
'''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	):
							lowercase_ : List[str]       ='''naver-clova-ix/donut-base-finetuned-docvqa'''
							lowercase_ : Optional[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.'''
							)
							lowercase_ : str       ='''document_qa'''
							lowercase_ : Tuple       =AutoProcessor
							lowercase_ : List[str]       =VisionEncoderDecoderModel

							lowercase_ : List[str]       =['''image''', '''text''']
							lowercase_ : Dict       =['''text''']




							def __init__(       self     ,*A__     ,**A__):

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

												super().__init__(*A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
												lowercase					=		task_prompt.replace('''{user_input}'''     ,A__)
												lowercase					=		self.pre_processor.tokenizer(
												    A__     ,add_special_tokens=A__     ,return_tensors='''pt''').input_ids
												lowercase					=		self.pre_processor(A__     ,return_tensors='''pt''').pixel_values

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




							def 				A__   (       self     ,A__):
												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 				A__   (       self     ,A__):
												lowercase					=		self.pre_processor.batch_decode(A__)[0]
												lowercase					=		sequence.replace(self.pre_processor.tokenizer.eos_token     ,'''''')
												lowercase					=		sequence.replace(self.pre_processor.tokenizer.pad_token     ,'''''')
												lowercase					=		re.sub(r'''<.*?>'''     ,''''''     ,A__     ,count=1).strip()  # remove first task start token
												lowercase					=		self.pre_processor.tokenajson(A__)

												return sequence["answer"]






from pathlib import Path

import numpy as np
from PIL import Image



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase			,		lowercase					=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return (gray > 127) & (gray <= 255)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		np.zeros_like(lowerCAmelCase__	)
					lowercase					=		np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

					# Copy image to padded image
					lowercase					=		image

					# Iterate over image & apply kernel
					for x in range(image.shape[1]	):
										for y in range(image.shape[0]	):
															lowercase					=		(
															    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
															).sum()
															lowercase					=		int(summation > 0	)
					return output


if __name__ == "__main__":
							# read original image
							lowercase__	:str										=					Path(__file__).resolve().parent / "image_data" / "lena.jpg"
							lowercase__	:List[str]										=					np.array(Image.open(lena_path))
							# kernel to be applied
							lowercase__	:Union[str, Any]										=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
							lowercase__	:Optional[int]										=					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
							# Save the output image
							lowercase__	:str										=					Image.fromarray(output).convert("RGB")
							pil_img.save("result_dilation.png")




