




"""simple docstring"""




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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 transformers import OneFormerImageProcessor
										from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
										from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
					from PIL import Image



def 					a__				(       snake_case__ ,				snake_case__="shi-labs/oneformer_demo"       )		->      str:
			with open(hf_hub_download(snake_case__ ,				snake_case__ ,				repo_type="""dataset"""       ) ,				"""r"""       ) as f:
						lowerCamelCase       =      json.load(snake_case__       )
			lowerCamelCase       =      {}
			lowerCamelCase       =      []
			lowerCamelCase       =      []
			for key, info in class_info.items():
						lowerCamelCase       =      info["""name"""]
						class_names.append(info["""name"""]       )
						if info["isthing"]:
									thing_ids.append(int(snake_case__       )       )
			lowerCamelCase       =      thing_ids
			lowerCamelCase       =      class_names
			return metadata






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=7	,				_a=3	,				_a=30	,				_a=400	,				_a=None	,				_a=True	,				_a=True	,				_a=[0.5, 0.5, 0.5]	,				_a=[0.5, 0.5, 0.5]	,				_a=10	,				_a=False	,				_a=255	,				_a="shi-labs/oneformer_demo"	,				_a="ade20k_panoptic.json"	,				_a=10	,				):





							"""simple docstring"""
							lowerCamelCase       =      parent
							lowerCamelCase       =      batch_size
							lowerCamelCase       =      num_channels
							lowerCamelCase       =      min_resolution
							lowerCamelCase       =      max_resolution
							lowerCamelCase       =      do_resize
							lowerCamelCase       =      {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
							lowerCamelCase       =      do_normalize
							lowerCamelCase       =      image_mean
							lowerCamelCase       =      image_std
							lowerCamelCase       =      class_info_file
							lowerCamelCase       =      prepare_metadata(_a	,				_a				)
							lowerCamelCase       =      num_text
							lowerCamelCase       =      repo_path

							# for the post_process_functions
							lowerCamelCase       =      2
							lowerCamelCase       =      10
							lowerCamelCase       =      10
							lowerCamelCase       =      3
							lowerCamelCase       =      4
							lowerCamelCase       =      num_labels
							lowerCamelCase       =      do_reduce_labels
							lowerCamelCase       =      ignore_index





				def      _lowerCAmelCase       ( self				):





							"""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,
							    "num_labels": self.num_labels,
							    "do_reduce_labels": self.do_reduce_labels,
							    "ignore_index": self.ignore_index,
							    "class_info_file": self.class_info_file,
							    "metadata": self.metadata,
							    "num_text": self.num_text,
							}





				def      _lowerCAmelCase       ( self	,				_a	,				_a=False				):





							"""simple docstring"""
							if not batched:
										lowerCamelCase       =      image_inputs[0]
										if isinstance(_a	,				Image.Image				):
													lowerCamelCase   ,     lowerCamelCase       =      image.size
										else:
													lowerCamelCase   ,     lowerCamelCase       =      image.shape[1], image.shape[2]
										if w < h:
													lowerCamelCase       =      int(self.size["""shortest_edge"""] * h / w				)
													lowerCamelCase       =      self.size["""shortest_edge"""]
										elif w > h:
													lowerCamelCase       =      self.size["""shortest_edge"""]
													lowerCamelCase       =      int(self.size["""shortest_edge"""] * w / h				)
										else:
													lowerCamelCase       =      self.size["""shortest_edge"""]
													lowerCamelCase       =      self.size["""shortest_edge"""]

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

							return expected_height, expected_width





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return OneFormerForUniversalSegmentationOutput(
							    # +1 for null class
							    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)				)	,				masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)				)	,				)






@require_torch
@require_vision
class 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
				# only for test_image_processing_common.test_image_proc_to_json_string
				__UpperCamelCase    						=		image_processing_class





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      OneFormerImageProcessorTester(self				)





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self.image_processing_tester.prepare_image_processor_dict()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      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"""				)				)
							self.assertTrue(hasattr(_a	,				"""ignore_index"""				)				)
							self.assertTrue(hasattr(_a	,				"""class_info_file"""				)				)
							self.assertTrue(hasattr(_a	,				"""num_text"""				)				)
							self.assertTrue(hasattr(_a	,				"""repo_path"""				)				)
							self.assertTrue(hasattr(_a	,				"""metadata"""				)				)
							self.assertTrue(hasattr(_a	,				"""do_reduce_labels"""				)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processor
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							lowerCamelCase       =      prepare_image_inputs(self.image_processing_tester	,				equal_resolution=_a				)
							for image in image_inputs:
										self.assertIsInstance(_a	,				Image.Image				)

