





# 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 SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Union[str, Any]       =       "naver-clova-ix/donut-base-finetuned-docvqa"
						__magic_name__:			int       =       (
						    "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."
						)
						__magic_name__:			Optional[Any]       =       "document_qa"
						__magic_name__:			str       =       AutoProcessor
						__magic_name__:			Tuple       =       VisionEncoderDecoderModel

						__magic_name__:			int       =       ["image", "text"]
						__magic_name__:			List[str]       =       ["text"]






						def __init__(      self      :      List[Any]		,   *_A      :      Optional[int]		,   **_A      :      str	)							->    List[Any]:







							"""simple docstring"""


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

							super().__init__(*_A		,   **_A	)






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      "Image"		,   _A      :      str	)							->    List[Any]:







							"""simple docstring"""


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

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






						def UpperCAmelCase_				(      self      :      str		,   _A      :      Dict	)							->    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=_A		,   pad_token_id=self.pre_processor.tokenizer.pad_token_id		,   eos_token_id=self.pre_processor.tokenizer.eos_token_id		,   use_cache=_A		,   num_beams=1		,   bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,   return_dict_in_generate=_A		,   ).sequences






						def UpperCAmelCase_				(      self      :      str		,   _A      :      Optional[int]	)							->    List[str]:







							"""simple docstring"""


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

							return sequence["answer"]








from ....utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      str		,   _A      :      List[Any]		,   _A      :      List[Any]=None		,   _A      :      str=2048	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	config.__dict__
							snake_case_		:			List[str]											=	modal_hidden_size
							if num_labels:
								snake_case_		:			int											=	num_labels

