

import os
import sys

_lowerCamelCase    :       str        =							os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_lowerCamelCase    :       Union[str, Any]        =							[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]

@add_start_docstrings(AutoConfig.__doc__      )
def       a__					(		*UpperCAmelCase							:      Tuple    ,			**UpperCAmelCase							:      Optional[Any]      )  ->						Union[str, Any]:
  return AutoConfig.from_pretrained(*UpperCAmelCase    ,			**UpperCAmelCase      )

@add_start_docstrings(AutoTokenizer.__doc__      )
def       a__					(		*UpperCAmelCase							:      Union[str, Any]    ,			**UpperCAmelCase							:      Tuple      )  ->						Optional[Any]:
  return AutoTokenizer.from_pretrained(*UpperCAmelCase    ,			**UpperCAmelCase      )

@add_start_docstrings(AutoModel.__doc__      )
def       a__					(		*UpperCAmelCase							:      Optional[int]    ,			**UpperCAmelCase							:      Any      )  ->						Union[str, Any]:
  return AutoModel.from_pretrained(*UpperCAmelCase    ,			**UpperCAmelCase      )

@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def       a__					(		*UpperCAmelCase							:      Union[str, Any]    ,			**UpperCAmelCase							:      Optional[int]      )  ->						Dict:
  return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase    ,			**UpperCAmelCase      )

@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def       a__					(		*UpperCAmelCase							:      Dict    ,			**UpperCAmelCase							:      Tuple      )  ->						List[Any]:
  return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase    ,			**UpperCAmelCase      )

@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def       a__					(		*UpperCAmelCase							:      Union[str, Any]    ,			**UpperCAmelCase							:      Optional[Any]      )  ->						int:
  return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase    ,			**UpperCAmelCase      )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def       a__					(		*UpperCAmelCase							:      Dict    ,			**UpperCAmelCase							:      Tuple      )  ->						Tuple:
  return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase    ,			**UpperCAmelCase      )








from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_lowerCamelCase    :       Dict        =							logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__						)
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Optional[Any],		*__A							:					Tuple,		**__A							:					Tuple						):
     super().__init__(*__A,		**__A						)
     self.check_model_type(__A						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					int=None,		__A							:					Tuple=None,		__A							:					Any=None,		**__A							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			{}, {}
     if padding is not None:
       UpperCAmelCase	:							Optional[int]						=			padding
     if truncation is not None:
       UpperCAmelCase	:							Optional[int]						=			truncation
     if top_k is not None:
       UpperCAmelCase	:							Tuple						=			top_k
     return preprocess_params, {}, postprocess_params



   def __call__(							self							:					Union[str, Any],		__A							:					Union["Image.Image", str],		__A							:					str = None,		**__A							:					Optional[int]						):
     if isinstance(__A,		(Image.Image, str)						) and isinstance(__A,		__A						):
       UpperCAmelCase	:							int						=			{'''image''': image, '''question''': question}
     else:
       UpperCAmelCase	:							str						=			image
     UpperCAmelCase	:							Union[str, Any]						=			super().__call__(__A,		**__A						)
     return results



   def 	__magic_name__   (							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=False,		__A							:					List[Any]=False						):
     UpperCAmelCase	:							int						=			load_image(inputs['''image''']						)
     UpperCAmelCase	:							List[str]						=			self.tokenizer(
         inputs['''question'''],		return_tensors=self.framework,		padding=__A,		truncation=__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processor(images=__A,		return_tensors=self.framework						)
     model_inputs.update(__A						)
     return model_inputs



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any]						):
     UpperCAmelCase	:							Optional[int]						=			self.model(**__A						)
     return model_outputs





   def 	__magic_name__   (							self							:					Any,		__A							:					List[str],		__A							:					Union[str, Any]=5						):
     if top_k > self.model.config.num_labels:
       UpperCAmelCase	:							Any						=			self.model.config.num_labels

     if self.framework == "pt":
       UpperCAmelCase	:							Any						=			model_outputs.logits.sigmoid()[0]
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			probs.topk(__A						)
     else:
       raise ValueError(F'''Unsupported framework: {self.framework}'''						)

     UpperCAmelCase	:							str						=			scores.tolist()
     UpperCAmelCase	:							Tuple						=			ids.tolist()
     return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__A,		__A						)]