							# Test not batched input
							lowerCamelCase       =      image_processor(image_inputs[0]	,				["""semantic"""]	,				return_tensors="""pt"""				).pixel_values

							lowerCamelCase   ,     lowerCamelCase       =      self.image_processing_tester.get_expected_values(_a				)

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

							# Test batched
							lowerCamelCase   ,     lowerCamelCase       =      self.image_processing_tester.get_expected_values(_a	,				batched=_a				)

							lowerCamelCase       =      image_processor(
							    _a	,				["""semantic"""] * len(_a				)	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processor
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCamelCase       =      prepare_image_inputs(self.image_processing_tester	,				equal_resolution=_a	,				numpify=_a				)
							for image in image_inputs:
										self.assertIsInstance(_a	,				np.ndarray				)

							# Test not batched input
							lowerCamelCase       =      image_processor(image_inputs[0]	,				["""semantic"""]	,				return_tensors="""pt"""				).pixel_values

							lowerCamelCase   ,     lowerCamelCase       =      self.image_processing_tester.get_expected_values(_a				)

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

							# Test batched
							lowerCamelCase   ,     lowerCamelCase       =      self.image_processing_tester.get_expected_values(_a	,				batched=_a				)

							lowerCamelCase       =      image_processor(
							    _a	,				["""semantic"""] * len(_a				)	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processor
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCamelCase       =      prepare_image_inputs(self.image_processing_tester	,				equal_resolution=_a	,				torchify=_a				)
							for image in image_inputs:
										self.assertIsInstance(_a	,				torch.Tensor				)

							# Test not batched input
							lowerCamelCase       =      image_processor(image_inputs[0]	,				["""semantic"""]	,				return_tensors="""pt"""				).pixel_values

							lowerCamelCase   ,     lowerCamelCase       =      self.image_processing_tester.get_expected_values(_a				)

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

							# Test batched
							lowerCamelCase   ,     lowerCamelCase       =      self.image_processing_tester.get_expected_values(_a	,				batched=_a				)

							lowerCamelCase       =      image_processor(
							    _a	,				["""semantic"""] * len(_a				)	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,				)





				def      _lowerCAmelCase       ( self	,				_a=False	,				_a=False	,				_a="np"				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# prepare image and target
							lowerCamelCase       =      self.image_processing_tester.num_labels
							lowerCamelCase       =      None
							lowerCamelCase       =      None
							lowerCamelCase       =      prepare_image_inputs(self.image_processing_tester	,				equal_resolution=_a				)
							if with_segmentation_maps:
										lowerCamelCase       =      num_labels
										if is_instance_map:
													lowerCamelCase       =      list(range(_a				)				) * 2
													lowerCamelCase       =      dict(enumerate(_a				)				)
										lowerCamelCase       =      [
										    np.random.randint(0	,				high * 2	,				(img.size[1], img.size[0])				).astype(np.uinta				) for img in image_inputs
										]
										if segmentation_type == "pil":
													lowerCamelCase       =      [Image.fromarray(_a				) for annotation in annotations]

							lowerCamelCase       =      image_processor(
							    _a	,				["""semantic"""] * len(_a				)	,				_a	,				return_tensors="""pt"""	,				instance_id_to_semantic_id=_a	,				pad_and_return_pixel_mask=_a	,				)

							return inputs





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							def common(_a=False	,				_a=None				):
										lowerCamelCase       =      self.comm_get_image_processor_inputs(
										    with_segmentation_maps=_a	,				is_instance_map=_a	,				segmentation_type=_a				)

										lowerCamelCase       =      inputs["""mask_labels"""]
										lowerCamelCase       =      inputs["""class_labels"""]
										lowerCamelCase       =      inputs["""pixel_values"""]
										lowerCamelCase       =      inputs["""text_inputs"""]

