


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 YolosImageProcessor




class 							lowerCamelCase   (  unittest.TestCase ):


	def __init__(    self				,     lowercase__				,     lowercase__=7				,     lowercase__=3				,     lowercase__=3_0				,     lowercase__=4_0_0				,     lowercase__=True				,     lowercase__=None				,     lowercase__=True				,     lowercase__=[0.5, 0.5, 0.5]				,     lowercase__=[0.5, 0.5, 0.5]				,     lowercase__=True				,     lowercase__=1 / 2_5_5				,     lowercase__=True				,     ):
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							__UpperCAmelCase			:							Optional[Any]	    =	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
							__UpperCAmelCase			:							Tuple	    =	parent
							__UpperCAmelCase			:							Union[str, Any]	    =	batch_size
							__UpperCAmelCase			:							str	    =	num_channels
							__UpperCAmelCase			:							Union[str, Any]	    =	min_resolution
							__UpperCAmelCase			:							int	    =	max_resolution
							__UpperCAmelCase			:							Dict	    =	do_resize
							__UpperCAmelCase			:							List[Any]	    =	size
							__UpperCAmelCase			:							List[Any]	    =	do_normalize
							__UpperCAmelCase			:							List[Any]	    =	image_mean
							__UpperCAmelCase			:							Union[str, Any]	    =	image_std
							__UpperCAmelCase			:							Union[str, Any]	    =	do_rescale
							__UpperCAmelCase			:							List[Any]	    =	rescale_factor
							__UpperCAmelCase			:							int	    =	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				,     lowercase__				,     lowercase__=False):
							if not batched:
													__UpperCAmelCase			:							Any	    =	image_inputs[0]
													if isinstance(lowercase__				,     Image.Image):
																			__UpperCAmelCase    ,      __UpperCAmelCase			:							List[str]	    =	image.size
													else:
																			__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	image.shape[1], image.shape[2]
													if w < h:
																			__UpperCAmelCase			:							int	    =	int(self.size['''shortest_edge'''] * h / w)
																			__UpperCAmelCase			:							str	    =	self.size['''shortest_edge''']
													elif w > h:
																			__UpperCAmelCase			:							Union[str, Any]	    =	self.size['''shortest_edge''']
																			__UpperCAmelCase			:							int	    =	int(self.size['''shortest_edge'''] * w / h)
													else:
																			__UpperCAmelCase			:							str	    =	self.size['''shortest_edge''']
																			__UpperCAmelCase			:							Optional[Any]	    =	self.size['''shortest_edge''']

							else:
													__UpperCAmelCase			:							Union[str, Any]	    =	[]
													for image in image_inputs:
																			__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	self.get_expected_values([image])
																			expected_values.append((expected_height, expected_width))
													__UpperCAmelCase			:							Optional[int]	    =	max(lowercase__				,     key=lambda lowercase__: item[0])[0]
													__UpperCAmelCase			:							List[Any]	    =	max(lowercase__				,     key=lambda lowercase__: item[1])[1]

							return expected_height, expected_width






@require_torch
@require_vision
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		List[str]										=						YolosImageProcessor if is_vision_available() else None


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	YolosImageProcessingTester(self)


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


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowercase__				,     '''image_mean'''))
							self.assertTrue(hasattr(lowercase__				,     '''image_std'''))
							self.assertTrue(hasattr(lowercase__				,     '''do_normalize'''))
							self.assertTrue(hasattr(lowercase__				,     '''do_resize'''))
							self.assertTrue(hasattr(lowercase__				,     '''size'''))


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	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				,     lowercase__)

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


	def  A(    self):
							pass


	def  A(    self):
							# Initialize image_processing
							__UpperCAmelCase			:							Optional[Any]	    =	self.image_processing_class(**self.image_processor_dict)
							# create random PIL images
							__UpperCAmelCase			:							Optional[Any]	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     Image.Image)

							# Test not batched input
							__UpperCAmelCase			:							Union[str, Any]	    =	image_processing(image_inputs[0]				,     return_tensors='''pt''').pixel_values

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	self.image_processor_tester.get_expected_values(lowercase__)

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

							# Test batched
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	self.image_processor_tester.get_expected_values(lowercase__				,     batched=lowercase__)

							__UpperCAmelCase			:							Dict	    =	image_processing(lowercase__				,     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
							__UpperCAmelCase			:							Optional[Any]	    =	self.image_processing_class(**self.image_processor_dict)
							# create random numpy tensors
							__UpperCAmelCase			:							str	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     numpify=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     np.ndarray)

							# Test not batched input
							__UpperCAmelCase			:							Optional[int]	    =	image_processing(image_inputs[0]				,     return_tensors='''pt''').pixel_values

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	self.image_processor_tester.get_expected_values(lowercase__)

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

							# Test batched
							__UpperCAmelCase			:							Tuple	    =	image_processing(lowercase__				,     return_tensors='''pt''').pixel_values

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Dict	    =	self.image_processor_tester.get_expected_values(lowercase__				,     batched=lowercase__)

							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
							__UpperCAmelCase			:							Union[str, Any]	    =	self.image_processing_class(**self.image_processor_dict)
							# create random PyTorch tensors
							__UpperCAmelCase			:							Tuple	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     torchify=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     torch.Tensor)

							# Test not batched input
							__UpperCAmelCase			:							List[str]	    =	image_processing(image_inputs[0]				,     return_tensors='''pt''').pixel_values

