




"""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    snake_case     (    SCREAMING_SNAKE_CASE__  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  """naver-clova-ix/donut-base-finetuned-docvqa"""
				SCREAMING_SNAKE_CASE_       :				Any       				=  (
				    """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_       :				int       				=  """document_qa"""
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  AutoProcessor
				SCREAMING_SNAKE_CASE_       :				Any       				=  VisionEncoderDecoderModel

				SCREAMING_SNAKE_CASE_       :				List[str]       				=  ["""image""", """text"""]
				SCREAMING_SNAKE_CASE_       :				Any       				=  ["""text"""]

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



						'''simple docstring'''


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

						super().__init__(*UpperCamelCase__					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    "Image"					,  UpperCamelCase__							:    str)->       List[Any]:



						'''simple docstring'''


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

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

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Tuple)->       List[Any]:



						'''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=UpperCamelCase__					,  pad_token_id=self.pre_processor.tokenizer.pad_token_id					,  eos_token_id=self.pre_processor.tokenizer.eos_token_id					,  use_cache=UpperCamelCase__					,  num_beams=1					,  bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]					,  return_dict_in_generate=UpperCamelCase__					,  ).sequences




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



						'''simple docstring'''


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

						return sequence["answer"]





"""simple docstring"""


import numpy as np






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     np.array:
		return (2 / (1 + np.exp(-2 * vector			))) - 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()


