





import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


class 			__lowerCAmelCase				(					unittest.TestCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			Any            =		load_tool('document-question-answering' )
			self.tool.setup()
			lowercase__				:			List[Any]            =		load_tool('document-question-answering',				remote=lowerCamelCase_ )

	def       snake_case__(	self:	str ):
			lowercase__				:			Tuple            =		load_dataset('hf-internal-testing/example-documents',				split='test' )
			lowercase__				:			str            =		dataset[0]['image']

			lowercase__				:			List[str]            =		self.tool(lowerCamelCase_,				'When is the coffee break?' )
			self.assertEqual(lowerCamelCase_,				'11-14 to 11:39 a.m.' )

	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			Optional[Any]            =		load_dataset('hf-internal-testing/example-documents',				split='test' )
			lowercase__				:			Tuple            =		dataset[0]['image']

			lowercase__				:			Tuple            =		self.remote_tool(lowerCamelCase_,				'When is the coffee break?' )
			self.assertEqual(lowerCamelCase_,				'11-14 to 11:39 a.m.' )

	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			Tuple            =		load_dataset('hf-internal-testing/example-documents',				split='test' )
			lowercase__				:			Tuple            =		dataset[0]['image']

			self.tool(document=lowerCamelCase_,				question='When is the coffee break?' )





	def       snake_case__(	self:	int ):
			lowercase__				:			str            =		load_dataset('hf-internal-testing/example-documents',				split='test' )
			lowercase__				:			Dict            =		dataset[0]['image']

			lowercase__				:			Union[str, Any]            =		self.remote_tool(document=lowerCamelCase_,				question='When is the coffee break?' )
			self.assertEqual(lowerCamelCase_,				'11-14 to 11:39 a.m.' )









from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any] ):
			super().__init__()

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

			lowercase__				:			Any            =		min(h / sample_size,				w / sample_size )
			lowercase__      ,							lowercase__				:			Optional[int]            =		(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

			lowercase__      ,							lowercase__				:			Any            =		1024**2, img_w * img_h
			lowercase__				:			Union[str, Any]            =		(Sa / Sa) ** 0.5
			lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		self.watermark_image_as_pil.resize(
			    (wm_size, wm_size),				PIL_INTERPOLATION['bicubic'],				reducing_gap=lowerCamelCase_ )

			for pil_img in images:
					pil_img.paste(lowerCamelCase_,				box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y),				mask=wm_img.split()[-1] )

			return images


