






'''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 _lowercase     (    _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:    Optional[int]      ,							*UpperCamelCase__:    Optional[int]      ,							**UpperCamelCase__:    Tuple					):
														if not is_vision_available():
																					raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""					)

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




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    "Image"      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     Union[str, Any]	  =	"""<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
														lowerCamelCase__ :     Tuple	  =	task_prompt.replace("""{user_input}"""      ,							lowercase_					)
														lowerCamelCase__ :     Union[str, Any]	  =	self.pre_processor.tokenizer(
														    lowercase_      ,							add_special_tokens=lowercase_      ,							return_tensors="""pt"""					).input_ids
														lowerCamelCase__ :     Any	  =	self.pre_processor(lowercase_      ,							return_tensors="""pt"""					).pixel_values

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




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

														return sequence["answer"]









'''simple docstring'''







from __future__ import annotations





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__ :     str	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     list[str]	  =	[]
							for temp in range(int(UpperCamelCase				)				):
														series.append(f'''1 / {pow(temp + 1    ,      int(UpperCamelCase				)				)}''' if series else """1"""				)
							return series


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A     :      Optional[Any]   					=int(input('''Enter the last number (nth term) of the P-Series'''))
			_A     :      List[str]   					=int(input('''Enter the power for  P-Series'''))
			print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
			print(p_series(nth_term, power))

