




'''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 		a							(  _lowerCamelCase	):
				snake_case_           =						"naver-clova-ix/donut-base-finetuned-docvqa"
				snake_case_           =						(
				    "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."
				)
				snake_case_           =						"document_qa"
				snake_case_           =						AutoProcessor
				snake_case_           =						VisionEncoderDecoderModel

				snake_case_           =						["image", "text"]
				snake_case_           =						["text"]


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

									super().__init__(*lowercase_			,							**lowercase_   )


				def     A_					(						self       :   List[Any]			,							lowercase_       :   "Image"			,							lowercase_       :   str   ):
									snake_case_		     =							'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
									snake_case_		     =							task_prompt.replace('''{user_input}'''			,							lowercase_   )
									snake_case_		     =							self.pre_processor.tokenizer(
									    lowercase_			,							add_special_tokens=lowercase_			,							return_tensors='''pt'''   ).input_ids
									snake_case_		     =							self.pre_processor(lowercase_			,							return_tensors='''pt'''   ).pixel_values

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


				def     A_					(						self       :   Tuple			,							lowercase_       :   Optional[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=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     A_					(						self       :   List[str]			,							lowercase_       :   Tuple   ):
									snake_case_		     =							self.pre_processor.batch_decode(lowercase_   )[0]
									snake_case_		     =							sequence.replace(self.pre_processor.tokenizer.eos_token			,							''''''   )
									snake_case_		     =							sequence.replace(self.pre_processor.tokenizer.pad_token			,							''''''   )
									snake_case_		     =							re.sub(R'''<.*?>'''			,							''''''			,							lowercase_			,							count=1   ).strip()  # remove first task start token
									snake_case_		     =							self.pre_processor.tokenajson(lowercase_   )

									return sequence["answer"]







'''simple docstring'''

from ....utils import logging


a    :							Optional[int]										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   Any=None			,							lowercase_       :   List[Any]=2048   ):
									snake_case_		     =							config.__dict__
									snake_case_		     =							modal_hidden_size
									if num_labels:
														snake_case_		     =							num_labels

