
"""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     lowercase		:




							def __init__(    self       ,A__       ,A__=sys.maxsize):
												lowercase						     =						'''bilinear'''
												lowercase						     =						max_size
												lowercase						     =						short_edge_length





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

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

												return img_augs







class     lowercase		:




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




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

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





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

																	if self.size_divisibility > 0:
																						raise NotImplementedError()
																	# pad
																	lowercase						     =						torch.true_divide(__a       ,__a)
																	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(__snake_case		).all(), "Box tensor contains infinite or NaN!"
					lowercase     ,						lowercase						     =						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		)






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor







class     lowercase		( unittest.TestCase		):




							def __init__(    self       ,A__       ,A__=7       ,A__=3       ,A__=3_0       ,A__=4_0_0       ,A__=True       ,A__=None       ,A__=True       ,A__=[0.5, 0.5, 0.5]       ,A__=[0.5, 0.5, 0.5]       ,A__=True       ,A__=1 / 2_5_5       ,A__=True       ,):
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												lowercase						     =						size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						num_channels
												lowercase						     =						min_resolution
												lowercase						     =						max_resolution
												lowercase						     =						do_resize
												lowercase						     =						size
												lowercase						     =						do_normalize
												lowercase						     =						image_mean
												lowercase						     =						image_std
												lowercase						     =						do_rescale
												lowercase						     =						rescale_factor
												lowercase						     =						do_pad




							def 			A__				(    self):
												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_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}





							def 			A__				(    self       ,A__       ,A__=False):
												if not batched:
																	lowercase						     =						image_inputs[0]
																	if isinstance(A__       ,Image.Image):
																						lowercase     ,						lowercase						     =						image.size
																	else:
																						lowercase     ,						lowercase						     =						image.shape[1], image.shape[2]
																	if w < h:
																						lowercase						     =						int(self.size['''shortest_edge'''] * h / w)
																						lowercase						     =						self.size['''shortest_edge''']
																	elif w > h:
																						lowercase						     =						self.size['''shortest_edge''']
																						lowercase						     =						int(self.size['''shortest_edge'''] * w / h)
																	else:
																						lowercase						     =						self.size['''shortest_edge''']
																						lowercase						     =						self.size['''shortest_edge''']

												else:
																	lowercase						     =						[]
																	for image in image_inputs:
																						lowercase     ,						lowercase						     =						self.get_expected_values([image])
																						expected_values.append((expected_height, expected_width))
																	lowercase						     =						max(A__       ,key=lambda A__: item[0])[0]
																	lowercase						     =						max(A__       ,key=lambda A__: item[1])[1]

												return expected_height, expected_width







@require_torch
@require_vision
class     lowercase		( SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Dict	 =ConditionalDetrImageProcessor if is_vision_available() else None




							def 			A__				(    self):
												lowercase						     =						ConditionalDetrImageProcessingTester(self)




							@property
							def 			A__				(    self):
												return self.image_processor_tester.prepare_image_processor_dict()




							def 			A__				(    self):
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(A__       ,'''image_mean'''))
												self.assertTrue(hasattr(A__       ,'''image_std'''))
												self.assertTrue(hasattr(A__       ,'''do_normalize'''))
												self.assertTrue(hasattr(A__       ,'''do_resize'''))
												self.assertTrue(hasattr(A__       ,'''size'''))




							def 			A__				(    self):
												lowercase						     =						self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size       ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3})
												self.assertEqual(image_processor.do_pad       ,A__)

												lowercase						     =						self.image_processing_class.from_dict(
												    self.image_processor_dict       ,size=4_2       ,max_size=8_4       ,pad_and_return_pixel_mask=A__)
												self.assertEqual(image_processor.size       ,{'''shortest_edge''': 4_2, '''longest_edge''': 8_4})
												self.assertEqual(image_processor.do_pad       ,A__)




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,Image.Image)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




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

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__       ,torchify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,torch.Tensor)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							@slow
							def 			A__				(    self):
												# prepare image and target
												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,'''r''') as f:
																	lowercase						     =						json.loads(f.read())

												lowercase						     =						{'''image_id''': 3_9_7_6_9, '''annotations''': target}

												# encode them
												lowercase						     =						ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''')
												lowercase						     =						image_processing(images=A__       ,annotations=A__       ,return_tensors='''pt''')

												# verify pixel values
												lowercase						     =						torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape       ,A__)

												lowercase						     =						torch.tensor([0.2796, 0.3138, 0.3481])
												self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,A__       ,atol=1E-4))

												# verify area
												lowercase						     =						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,A__))
												# verify boxes
												lowercase						     =						torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,A__)
												lowercase						     =						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,A__       ,atol=1E-3))
												# verify image_id
												lowercase						     =						torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,A__))
												# verify is_crowd
												lowercase						     =						torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,A__))
												# verify class_labels
												lowercase						     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,A__))
												# verify orig_size
												lowercase						     =						torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,A__))
												# verify size
												lowercase						     =						torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,A__))





							@slow
							def 			A__				(    self):
												# prepare image, target and masks_path
												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,'''r''') as f:
																	lowercase						     =						json.loads(f.read())

												lowercase						     =						{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

												lowercase						     =						pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

												# encode them
												lowercase						     =						ConditionalDetrImageProcessor(format='''coco_panoptic''')
												lowercase						     =						image_processing(images=A__       ,annotations=A__       ,masks_path=A__       ,return_tensors='''pt''')

												# verify pixel values
												lowercase						     =						torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape       ,A__)

												lowercase						     =						torch.tensor([0.2796, 0.3138, 0.3481])
												self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,A__       ,atol=1E-4))

												# verify area
												lowercase						     =						torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,A__))
												# verify boxes
												lowercase						     =						torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,A__)
												lowercase						     =						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,A__       ,atol=1E-3))
												# verify image_id
												lowercase						     =						torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,A__))
												# verify is_crowd
												lowercase						     =						torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,A__))
												# verify class_labels
												lowercase						     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,A__))
												# verify masks
												lowercase						     =						8_2_2_8_7_3
												self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,A__)
												# verify orig_size
												lowercase						     =						torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,A__))
												# verify size
												lowercase						     =						torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,A__))




