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 DetaImageProcessor




class    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=7      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=30      ,   lowerCAmelCase__=4_00      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=[0.5, 0.5, 0.5]      ,   lowerCAmelCase__=[0.5, 0.5, 0.5]      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=1 / 2_55      ,   lowerCAmelCase__=True      ,   )     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
							__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        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							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        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=False						)     ->					Optional[Any]:

							'''simple docstring'''


							if not batched:
											__lowercase 			=							image_inputs[0]
											if isinstance(lowerCAmelCase__      ,   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(lowerCAmelCase__      ,   key=lambda lowerCAmelCase__						: item[0]						)[0]
											__lowercase 			=							max(lowerCAmelCase__      ,   key=lambda lowerCAmelCase__						: item[1]						)[1]

							return expected_height, expected_width




@require_torch
@require_vision
class    _UpperCamelCase      (						_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : Optional[int]			 =						DetaImageProcessor if is_vision_available() else None





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							DetaImageProcessingTester(self						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''image_mean'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''image_std'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''do_normalize'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''do_resize'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''do_rescale'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''do_pad'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''size'''						)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size      ,   {'''shortest_edge''': 18, '''longest_edge''': 13_33}						)
							self.assertEqual(image_processor.do_pad      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							__lowercase 			=							prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=lowerCAmelCase__						)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__      ,   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(lowerCAmelCase__						)

							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(lowerCAmelCase__      ,   batched=lowerCAmelCase__						)

							__lowercase 			=							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,
							        expected_height,
							        expected_width,
							    )      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							__lowercase 			=							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
							__lowercase 			=							image_processing(image_inputs[0]      ,   return_tensors='''pt'''						).pixel_values

							__lowercase			,     __lowercase 			=							self.image_processor_tester.get_expected_values(lowerCAmelCase__						)

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

							# Test batched
							__lowercase 			=							image_processing(lowerCAmelCase__      ,   return_tensors='''pt'''						).pixel_values

							__lowercase			,     __lowercase 			=							self.image_processor_tester.get_expected_values(lowerCAmelCase__      ,   batched=lowerCAmelCase__						)

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							__lowercase 			=							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
							__lowercase 			=							image_processing(image_inputs[0]      ,   return_tensors='''pt'''						).pixel_values

							__lowercase			,     __lowercase 			=							self.image_processor_tester.get_expected_values(lowerCAmelCase__						)

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

							# Test batched
							__lowercase 			=							image_processing(lowerCAmelCase__      ,   return_tensors='''pt'''						).pixel_values

							__lowercase			,     __lowercase 			=							self.image_processor_tester.get_expected_values(lowerCAmelCase__      ,   batched=lowerCAmelCase__						)

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





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							__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_97_69, '''annotations''': target}

							# encode them
							__lowercase 			=							DetaImageProcessor()
							__lowercase 			=							image_processing(images=lowerCAmelCase__      ,   annotations=lowerCAmelCase__      ,   return_tensors='''pt'''						)

							# verify pixel values
							__lowercase 			=							torch.Size([1, 3, 8_00, 10_66]						)
							self.assertEqual(encoding['''pixel_values'''].shape      ,   lowerCAmelCase__						)

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

							# verify area
							__lowercase 			=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']      ,   lowerCAmelCase__						)						)
							# verify boxes
							__lowercase 			=							torch.Size([6, 4]						)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,   lowerCAmelCase__						)
							__lowercase 			=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,   lowerCAmelCase__      ,   atol=1E-3						)						)
							# verify image_id
							__lowercase 			=							torch.tensor([3_97_69]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,   lowerCAmelCase__						)						)
							# verify is_crowd
							__lowercase 			=							torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,   lowerCAmelCase__						)						)
							# verify class_labels
							__lowercase 			=							torch.tensor([75, 75, 63, 65, 17, 17]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,   lowerCAmelCase__						)						)
							# verify orig_size
							__lowercase 			=							torch.tensor([4_80, 6_40]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,   lowerCAmelCase__						)						)
							# verify size
							__lowercase 			=							torch.tensor([8_00, 10_66]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,   lowerCAmelCase__						)						)





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__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_97_69, '''segments_info''': target}

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

							# encode them
							__lowercase 			=							DetaImageProcessor(format='''coco_panoptic'''						)
							__lowercase 			=							image_processing(images=lowerCAmelCase__      ,   annotations=lowerCAmelCase__      ,   masks_path=lowerCAmelCase__      ,   return_tensors='''pt'''						)

							# verify pixel values
							__lowercase 			=							torch.Size([1, 3, 8_00, 10_66]						)
							self.assertEqual(encoding['''pixel_values'''].shape      ,   lowerCAmelCase__						)

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

							# verify area
							__lowercase 			=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']      ,   lowerCAmelCase__						)						)
							# verify boxes
							__lowercase 			=							torch.Size([6, 4]						)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,   lowerCAmelCase__						)
							__lowercase 			=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,   lowerCAmelCase__      ,   atol=1E-3						)						)
							# verify image_id
							__lowercase 			=							torch.tensor([3_97_69]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,   lowerCAmelCase__						)						)
							# verify is_crowd
							__lowercase 			=							torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,   lowerCAmelCase__						)						)
							# verify class_labels
							__lowercase 			=							torch.tensor([17, 17, 63, 75, 75, 93]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,   lowerCAmelCase__						)						)
							# verify masks
							__lowercase 			=							82_28_73
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()      ,   lowerCAmelCase__						)
							# verify orig_size
							__lowercase 			=							torch.tensor([4_80, 6_40]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,   lowerCAmelCase__						)						)
							# verify size
							__lowercase 			=							torch.tensor([8_00, 10_66]						)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,   lowerCAmelCase__						)						)
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						(KDPMaDiscreteScheduler,)
			__a  : Dict			 =						10





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							{
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowerCAmelCase__						)
							return config





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]      ,   [0.0002, 0.002, 0.02]						):
											self.check_over_configs(beta_start=lowerCAmelCase__      ,   beta_end=lowerCAmelCase__						)





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

							'''simple docstring'''


							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowerCAmelCase__						)





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

							'''simple docstring'''


							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config(prediction_type='''v_prediction'''						)
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07						) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07						) < 1E-2
											assert abs(result_mean.item() - 0.0002						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps      ,   device=lowerCAmelCase__						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter.to(lowerCAmelCase__						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if str(lowerCAmelCase__						).startswith('''cpu'''						):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3