


# 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 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			Tuple      = '''naver-clova-ix/donut-base-finetuned-docvqa'''
     lowerCamelCase			:			List[str]      = (
         '''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			:			Optional[Any]      = '''document_qa'''
     lowerCamelCase			:			List[str]      = AutoProcessor
     lowerCamelCase			:			int      = VisionEncoderDecoderModel

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







     def __init__(						self :						List[Any]   ,   *_lowercase :						str   ,   **_lowercase :						Tuple      ):
           if not is_vision_available():
                 raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''      )

           super().__init__(*_lowercase   ,   **_lowercase      )







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

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







     def 							lowercase__       (						self :						Dict   ,   _lowercase :						List[Any]      ):
           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 :						Optional[Any]   ,   _lowercase :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.pre_processor.batch_decode(_lowercase      )[0]
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		sequence.replace(self.pre_processor.tokenizer.eos_token   ,   ''''''      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		sequence.replace(self.pre_processor.tokenizer.pad_token   ,   ''''''      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		re.sub(R'''<.*?>'''   ,   ''''''   ,   _lowercase   ,   count=1      ).strip()  # remove first task start token
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.pre_processor.tokenajson(_lowercase      )

           return sequence["answer"]









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase_    						=       {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





