

'''simple docstring'''







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_	):
    lowercase		    =						"naver-clova-ix/donut-base-finetuned-docvqa"
    lowercase		    =						(
        "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."
    )
    lowercase		    =						"document_qa"
    lowercase		    =						AutoProcessor
    lowercase		    =						VisionEncoderDecoderModel

    lowercase		    =						["image", "text"]
    lowercase		    =						["text"]



    def __init__(    self						, *UpperCamelCase__						, **UpperCamelCase__				)  ->					List[Any]:





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

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



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					Union[str, Any]:





           '''simple docstring'''
           A_		    =					"""<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
           A_		    =					task_prompt.replace("""{user_input}"""						, __a				)
           A_		    =					self.pre_processor.tokenizer(
               __a						, add_special_tokens=__a						, return_tensors="""pt"""				).input_ids
           A_		    =					self.pre_processor(__a						, return_tensors="""pt"""				).pixel_values

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



    def     snake_case_	(    self						, UpperCamelCase__				)  ->					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     snake_case_	(    self						, UpperCamelCase__				)  ->					Optional[int]:





           '''simple docstring'''
           A_		    =					self.pre_processor.batch_decode(__a				)[0]
           A_		    =					sequence.replace(self.pre_processor.tokenizer.eos_token						, """"""				)
           A_		    =					sequence.replace(self.pre_processor.tokenizer.pad_token						, """"""				)
           A_		    =					re.sub(R"""<.*?>"""						, """"""						, __a						, count=1				).strip()  # remove first task start token
           A_		    =					self.pre_processor.tokenajson(__a				)

           return sequence["answer"]







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      str						)	->	list:
				if n_term == "":
								return []
				_a								= []
				for temp in range(int(lowercase						)						):
								series.append(F'1/{temp + 1}' if series else "1"						)
				return series


if __name__ == "__main__":
					lowerCAmelCase_					:    Union[str, Any]      	=			input('Enter the last number (nth term) of the Harmonic Series')
					print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
					print(harmonic_series(nth_term))
