


'''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				(		lowerCAmelCase_					):
	A_				=    "naver-clova-ix/donut-base-finetuned-docvqa"
	A_				=    (
	    "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."
	)
	A_				=    "document_qa"
	A_				=    AutoProcessor
	A_				=    VisionEncoderDecoderModel

	A_				=    ["image", "text"]
	A_				=    ["text"]




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

						'''simple docstring'''





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

						super().__init__(*__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





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

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




	def       __UpperCAmelCase							(   self						,				__a							):

						'''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						,				__a							):

						'''simple docstring'''





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

						return sequence["answer"]




"""simple docstring"""






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 42
					snake_case__				    = None
					snake_case__				    = None






lowerCAmelCase__  :  Union[str, Any]						     =	namedtuple('CoinsDistribResult', 'moves excess')



def   a_						(					lowerCamelCase       ):

				if root is None:
								return 0

				# Validation
				def count_nodes(lowerCamelCase       ) -> int:
								if node is None:
												return 0

								return count_nodes(node.left       ) + count_nodes(node.right       ) + 1

				def count_coins(lowerCamelCase       ) -> int:
								if node is None:
												return 0

								return count_coins(node.left       ) + count_coins(node.right       ) + node.data

				if count_nodes(lowerCamelCase       ) != count_coins(lowerCamelCase       ):
								raise ValueError('The nodes number should be same as the number of coins'       )

				# Main calculation
				def get_distrib(lowerCamelCase       ) -> CoinsDistribResult:

								if node is None:
												return CoinsDistribResult(0					,			1       )

								UpperCAmelCase__      ,	UpperCAmelCase__             =							get_distrib(node.left       )
								UpperCAmelCase__      ,	UpperCAmelCase__             =							get_distrib(node.right       )

								UpperCAmelCase__             =							1 - left_distrib_excess
								UpperCAmelCase__             =							1 - right_distrib_excess

								UpperCAmelCase__             =							(
								    left_distrib_moves
								    + right_distrib_moves
								    + abs(lowerCamelCase       )
								    + abs(lowerCamelCase       )
								)
								UpperCAmelCase__             =							node.data - coins_to_left - coins_to_right

								return CoinsDistribResult(lowerCamelCase					,			lowerCamelCase       )

				return get_distrib(lowerCamelCase       )[0]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



