



"""simple docstring"""





import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
  from PIL import Image
class 				lowercase__       (   __A				):
       UpperCamelCase						=					'naver-clova-ix/donut-base-finetuned-docvqa'
       UpperCamelCase						=					(
           'This is a tool that answers a question about an document (pdf). It takes an input named `document` which '
           'should be the document containing the information, as well as a `question` that is the question about the '
           'document. It returns a text that contains the answer to the question.'
       )
       UpperCamelCase						=					'document_qa'
       UpperCamelCase						=					AutoProcessor
       UpperCamelCase						=					VisionEncoderDecoderModel

       UpperCamelCase						=					['image', 'text']
       UpperCamelCase						=					['text']



       def __init__(							self      :  Optional[int]		,    *_UpperCAmelCase      :  Dict		,    **_UpperCAmelCase      :  Dict   )			->						Optional[int]:


             '''simple docstring'''




             if not is_vision_available():
                   raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool."   )

             super().__init__(*__lowercase		,    **__lowercase   )



       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							"<s_docvqa><s_question>{user_input}</s_question><s_answer>"
             UpperCAmelCase_      			=							task_prompt.replace("{user_input}"		,    __lowercase   )
             UpperCAmelCase_      			=							self.pre_processor.tokenizer(
                 __lowercase		,    add_special_tokens=__lowercase		,    return_tensors="pt"   ).input_ids
             UpperCAmelCase_      			=							self.pre_processor(__lowercase		,    return_tensors="pt"   ).pixel_values

             return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}



       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  Union[str, Any]   )			->						Optional[int]:


             '''simple docstring'''




             return self.model.generate(
                 inputs["pixel_values"].to(self.device   )		,    decoder_input_ids=inputs["decoder_input_ids"].to(self.device   )		,    max_length=self.model.decoder.config.max_position_embeddings		,    early_stopping=__lowercase		,    pad_token_id=self.pre_processor.tokenizer.pad_token_id		,    eos_token_id=self.pre_processor.tokenizer.eos_token_id		,    use_cache=__lowercase		,    num_beams=1		,    bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,    return_dict_in_generate=__lowercase		,    ).sequences







       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  Tuple   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.pre_processor.batch_decode(__lowercase   )[0]
             UpperCAmelCase_      			=							sequence.replace(self.pre_processor.tokenizer.eos_token		,    ""   )
             UpperCAmelCase_      			=							sequence.replace(self.pre_processor.tokenizer.pad_token		,    ""   )
             UpperCAmelCase_      			=							re.sub(r"<.*?>"		,    ""		,    __lowercase		,    count=1   ).strip()  # remove first task start token
             UpperCAmelCase_      			=							self.pre_processor.tokenajson(__lowercase   )

             return sequence["answer"]









"""simple docstring"""





from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




