


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       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=7       ,  lowercase_=3       ,  lowercase_=30       ,  lowercase_=400       ,  lowercase_=True       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=True       ,  lowercase_=1 / 255       ,  lowercase_=True       ,  )  -> List[str]:
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											a__      =size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											a__      =parent
											a__      =batch_size
											a__      =num_channels
											a__      =min_resolution
											a__      =max_resolution
											a__      =do_resize
											a__      =size
											a__      =do_normalize
											a__      =image_mean
											a__      =image_std
											a__      =do_rescale
											a__      =rescale_factor
											a__      =do_pad





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

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

											return expected_height, expected_width







@require_torch
@require_vision
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =YolosImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =YolosImageProcessingTester(self)





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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =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 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size       ,  {'shortest_edge': 18, 'longest_edge': 1333})
											self.assertEqual(image_processor.do_pad       ,  lowercase_)

											a__      =self.image_processing_class.from_dict(
											    self.image_processor_dict       ,  size=42       ,  max_size=84       ,  pad_and_return_pixel_mask=lowercase_)
											self.assertEqual(image_processor.size       ,  {'shortest_edge': 42, 'longest_edge': 84})
											self.assertEqual(image_processor.do_pad       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											pass





				def 						__UpperCamelCase    (				self)  -> Any:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image)

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

											a__    ,     a__      =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
											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

											a__      =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 						__UpperCamelCase    (				self)  -> Any:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											a__      =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
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =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
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =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 						__UpperCamelCase    (				self)  -> Dict:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											a__      =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
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =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
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =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 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# Initialize image_processings
											a__      =self.image_processing_class(**self.image_processor_dict)
											a__      =self.image_processing_class(do_resize=lowercase_       ,  do_normalize=lowercase_       ,  do_rescale=lowercase_)
											# create random PyTorch tensors
											a__      =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
											a__      =image_processing_a.pad(lowercase_       ,  return_tensors='pt')
											a__      =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 						__UpperCamelCase    (				self)  -> int:
											# prepare image and target
											a__      =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,  'r') as f:
																		a__      =json.loads(f.read())

											a__      ={'image_id': 39769, 'annotations': target}

											# encode them
											a__      =YolosImageProcessor.from_pretrained('hustvl/yolos-small')
											a__      =image_processing(images=lowercase_       ,  annotations=lowercase_       ,  return_tensors='pt')

											# verify pixel values
											a__      =torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape       ,  lowercase_)

											a__      =torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  lowercase_       ,  atol=1e-4))

											# verify area
											a__      =torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  lowercase_))
											# verify boxes
											a__      =torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  lowercase_)
											a__      =torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  lowercase_       ,  atol=1e-3))
											# verify image_id
											a__      =torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  lowercase_))
											# verify is_crowd
											a__      =torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  lowercase_))
											# verify class_labels
											a__      =torch.tensor([75, 75, 63, 65, 17, 17])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  lowercase_))
											# verify orig_size
											a__      =torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  lowercase_))
											# verify size
											a__      =torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  lowercase_))





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# prepare image, target and masks_path
											a__      =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,  'r') as f:
																		a__      =json.loads(f.read())

											a__      ={'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

											# encode them
											a__      =YolosImageProcessor(format='coco_panoptic')
											a__      =image_processing(images=lowercase_       ,  annotations=lowercase_       ,  masks_path=lowercase_       ,  return_tensors='pt')

											# verify pixel values
											a__      =torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape       ,  lowercase_)

											a__      =torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  lowercase_       ,  atol=1e-4))

											# verify area
											a__      =torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  lowercase_))
											# verify boxes
											a__      =torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  lowercase_)
											a__      =torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  lowercase_       ,  atol=1e-3))
											# verify image_id
											a__      =torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  lowercase_))
											# verify is_crowd
											a__      =torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  lowercase_))
											# verify class_labels
											a__      =torch.tensor([17, 17, 63, 75, 75, 93])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  lowercase_))
											# verify masks
											a__      =822873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,  lowercase_)
											# verify orig_size
											a__      =torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  lowercase_))
											# verify size
											a__      =torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  lowercase_))



