






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BlipImageProcessor





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=18							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=False							, lowercase__=[0.4814_5466, 0.457_8275, 0.4082_1073]							, lowercase__=[0.2686_2954, 0.2613_0258, 0.2757_7711]							, lowercase__=True							, )				->				Dict:
				__A    =							size if size is not None else {"height": 20, "width": 20}
				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							image_size
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_pad
				__A    =							do_convert_rgb






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_convert_rgb": self.do_convert_rgb,
				    "do_pad": self.do_pad,
				}






		def 					_lowerCamelCase					(      self							, lowercase__=False							, lowercase__=False							, lowercase__=False						)				->				str:
				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				if equal_resolution:
						__A    =							[]
						for i in range(self.batch_size						):
								image_inputs.append(
								    np.random.randint(
								        255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta						)						)
				else:
						__A    =							[]
						for i in range(self.batch_size						):
								__A ,   __A    =							np.random.choice(np.arange(self.min_resolution							, self.max_resolution						)							, 2						)
								image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta						)						)

				if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						__A    =							[Image.fromarray(np.moveaxis(lowercase__							, 0							, -1						)						) for x in image_inputs]

				if torchify:
						__A    =							[torch.from_numpy(lowercase__						) for x in image_inputs]

				return image_inputs





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       List[Any] 					= BlipImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							BlipImageProcessingTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_normalize"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_mean"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_std"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_convert_rgb"						)						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				pass






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__A    =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, Image.Image						)

				# Test not batched input
				__A    =							image_processor(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processor(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__							, numpify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, np.ndarray						)

				# Test not batched input
				__A    =							image_processor(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processor(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Any:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__							, torchify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)

				# Test not batched input
				__A    =							image_processor(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processor(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       List[str] 					= BlipImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							BlipImageProcessingTester(self							, num_channels=4						)
				__A    =							3






		@property
		def 					_lowerCamelCase					(      self						)				->				int:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_normalize"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_mean"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_std"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_convert_rgb"						)						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				pass






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__A    =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, Image.Image						)

				# Test not batched input
				__A    =							image_processor(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processor(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )









import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class        a__      :






		def __init__(      self							, lowercase__							, lowercase__=sys.maxsize						)				->				str:
				__A    =							"bilinear"
				__A    =							max_size
				__A    =							short_edge_length






		def __call__(      self							, lowercase__						)				->				Dict:
				__A    =							[]
				for img in imgs:
						__A ,   __A    =							img.shape[:2]
						# later: provide list and randomly choose index for resize
						__A    =							np.random.randint(self.short_edge_length[0]							, self.short_edge_length[1] + 1						)
						if size == 0:
								return img
						__A    =							size * 1.0 / min(lowercase__							, lowercase__						)
						if h < w:
								__A ,   __A    =							size, scale * w
						else:
								__A ,   __A    =							scale * h, size
						if max(lowercase__							, lowercase__						) > self.max_size:
								__A    =							self.max_size * 1.0 / max(lowercase__							, lowercase__						)
								__A    =							newh * scale
								__A    =							neww * scale
						__A    =							int(neww + 0.5						)
						__A    =							int(newh + 0.5						)

						if img.dtype == np.uinta:
								__A    =							Image.fromarray(lowercase__						)
								__A    =							pil_image.resize((neww, newh)							, PILImageResampling.BILINEAR						)
								__A    =							np.asarray(lowercase__						)
						else:
								__A    =							img.permute(2							, 0							, 1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
								__A    =							nn.functional.interpolate(
								    lowercase__							, (newh, neww)							, mode=self.interp_method							, align_corners=lowercase__						).squeeze(0						)
						img_augs.append(lowercase__						)

				return img_augs





class        a__      :






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				__A    =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							, cfg.INPUT.MAX_SIZE_TEST						)
				__A    =							cfg.INPUT.FORMAT
				__A    =							cfg.SIZE_DIVISIBILITY
				__A    =							cfg.PAD_VALUE
				__A    =							cfg.INPUT.MAX_SIZE_TEST
				__A    =							cfg.MODEL.DEVICE
				__A    =							torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							lambda lowercase__						: (x - self.pixel_mean) / self.pixel_std






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Optional[int]:
				__A    =							tuple(max(lowercase__						) for s in zip(*[img.shape for img in images]						)						)
				__A    =							[im.shape[-2:] for im in images]
				__A    =							[
				    nn.functional.pad(
				        lowercase__							, [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]							, value=self.pad_value							, )
				    for size, im in zip(lowercase__							, lowercase__						)
				]

				return torch.stack(lowercase__						), torch.tensor(lowercase__						)






		def __call__(      self							, lowercase__							, lowercase__=False						)				->				int:
				with torch.no_grad():
						if not isinstance(lowercase__							, lowercase__						):
								__A    =							[images]
						if single_image:
								assert len(lowercase__						) == 1
						for i in range(len(lowercase__						)						):
								if isinstance(images[i]							, torch.Tensor						):
										images.insert(lowercase__							, images.pop(lowercase__						).to(self.device						).float()						)
								elif not isinstance(images[i]							, torch.Tensor						):
										images.insert(
										    lowercase__							, torch.as_tensor(img_tensorize(images.pop(lowercase__						)							, input_format=self.input_format						)						)
										    .to(self.device						)
										    .float()							, )
            # resize smallest edge
						__A    =							torch.tensor([im.shape[:2] for im in images]						)
						__A    =							self.aug(lowercase__						)
						# transpose images and convert to torch tensors
						# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
						# now normalize before pad to avoid useless arithmetic
						__A    =							[self.normalizer(lowercase__						) for x in images]
						# now pad them to do the following operations
						__A ,   __A    =							self.pad(lowercase__						)
						# Normalize

						if self.size_divisibility > 0:
								raise NotImplementedError()
						# pad
						__A    =							torch.true_divide(lowercase__							, lowercase__						)
						if single_image:
								return images[0], sizes[0], scales_yx[0]
						else:
								return images, sizes, scales_yx



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		assert torch.isfinite(lowerCAmelCase__	).all(), "Box tensor contains infinite or NaN!"
		__A ,   __A    =							box_size
		tensor[:, 0].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 1].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 2].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 3].clamp_(min=0					,     max=lowerCAmelCase__	)

