
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

lowercase__          =logging.get_logger(__name__)



@add_end_docstrings(__lowercase   )
class 		UpperCamelCase__  (	__lowercase   ):
   def __init__(self			: Optional[int] ,    *snake_case_			: Dict ,    **snake_case_			: Tuple       ):
    super().__init__(*snake_case_ ,    **snake_case_       )
    self.check_model_type(snake_case_       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: str=None ,    snake_case_			: Any=None ,    snake_case_			: str=None ,    **snake_case_			: str       ):
    __a			:  Tuple		    =  {}, {}
    if padding is not None:
     __a			:  List[Any]		    =  padding
    if truncation is not None:
     __a			:  int		    =  truncation
    if top_k is not None:
     __a			:  List[str]		    =  top_k
    return preprocess_params, {}, postprocess_params
   def __call__(self			: Union[str, Any] ,    snake_case_			: Union["Image.Image", str] ,    snake_case_			: str = None ,    **snake_case_			: List[str]       ):
    if isinstance(snake_case_ ,    (Image.Image, str)       ) and isinstance(snake_case_ ,    snake_case_       ):
     __a			:  List[Any]		    =  {'''image''': image, '''question''': question}
    else:
     __a			:  Any		    =  image
    __a			:  str		    =  super().__call__(snake_case_ ,    **snake_case_       )
    return results
   def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: str ,    snake_case_			: List[Any]=False ,    snake_case_			: Union[str, Any]=False       ):
    __a			:  Dict		    =  load_image(inputs['''image''']       )
    __a			:  Optional[Any]		    =  self.tokenizer(
        inputs['''question'''] ,    return_tensors=self.framework ,    padding=snake_case_ ,    truncation=snake_case_       )
    __a			:  Any		    =  self.image_processor(images=snake_case_ ,    return_tensors=self.framework       )
    model_inputs.update(snake_case_       )
    return model_inputs
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: Tuple       ):
    __a			:  Tuple		    =  self.model(**snake_case_       )
    return model_outputs







   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[Any] ,    snake_case_			: Union[str, Any]=5       ):
    if top_k > self.model.config.num_labels:
     __a			:  Union[str, Any]		    =  self.model.config.num_labels

    if self.framework == "pt":
     __a			:  Union[str, Any]		    =  model_outputs.logits.sigmoid()[0]
     __a			:  Tuple		    =  probs.topk(snake_case_       )
    else:
     raise ValueError(f"Unsupported framework: {self.framework}"       )

    __a			:  str		    =  scores.tolist()
    __a			:  List[Any]		    =  ids.tolist()
    return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case_ ,    snake_case_       )]


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
