :			int    =    None





"""simple docstring"""






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""image_processor""", """tokenizer"""]
			lowerCamelCase__      =			"""BlipImageProcessor"""
			lowerCamelCase__      =			"""AutoTokenizer"""
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							super().__init__(lowercase  ,      lowercase  )

							# add QFormer tokenizer
							_lowerCamelCase     :			int    =    qformer_tokenizer
			def __call__(  self  ,      lowercase = None  ,      lowercase = None  ,      lowercase = True  ,      lowercase = False  ,      lowercase = None  ,      lowercase = None  ,      lowercase = 0  ,      lowercase = None  ,      lowercase = None  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      **lowercase  ,      ):
							if images is None and text is None:
											raise ValueError('You have to specify at least images or text.'  )

							_lowerCamelCase     :			int    =    BatchFeature()

							if text is not None:
											_lowerCamelCase     :			List[str]    =    self.tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											encoding.update(lowercase  )
											_lowerCamelCase     :			List[str]    =    self.qformer_tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											_lowerCamelCase     :			List[Any]    =    qformer_text_encoding.pop('input_ids'  )
											_lowerCamelCase     :			Tuple    =    qformer_text_encoding.pop('attention_mask'  )

							if images is not None:
											_lowerCamelCase     :			int    =    self.image_processor(lowercase  ,      return_tensors=lowercase  )
											encoding.update(lowercase  )

							return encoding
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.batch_decode(*lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.decode(*lowercase  ,      **lowercase  )
			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer.model_input_names
							_lowerCamelCase     :			Any    =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
			def 					A_    (  self  ,      lowercase  ,      **lowercase  ):
							if os.path.isfile(lowercase  ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''  )
							os.makedirs(lowercase  ,      exist_ok=lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    os.path.join(lowercase  ,      'qformer_tokenizer'  )
							self.qformer_tokenizer.save_pretrained(lowercase  )
							return super().save_pretrained(lowercase  ,      **lowercase  )






			@classmethod
			def 					A_    (  cls  ,      lowercase  ,      **lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    AutoTokenizer.from_pretrained(lowercase  ,      subfolder='qformer_tokenizer'  )
							_lowerCamelCase     :			Dict    =    cls._get_arguments_from_pretrained(lowercase  ,      **lowercase  )
							args.append(lowercase  )
							return cls(*lowercase  )