"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		(DDIMParallelScheduler,)
							_snake_case						      =		(('''eta''', 0.0), ('''num_inference_steps''', 5_0))




							def    A__						( self    ,				**snake_case_				)       ->  Optional[Any]:
													__lowerCAmelCase     =  {
													    """num_train_timesteps""": 1_000,
													    """beta_start""": 0.0_001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													    """clip_sample""": True,
													}

													config.update(**snake_case_				)
													return config




							def    A__						( self    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(**snake_case_				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter

													scheduler.set_timesteps(snake_case_				)

													for t in scheduler.timesteps:
																			__lowerCAmelCase     =  model(snake_case_    ,				snake_case_				)
																			__lowerCAmelCase     =  scheduler.step(snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_				).prev_sample

													return sample




							def    A__						( self				)       ->  List[str]:
													for timesteps in [100, 500, 1_000]:
																			self.check_over_configs(num_train_timesteps=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for steps_offset in [0, 1]:
																			self.check_over_configs(steps_offset=snake_case_				)

													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(steps_offset=1				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)
													scheduler.set_timesteps(5				)
													assert torch.equal(scheduler.timesteps    ,				torch.LongTensor([801, 601, 401, 201, 1]				)				)




							def    A__						( self				)       ->  int:
													for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,				[0.002, 0.02, 0.2, 2]				):
																			self.check_over_configs(beta_start=snake_case_    ,				beta_end=snake_case_				)




							def    A__						( self				)       ->  str:
													for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=snake_case_				)




							def    A__						( self				)       ->  Dict:
													for timestep_spacing in ["trailing", "leading"]:
																			self.check_over_configs(timestep_spacing=snake_case_				)




							def    A__						( self				)       ->  str:
													for rescale_betas_zero_snr in [True, False]:
																			self.check_over_configs(rescale_betas_zero_snr=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													self.check_over_configs(thresholding=snake_case_				)
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=snake_case_    ,				prediction_type=snake_case_    ,				sample_max_value=snake_case_    ,				)




							def    A__						( self				)       ->  Union[str, Any]:
													for t in [1, 10, 49]:
																			self.check_over_forward(time_step=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, num_inference_steps in zip([1, 10, 50]    ,				[10, 50, 500]				):
																			self.check_over_forward(time_step=snake_case_    ,				num_inference_steps=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, eta in zip([1, 10, 49]    ,				[0.0, 0.5, 1.0]				):
																			self.check_over_forward(time_step=snake_case_    ,				eta=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(420    ,				400				) - 0.14_771				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(980    ,				960				) - 0.32_460				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487    ,				486				) - 0.00_979				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999    ,				998				) - 0.02				)				) < 1e-5




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0
													scheduler.set_timesteps(snake_case_				)

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter
													__lowerCAmelCase     =  self.dummy_sample_deter + 0.1
													__lowerCAmelCase     =  self.dummy_sample_deter - 0.1

													__lowerCAmelCase     =  samplea.shape[0]
													__lowerCAmelCase     =  torch.stack([samplea, samplea, samplea]    ,				dim=0				)
													__lowerCAmelCase     =  torch.arange(snake_case_				)[0:3, None].repeat(1    ,				snake_case_				)

													__lowerCAmelCase     =  model(samples.flatten(0    ,				1				)    ,				timesteps.flatten(0    ,				1				)				)
													__lowerCAmelCase     =  scheduler.batch_step_no_noise(snake_case_    ,				timesteps.flatten(0    ,				1				)    ,				samples.flatten(0    ,				1				)    ,				snake_case_				)

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
													assert abs(result_mean.item() - 0.4_982				) < 1e-3




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  self.full_loop()

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 172.0_067				) < 1e-2
													assert abs(result_mean.item() - 0.223_967				) < 1e-3




							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  self.full_loop(prediction_type="""v_prediction"""				)

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 52.5_302				) < 1e-2
													assert abs(result_mean.item() - 0.0_684				) < 1e-3




							def    A__						( self				)       ->  Tuple:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.8_295				) < 1e-2
													assert abs(result_mean.item() - 0.1_951				) < 1e-3




							def    A__						( self				)       ->  Union[str, Any]:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.0_784				) < 1e-2
													assert abs(result_mean.item() - 0.1_941				) < 1e-3
