


'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	List[str]		=     '''naver-clova-ix/donut-base-finetuned-docvqa'''
     lowerCamelCase				:	List[Any]		=     (
         '''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.'''
     )
     lowerCamelCase				:	Union[str, Any]		=     '''document_qa'''
     lowerCamelCase				:	List[str]		=     AutoProcessor
     lowerCamelCase				:	List[str]		=     VisionEncoderDecoderModel

     lowerCamelCase				:	Union[str, Any]		=     ['''image''', '''text''']
     lowerCamelCase				:	Any		=     ['''text''']





     def __init__( self       :      Optional[int]    ,      *UpperCAmelCase__       :      int    ,      **UpperCAmelCase__       :      Optional[Any]    )				->  Optional[Any]:
        if not is_vision_available():
           raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'    )

        super().__init__(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      "Image"    ,      UpperCAmelCase__       :      str    )				->  Dict:
        lowerCAmelCase     =			'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
        lowerCAmelCase     =			task_prompt.replace('{user_input}'    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			self.pre_processor.tokenizer(
            UpperCAmelCase__    ,      add_special_tokens=UpperCAmelCase__    ,      return_tensors='pt'    ).input_ids
        lowerCAmelCase     =			self.pre_processor(UpperCAmelCase__    ,      return_tensors='pt'    ).pixel_values

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





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Optional[int]    )				->  int:
        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=UpperCAmelCase__    ,      pad_token_id=self.pre_processor.tokenizer.pad_token_id    ,      eos_token_id=self.pre_processor.tokenizer.eos_token_id    ,      use_cache=UpperCAmelCase__    ,      num_beams=1    ,      bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]    ,      return_dict_in_generate=UpperCAmelCase__    ,      ).sequences





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Tuple    )				->  Dict:
        lowerCAmelCase     =			self.pre_processor.batch_decode(UpperCAmelCase__    )[0]
        lowerCAmelCase     =			sequence.replace(self.pre_processor.tokenizer.eos_token    ,      ''    )
        lowerCAmelCase     =			sequence.replace(self.pre_processor.tokenizer.pad_token    ,      ''    )
        lowerCAmelCase     =			re.sub(R'<.*?>'    ,      ''    ,      UpperCAmelCase__    ,      count=1    ).strip()  # remove first task start token
        lowerCAmelCase     =			self.pre_processor.tokenajson(UpperCAmelCase__    )

        return sequence["answer"]






'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					warnings.warn(
					    """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
					    """ Please use DeformableDetrImageProcessor instead."""       ,    __lowerCAmelCase       ,    )
					super().__init__(*__lowerCAmelCase       ,    **__lowerCAmelCase)