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	self.image_processor_tester.get_expected_values(lowercase__)

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

							# Test batched
							__UpperCAmelCase			:							Union[str, Any]	    =	image_processing(lowercase__				,     return_tensors='''pt''').pixel_values

							__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	self.image_processor_tester.get_expected_values(lowercase__				,     batched=lowercase__)

							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_processings
							__UpperCAmelCase			:							str	    =	self.image_processing_class(**self.image_processor_dict)
							__UpperCAmelCase			:							Union[str, Any]	    =	self.image_processing_class(do_resize=lowercase__				,     do_normalize=lowercase__				,     do_rescale=lowercase__)
							# create random PyTorch tensors
							__UpperCAmelCase			:							Optional[int]	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     torchify=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     torch.Tensor)

							# Test whether the method "pad" and calling the image processor return the same tensors
							__UpperCAmelCase			:							List[Any]	    =	image_processing_a.pad(lowercase__				,     return_tensors='''pt''')
							__UpperCAmelCase			:							int	    =	image_processing_a(lowercase__				,     return_tensors='''pt''')

							self.assertTrue(
							    torch.allclose(encoded_images_with_method['''pixel_values''']				,     encoded_images['''pixel_values''']				,     atol=1e-4))


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

							__UpperCAmelCase			:							Tuple	    =	{'''image_id''': 3_9_7_6_9, '''annotations''': target}

							# encode them
							__UpperCAmelCase			:							Tuple	    =	YolosImageProcessor.from_pretrained('''hustvl/yolos-small''')
							__UpperCAmelCase			:							Optional[int]	    =	image_processing(images=lowercase__				,     annotations=lowercase__				,     return_tensors='''pt''')

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

							__UpperCAmelCase			:							Any	    =	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,     lowercase__				,     atol=1e-4))

							# verify area
							__UpperCAmelCase			:							Union[str, Any]	    =	torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,     lowercase__))
							# verify boxes
							__UpperCAmelCase			:							List[Any]	    =	torch.Size([6, 4])
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,     lowercase__)
							__UpperCAmelCase			:							Dict	    =	torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,     lowercase__				,     atol=1e-3))
							# verify image_id
							__UpperCAmelCase			:							List[Any]	    =	torch.tensor([3_9_7_6_9])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,     lowercase__))
							# verify is_crowd
							__UpperCAmelCase			:							List[str]	    =	torch.tensor([0, 0, 0, 0, 0, 0])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,     lowercase__))
							# verify class_labels
							__UpperCAmelCase			:							Any	    =	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,     lowercase__))
							# verify orig_size
							__UpperCAmelCase			:							List[Any]	    =	torch.tensor([4_8_0, 6_4_0])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,     lowercase__))
							# verify size
							__UpperCAmelCase			:							Dict	    =	torch.tensor([8_0_0, 1_0_6_6])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,     lowercase__))


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

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

							__UpperCAmelCase			:							List[Any]	    =	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

							# encode them
							__UpperCAmelCase			:							Union[str, Any]	    =	YolosImageProcessor(format='''coco_panoptic''')
							__UpperCAmelCase			:							List[str]	    =	image_processing(images=lowercase__				,     annotations=lowercase__				,     masks_path=lowercase__				,     return_tensors='''pt''')

							# verify pixel values
							__UpperCAmelCase			:							List[str]	    =	torch.Size([1, 3, 8_0_0, 1_0_6_6])
							self.assertEqual(encoding['''pixel_values'''].shape				,     lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,     lowercase__				,     atol=1e-4))

							# verify area
							__UpperCAmelCase			:							List[str]	    =	torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,     lowercase__))
							# verify boxes
							__UpperCAmelCase			:							int	    =	torch.Size([6, 4])
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,     lowercase__)
							__UpperCAmelCase			:							int	    =	torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,     lowercase__				,     atol=1e-3))
							# verify image_id
							__UpperCAmelCase			:							List[str]	    =	torch.tensor([3_9_7_6_9])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,     lowercase__))
							# verify is_crowd
							__UpperCAmelCase			:							Optional[Any]	    =	torch.tensor([0, 0, 0, 0, 0, 0])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,     lowercase__))
							# verify class_labels
							__UpperCAmelCase			:							int	    =	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,     lowercase__))
							# verify masks
							__UpperCAmelCase			:							Union[str, Any]	    =	8_2_2_8_7_3
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,     lowercase__)
							# verify orig_size
							__UpperCAmelCase			:							Optional[Any]	    =	torch.tensor([4_8_0, 6_4_0])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,     lowercase__))
							# verify size
							__UpperCAmelCase			:							Optional[int]	    =	torch.tensor([8_0_0, 1_0_6_6])
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,     lowercase__))





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

							# setable values
							__UpperCAmelCase			:							int	    =	None
							__UpperCAmelCase			:							np.IntTensor	    =	None
							__UpperCAmelCase			:							torch.FloatTensor	    =	None  # sigma(t_i)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

							# sample eps ~ N(0, S_noise^2 * I)
							__UpperCAmelCase			:							List[str]	    =	self.config.s_noise * randn_tensor(sample.shape				,     generator=lowercase__).to(sample.device)
							__UpperCAmelCase			:							Optional[int]	    =	sigma + gamma * sigma
							__UpperCAmelCase			:							List[Any]	    =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