										# check the batch_size
										for mask_label, class_label, text_input in zip(_a	,				_a	,				_a				):
													self.assertEqual(mask_label.shape[0]	,				class_label.shape[0]				)
													# this ensure padding has happened
													self.assertEqual(mask_label.shape[1:]	,				pixel_values.shape[2:]				)
													self.assertEqual(len(_a				)	,				self.image_processing_tester.num_text				)

							common()
							common(is_instance_map=_a				)
							common(is_instance_map=_a	,				segmentation_type="""pil"""				)
							common(is_instance_map=_a	,				segmentation_type="""pil"""				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      np.zeros((20, 50)				)
							lowerCamelCase       =      1
							lowerCamelCase       =      1
							lowerCamelCase       =      1

							lowerCamelCase       =      binary_mask_to_rle(_a				)
							self.assertEqual(len(_a				)	,				4				)
							self.assertEqual(rle[0]	,				21				)
							self.assertEqual(rle[1]	,				45				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,				max_seq_length=77	,				task_seq_length=77	,				class_info_file="""ade20k_panoptic.json"""	,				num_text=self.image_processing_tester.num_text	,				repo_path="""shi-labs/oneformer_demo"""	,				)
							lowerCamelCase       =      self.image_processing_tester.get_fake_oneformer_outputs()

							lowerCamelCase       =      fature_extractor.post_process_semantic_segmentation(_a				)

							self.assertEqual(len(_a				)	,				self.image_processing_tester.batch_size				)
							self.assertEqual(
							    segmentation[0].shape	,				(
							        self.image_processing_tester.height,
							        self.image_processing_tester.width,
							    )	,				)

							lowerCamelCase       =      [(1, 4) for i in range(self.image_processing_tester.batch_size				)]
							lowerCamelCase       =      fature_extractor.post_process_semantic_segmentation(_a	,				target_sizes=_a				)

							self.assertEqual(segmentation[0].shape	,				target_sizes[0]				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,				max_seq_length=77	,				task_seq_length=77	,				class_info_file="""ade20k_panoptic.json"""	,				num_text=self.image_processing_tester.num_text	,				repo_path="""shi-labs/oneformer_demo"""	,				)
							lowerCamelCase       =      self.image_processing_tester.get_fake_oneformer_outputs()
							lowerCamelCase       =      image_processor.post_process_instance_segmentation(_a	,				threshold=0				)

							self.assertTrue(len(_a				) == self.image_processing_tester.batch_size				)
							for el in segmentation:
										self.assertTrue("""segmentation""" in el				)
										self.assertTrue("""segments_info""" in el				)
										self.assertEqual(type(el["""segments_info"""]				)	,				_a				)
										self.assertEqual(
										    el["""segmentation"""].shape	,				(self.image_processing_tester.height, self.image_processing_tester.width)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,				max_seq_length=77	,				task_seq_length=77	,				class_info_file="""ade20k_panoptic.json"""	,				num_text=self.image_processing_tester.num_text	,				repo_path="""shi-labs/oneformer_demo"""	,				)
							lowerCamelCase       =      self.image_processing_tester.get_fake_oneformer_outputs()
							lowerCamelCase       =      image_processor.post_process_panoptic_segmentation(_a	,				threshold=0				)

							self.assertTrue(len(_a				) == self.image_processing_tester.batch_size				)
							for el in segmentation:
										self.assertTrue("""segmentation""" in el				)
										self.assertTrue("""segments_info""" in el				)
										self.assertEqual(type(el["""segments_info"""]				)	,				_a				)
										self.assertEqual(
										    el["""segmentation"""].shape	,				(self.image_processing_tester.height, self.image_processing_tester.width)				)









"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      UNetaDModel(
							    block_out_channels=(32, 64)	,				layers_per_block=2	,				sample_size=32	,				in_channels=3	,				out_channels=3	,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")	,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")	,				)
							return model





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.dummy_uncond_unet
							lowerCamelCase       =      ScoreSdeVeScheduler()

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a	,				return_dict=_a				)[
							    0
							]

							lowerCamelCase       =      image[0, -3:, -3:, -1]
							lowerCamelCase       =      image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowerCamelCase       =      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2






@slow
@require_torch
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      """google/ncsnpp-church-256"""
							lowerCamelCase       =      UNetaDModel.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVeScheduler.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=10	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							lowerCamelCase       =      np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2



