




"""simple docstring"""







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  SCREAMING_SNAKE_CASE__   :






						"""simple docstring"""




						def __init__(							self			:     List[Any]  ,  lowerCAmelCase			:     Tuple  ,  lowerCAmelCase			:     int=sys.maxsize   ):
										lowerCAmelCase		=			"""bilinear"""
										lowerCAmelCase		=			max_size
										lowerCAmelCase		=			short_edge_length




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

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

										return img_augs






class  SCREAMING_SNAKE_CASE__   :






						"""simple docstring"""




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


						def 					__lowercase	(							self			:     List[str]  ,  lowerCAmelCase			:     List[Any]   ):
										lowerCAmelCase		=			tuple(max(lowerCAmelCase   ) for s in zip(*[img.shape for img in images]   )   )
										lowerCAmelCase		=			[im.shape[-2:] for im in images]
										lowerCAmelCase		=			[
										    nn.functional.pad(
										        lowerCAmelCase  ,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]  ,  value=self.pad_value  ,  )
										    for size, im in zip(lowerCAmelCase  ,  lowerCAmelCase   )
										]

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




						def __call__(							self			:     str  ,  lowerCAmelCase			:     Optional[Any]  ,  lowerCAmelCase			:     str=False   ):
										with torch.no_grad():
														if not isinstance(lowerCAmelCase  ,  lowerCAmelCase   ):
																		lowerCAmelCase		=			[images]
														if single_image:
																		assert len(lowerCAmelCase   ) == 1
														for i in range(len(lowerCAmelCase   )   ):
																		if isinstance(images[i]  ,  torch.Tensor   ):
																						images.insert(lowerCAmelCase  ,  images.pop(lowerCAmelCase   ).to(self.device   ).float()   )
																		elif not isinstance(images[i]  ,  torch.Tensor   ):
																						images.insert(
																						    lowerCAmelCase  ,  torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase   )  ,  input_format=self.input_format   )   )
																						    .to(self.device   )
																						    .float()  ,  )
            # resize smallest edge
														lowerCAmelCase		=			torch.tensor([im.shape[:2] for im in images]   )
														lowerCAmelCase		=			self.aug(lowerCAmelCase   )
														# 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
														lowerCAmelCase		=			[self.normalizer(lowerCAmelCase   ) for x in images]
														# now pad them to do the following operations
														lowerCAmelCase	,  lowerCAmelCase		=			self.pad(lowerCAmelCase   )
														# Normalize

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





def 							lowercase   (snake_case__				:	Any ,    snake_case__				:	List[str]	)       ->					Optional[Any]:


				'''simple docstring'''






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






def 							lowercase   (snake_case__				:	int ,    snake_case__				:	Tuple[int, int]	)       ->					List[str]:


				'''simple docstring'''






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











"""simple docstring"""







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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor






class  SCREAMING_SNAKE_CASE__   (   unittest.TestCase   ):


						def __init__(							self			:     int  ,  lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Optional[Any]=7  ,  lowerCAmelCase			:     str=3  ,  lowerCAmelCase			:     str=18  ,  lowerCAmelCase			:     Tuple=30  ,  lowerCAmelCase			:     Union[str, Any]=400  ,  lowerCAmelCase			:     List[str]=True  ,  lowerCAmelCase			:     List[str]=None  ,  lowerCAmelCase			:     int=True  ,  lowerCAmelCase			:     int=None  ,  lowerCAmelCase			:     Optional[int]=True  ,  lowerCAmelCase			:     Dict=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,  lowerCAmelCase			:     str=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,  lowerCAmelCase			:     Any=True  ,  ):
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			num_channels
										lowerCAmelCase		=			image_size
										lowerCAmelCase		=			min_resolution
										lowerCAmelCase		=			max_resolution
										lowerCAmelCase		=			do_resize
										lowerCAmelCase		=			size if size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_center_crop
										lowerCAmelCase		=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_normalize
										lowerCAmelCase		=			image_mean
										lowerCAmelCase		=			image_std
										lowerCAmelCase		=			do_convert_rgb




						def 					__lowercase	(							self			:     Tuple   ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}






@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__   (   _a  ,			unittest.TestCase   ):
						_a        =  OwlViTImageProcessor if is_vision_available() else None


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			OwlViTImageProcessingTester(self   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										return self.image_processor_tester.prepare_image_processor_dict()


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_resize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """size"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_normalize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_mean"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_std"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_convert_rgb"""   )   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 18, """width""": 18}   )

										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict  ,  size=42  ,  crop_size=84   )
										self.assertEqual(image_processor.size  ,  {"""height""": 42, """width""": 42}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 84, """width""": 84}   )


						def 					__lowercase	(							self			:     str   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase   )

										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  Image.Image   )

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

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )


						def 					__lowercase	(							self			:     Tuple   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  numpify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  np.ndarray   )

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

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )




						def 					__lowercase	(							self			:     Optional[int]   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  torchify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  torch.Tensor   )

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

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )





