






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

														super().__init__(*_lowerCamelCase      ,							**_lowerCamelCase					)




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

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




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Optional[Any]					):
														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=_lowerCamelCase      ,							pad_token_id=self.pre_processor.tokenizer.pad_token_id      ,							eos_token_id=self.pre_processor.tokenizer.eos_token_id      ,							use_cache=_lowerCamelCase      ,							num_beams=1      ,							bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]      ,							return_dict_in_generate=_lowerCamelCase      ,							).sequences




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

														return sequence["answer"]













'''simple docstring'''







from torch import nn





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if act_fn in ["swish", "silu"]:
														return nn.SiLU()
							elif act_fn == "mish":
														return nn.Mish()
							elif act_fn == "gelu":
														return nn.GELU()
							else:
														raise ValueError(f'''Unsupported activation function: {act_fn}'''				)

