


'''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			(	a_	):
    """simple docstring"""
    a													=   ["image_processor", "tokenizer"]
    a													=   "BlipImageProcessor"
    a													=   "AutoTokenizer"


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							int):
           super().__init__(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           # add QFormer tokenizer
           _A					:     Optional[int]     = qformer_tokenizer


    def __call__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							ImageInput = None				,							SCREAMING_SNAKE_CASE      :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							Union[bool, str, PaddingStrategy] = False				,							SCREAMING_SNAKE_CASE      :							Union[bool, str, TruncationStrategy] = None				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None				,							SCREAMING_SNAKE_CASE      :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							Optional[Union[str, TensorType]] = None				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							):
           if images is None and text is None:
                  raise ValueError('You have to specify at least images or text.')

           _A					:     Union[str, Any]     = BatchFeature()

           if text is not None:
                  _A					:     Optional[Any]     = self.tokenizer(
                      text=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE				,							padding=SCREAMING_SNAKE_CASE				,							truncation=SCREAMING_SNAKE_CASE				,							max_length=SCREAMING_SNAKE_CASE				,							stride=SCREAMING_SNAKE_CASE				,							pad_to_multiple_of=SCREAMING_SNAKE_CASE				,							return_attention_mask=SCREAMING_SNAKE_CASE				,							return_overflowing_tokens=SCREAMING_SNAKE_CASE				,							return_special_tokens_mask=SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							return_token_type_ids=SCREAMING_SNAKE_CASE				,							return_length=SCREAMING_SNAKE_CASE				,							verbose=SCREAMING_SNAKE_CASE				,							return_tensors=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE				,							)
                  encoding.update(SCREAMING_SNAKE_CASE)
                  _A					:     Optional[Any]     = self.qformer_tokenizer(
                      text=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE				,							padding=SCREAMING_SNAKE_CASE				,							truncation=SCREAMING_SNAKE_CASE				,							max_length=SCREAMING_SNAKE_CASE				,							stride=SCREAMING_SNAKE_CASE				,							pad_to_multiple_of=SCREAMING_SNAKE_CASE				,							return_attention_mask=SCREAMING_SNAKE_CASE				,							return_overflowing_tokens=SCREAMING_SNAKE_CASE				,							return_special_tokens_mask=SCREAMING_SNAKE_CASE				,							return_offsets_mapping=SCREAMING_SNAKE_CASE				,							return_token_type_ids=SCREAMING_SNAKE_CASE				,							return_length=SCREAMING_SNAKE_CASE				,							verbose=SCREAMING_SNAKE_CASE				,							return_tensors=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE				,							)
                  _A					:     Dict     = qformer_text_encoding.pop('input_ids')
                  _A					:     Optional[int]     = qformer_text_encoding.pop('attention_mask')

           if images is not None:
                  _A					:     str     = self.image_processor(SCREAMING_SNAKE_CASE				,							return_tensors=SCREAMING_SNAKE_CASE)
                  encoding.update(SCREAMING_SNAKE_CASE)

           return encoding


    def        A   (  self      :							Optional[Any]				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							int):
           return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							Optional[int]):
           return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)


    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def        A   (  self      :							List[str]):
           _A					:     Union[str, Any]     = self.tokenizer.model_input_names
           _A					:     List[Any]     = self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							**SCREAMING_SNAKE_CASE      :							List[str]):
           if os.path.isfile(SCREAMING_SNAKE_CASE):
                  raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(SCREAMING_SNAKE_CASE				,							exist_ok=SCREAMING_SNAKE_CASE)
           _A					:     Optional[int]     = os.path.join(SCREAMING_SNAKE_CASE				,							'qformer_tokenizer')
           self.qformer_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE)
           return super().save_pretrained(SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)


    @classmethod
    def        A   (  cls      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Optional[int]     = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				,							subfolder='qformer_tokenizer')
           _A					:     List[Any]     = cls._get_arguments_from_pretrained(SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           args.append(SCREAMING_SNAKE_CASE)
           return cls(*SCREAMING_SNAKE_CASE)










'''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 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = multiprocessing.Manager()
       _A					:     List[Any]     = manager.list()

       _A					:     Dict     = 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 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[Any]       ):
       with create_tempdir():
              # These system calls are needed when cleaning up tempdir.
              import os
              import shutil

              _A					:     Any     = shutil.rmtree
              _A					:     Optional[int]     = os.rmdir
              _A					:     str     = os.chdir

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

              # Run program.
              try:
                     _A					:     str     = {}
                     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.
              _A					:     Optional[int]     = rmtree
              _A					:     Optional[Any]     = rmdir
              _A					:     Dict     = chdir






@contextlib.contextmanager
def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       def signal_handler(lowerCamelCase					:      str  ,lowerCamelCase					:      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 				lowerCAmelCase__	(    ):
       _A					:     Any     = WriteOnlyStringIO()
       with contextlib.redirect_stdout(lowerCamelCase       ):
              with contextlib.redirect_stderr(lowerCamelCase       ):
                     with redirect_stdin(lowerCamelCase       ):
                            yield






@contextlib.contextmanager
def 				lowerCAmelCase__	(    ):
       with tempfile.TemporaryDirectory() as dirname:
              with chdir(lowerCamelCase       ):
                     yield dirname


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    pass




class 			__lowerCamelCase			(	io.StringIO	):
    """simple docstring"""


    def        A   (  self      :							Tuple				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							Dict):
           raise OSError


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           raise OSError


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							str				,							**SCREAMING_SNAKE_CASE      :							Optional[int]):
           raise OSError


    def        A   (  self      :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           return False




class 			__lowerCamelCase			(	contextlib._RedirectStream	):  # type: ignore
    """simple docstring"""
    a													=   "stdin"






@contextlib.contextmanager
def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple       ):
       if root == ".":
              yield
              return
       _A					:     Any     = os.getcwd()
       os.chdir(lowerCamelCase       )
       try:
              yield
       except BaseException as exc:
              raise exc
       finally:
              os.chdir(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]=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

       _A					:     List[Any]     = None
       _A					:     Dict     = None

       import os

       _A					:     Union[str, Any]     = '1'

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

       import shutil

       _A					:     int     = None
       _A					:     Any     = None
       _A					:     List[Any]     = None

       import subprocess

       _A					:     Optional[Any]     = None  # type: ignore

       _A					:     List[Any]     = None

       import sys

       _A					:     Any     = None
       _A					:     Tuple     = None
       _A					:     str     = None
       _A					:     Tuple     = None
       _A					:     List[str]     = None






