
"""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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   'naver-clova-ix/donut-base-finetuned-docvqa'
					_SCREAMING_SNAKE_CASE       :					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.'
					)
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   'document_qa'
					_SCREAMING_SNAKE_CASE       :					int     							=   AutoProcessor
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   VisionEncoderDecoderModel

					_SCREAMING_SNAKE_CASE       :					Any     							=   ['image', 'text']
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   ['text']





					def __init__(				self   :							Dict							,						*snake_case_   :							Dict							,						**snake_case_   :							int    ):
												if not is_vision_available():
																			raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool."    )

												super().__init__(*snake_case_							,						**snake_case_    )





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

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							List[str]    ):
												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=snake_case_							,						pad_token_id=self.pre_processor.tokenizer.pad_token_id							,						eos_token_id=self.pre_processor.tokenizer.eos_token_id							,						use_cache=snake_case_							,						num_beams=1							,						bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]							,						return_dict_in_generate=snake_case_							,						).sequences





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       self.pre_processor.batch_decode(snake_case_    )[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"<.*?>"							,						""							,						snake_case_							,						count=1    ).strip()  # remove first task start token
												__snake_case     			=       self.pre_processor.tokenajson(snake_case_    )

												return sequence["answer"]


"""simple docstring"""







from __future__ import annotations

from typing import Any
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							if not postfix_notation:
														return 0

							__snake_case     			=       {"+", "-", "*", "/"}
							__snake_case     			=       []

							for token in postfix_notation:
														if token in operations:
																					__snake_case			,   __snake_case     			=       stack.pop(), stack.pop()
																					if token == "+":
																												stack.append(a + b )
																					elif token == "-":
																												stack.append(a - b )
																					elif token == "*":
																												stack.append(a * b )
																					else:
																												if a * b < 0 and a % b != 0:
																																			stack.append(a // b + 1 )
																												else:
																																			stack.append(a // b )
														else:
																					stack.append(int(SCREAMING_SNAKE_CASE ) )

							return stack.pop()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
