


'''simple docstring'''







from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

A				:					Union[str, Any]    						=			logging.get_logger(__name__)


@add_end_docstrings(a_	)
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							str				,							*SCREAMING_SNAKE_CASE      :							Optional[Any]				,							**SCREAMING_SNAKE_CASE      :							Dict):
           super().__init__(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           requires_backends(self				,							'vision')
           self.check_model_type(
               TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING)


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE      :							Dict=None				,							SCREAMING_SNAKE_CASE      :							int=None):
           _A					:     Optional[int]     = {}
           _A					:     List[str]     = {}

           if prompt is not None:
                  _A					:     int     = prompt

           if generate_kwargs is not None:
                  _A					:     str     = generate_kwargs
           if max_new_tokens is not None:
                  if "generate_kwargs" not in forward_kwargs:
                         _A					:     Optional[Any]     = {}
                  if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                         raise ValueError(
                             '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
                             ' please use only one')
                  _A					:     Dict     = max_new_tokens
           return preprocess_params, forward_kwargs, {}


    def __call__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Union[str, List[str], "Image.Image", List["Image.Image"]]				,							**SCREAMING_SNAKE_CASE      :							Dict):
           return super().__call__(SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Tuple=None):
           _A					:     Any     = load_image(SCREAMING_SNAKE_CASE)

           if prompt is not None:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                         raise ValueError(
                             F'Received an invalid text input, got - {type(SCREAMING_SNAKE_CASE)} - but expected a single string. '
                             'Note also that one single text can be provided for conditional image to text generation.')

                  _A					:     Union[str, Any]     = self.model.config.model_type

                  if model_type == "git":
                         _A					:     Any     = self.image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors=self.framework)
                         _A					:     Dict     = self.tokenizer(text=SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE).input_ids
                         _A					:     List[Any]     = [self.tokenizer.cls_token_id] + input_ids
                         _A					:     List[Any]     = torch.tensor(SCREAMING_SNAKE_CASE).unsqueeze(0)
                         model_inputs.update({'input_ids': input_ids})

                  elif model_type == "pix2struct":
                         _A					:     Tuple     = self.image_processor(images=SCREAMING_SNAKE_CASE				,							header_text=SCREAMING_SNAKE_CASE				,							return_tensors=self.framework)

                  elif model_type != "vision-encoder-decoder":
                         # vision-encoder-decoder does not support conditional generation
                         _A					:     List[str]     = self.image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors=self.framework)
                         _A					:     Dict     = self.tokenizer(SCREAMING_SNAKE_CASE				,							return_tensors=self.framework)
                         model_inputs.update(SCREAMING_SNAKE_CASE)

                  else:
                         raise ValueError(F'Model type {model_type} does not support conditional text generation')

           else:
                  _A					:     List[str]     = self.image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors=self.framework)

           if self.model.config.model_type == "git" and prompt is None:
                  _A					:     Optional[Any]     = None

           return model_inputs


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=None):
           # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
           # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['input_ids']				,							SCREAMING_SNAKE_CASE)
               and all(x is None for x in model_inputs['input_ids'])
           ):
                  _A					:     Optional[Any]     = None

           if generate_kwargs is None:
                  _A					:     Tuple     = {}
           # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
           #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
           #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
           #  in the `_prepare_model_inputs` method.
           _A					:     int     = model_inputs.pop(self.model.main_input_name)
           _A					:     List[Any]     = self.model.generate(SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           return model_outputs


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           _A					:     Tuple     = []
           for output_ids in model_outputs:
                  _A					:     List[Any]     = {
                      'generated_text': self.tokenizer.decode(
                          SCREAMING_SNAKE_CASE				,							skip_special_tokens=SCREAMING_SNAKE_CASE				,							)
                  }
                  records.append(SCREAMING_SNAKE_CASE)
           return records










'''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






