




'''simple docstring'''







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 			_a  (	unittest.TestCase ):
       def __init__(	self						:	int   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Dict=7   ,				lowercase						:	Union[str, Any]=3   ,				lowercase						:	int=30   ,				lowercase						:	List[str]=400   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	Any=[0.5, 0.5, 0.5]   ,				lowercase						:	int=[0.5, 0.5, 0.5]   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Tuple=1 / 255   ,				lowercase						:	List[str]=True   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		min_resolution
           UpperCAmelCase			       =		max_resolution
           UpperCAmelCase			       =		do_resize
           UpperCAmelCase			       =		size
           UpperCAmelCase			       =		do_normalize
           UpperCAmelCase			       =		image_mean
           UpperCAmelCase			       =		image_std
           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_pad
       def     A      (	self						:	List[str]				):



           '''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     A      (	self						:	Union[str, Any]   ,				lowercase						:	Dict   ,				lowercase						:	Optional[Any]=False				):



           '''simple docstring'''

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

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

           return expected_height, expected_width






@require_torch
@require_vision
class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     List[Any]   				=					ConditionalDetrImageProcessor if is_vision_available() else None
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		ConditionalDetrImageProcessingTester(self				)
       @property
       def     A      (	self						:	int				):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		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						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 18, '''longest_edge''': 1_333}				)
           self.assertEqual(image_processor.do_pad   ,				lowercase				)

           UpperCAmelCase			       =		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     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           UpperCAmelCase			       =		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			       =		image_processing(image_inputs[0]   ,				return_tensors='''pt'''				).pixel_values

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

           UpperCAmelCase			       =		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						:	Union[str, Any]				):



           '''simple docstring'''

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

           UpperCAmelCase       ,     UpperCAmelCase			       =		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			       =		image_processing(lowercase   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		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						:	Optional[int]				):



           '''simple docstring'''

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

           UpperCAmelCase       ,     UpperCAmelCase			       =		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			       =		image_processing(lowercase   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		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,
               )   ,				)
       @slow
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,				'''r'''				) as f:
               UpperCAmelCase			       =		json.loads(f.read()				)

           UpperCAmelCase			       =		{'''image_id''': 39_769, '''annotations''': target}

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

           # verify pixel values
           UpperCAmelCase			       =		torch.Size([1, 3, 800, 1_066]				)
           self.assertEqual(encoding['''pixel_values'''].shape   ,				lowercase				)

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

           # verify area
           UpperCAmelCase			       =		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''']   ,				lowercase				)				)
           # verify boxes
           UpperCAmelCase			       =		torch.Size([6, 4]				)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				lowercase				)
           UpperCAmelCase			       =		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				lowercase   ,				atol=1E-3				)				)
           # verify image_id
           UpperCAmelCase			       =		torch.tensor([39_769]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				lowercase				)				)
           # verify is_crowd
           UpperCAmelCase			       =		torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				lowercase				)				)
           # verify class_labels
           UpperCAmelCase			       =		torch.tensor([75, 75, 63, 65, 17, 17]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				lowercase				)				)
           # verify orig_size
           UpperCAmelCase			       =		torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				lowercase				)				)
           # verify size
           UpperCAmelCase			       =		torch.tensor([800, 1_066]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				lowercase				)				)







       @slow
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,				'''r'''				) as f:
               UpperCAmelCase			       =		json.loads(f.read()				)

           UpperCAmelCase			       =		{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

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

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

           # verify pixel values
           UpperCAmelCase			       =		torch.Size([1, 3, 800, 1_066]				)
           self.assertEqual(encoding['''pixel_values'''].shape   ,				lowercase				)

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

           # verify area
           UpperCAmelCase			       =		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''']   ,				lowercase				)				)
           # verify boxes
           UpperCAmelCase			       =		torch.Size([6, 4]				)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				lowercase				)
           UpperCAmelCase			       =		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				lowercase   ,				atol=1E-3				)				)
           # verify image_id
           UpperCAmelCase			       =		torch.tensor([39_769]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				lowercase				)				)
           # verify is_crowd
           UpperCAmelCase			       =		torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				lowercase				)				)
           # verify class_labels
           UpperCAmelCase			       =		torch.tensor([17, 17, 63, 75, 75, 93]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				lowercase				)				)
           # verify masks
           UpperCAmelCase			       =		822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,				lowercase				)
           # verify orig_size
           UpperCAmelCase			       =		torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				lowercase				)				)
           # verify size
           UpperCAmelCase			       =		torch.tensor([800, 1_066]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				lowercase				)				)










import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__UpperCamelCase					:       Optional[Any]    					=      False

@skip_mps
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  StableDiffusionAttendAndExcitePipeline
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  TEXT_TO_IMAGE_PARAMS
							UpperCamelCase__					     =  TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''} )
							UpperCamelCase__					     =  TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase__					     =  TEXT_TO_IMAGE_IMAGE_PARAMS
							@classmethod
							def       lowerCamelCase__							(						cls						:Union[str, Any]				):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							@classmethod
							def       lowerCamelCase__							(						cls						:Union[str, Any]				):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							UNetaDConditionModel(
								    block_out_channels=(32, 64)		,      layers_per_block=1		,      sample_size=32		,      in_channels=4		,      out_channels=4		,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,      cross_attention_dim=32		,      attention_head_dim=(2, 4)		,      use_linear_projection=__magic_name__		,      )
								a										=							DDIMScheduler(
								    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""		,      clip_sample=__magic_name__		,      set_alpha_to_one=__magic_name__		,      )
								torch.manual_seed(0				)
								a										=							AutoencoderKL(
								    block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]		,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,      latent_channels=4		,      sample_size=128		,      )
								torch.manual_seed(0				)
								a										=							CLIPTextConfig(
								    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1E-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      hidden_act="""gelu"""		,      projection_dim=512		,      )
								a										=							CLIPTextModel(__magic_name__				)
								a										=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

								a										=							{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}

								return components
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:Optional[int]		,      __magic_name__						:Optional[int]=0				):







								'''simple docstring'''







								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							a										=							{
								    """prompt""": """a cat and a frog""",
								    """token_indices""": [2, 5],
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								    """max_iter_to_alter""": 2,
								    """thresholds""": {0: 0.7},
								}
								return inputs
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							self.get_dummy_inputs(__magic_name__				)
								a										=							pipe(**__magic_name__				).images
								a										=							image[0, -3:, -3:, -1]

								self.assertEqual(image.shape		,      (1, 64, 64, 3)				)
								a										=							np.array(
								    [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]				)
								a										=							np.abs(image_slice.flatten() - expected_slice				).max()
								self.assertLessEqual(__magic_name__		,      1E-3				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								super().test_cpu_offload_forward_pass(expected_max_diff=5E-4				)
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								self._test_inference_batch_single_identical(batch_size=2		,      expected_max_diff=7E-4				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_save_load_local(expected_max_difference=5E-4				)





							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								super().test_save_load_optional_components(expected_max_difference=4E-4				)







@require_torch_gpu
@slow
class 		__lowerCAmelCase	(	unittest.TestCase ):
							@classmethod
							def       lowerCamelCase__							(						cls						:int				):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							@classmethod
							def       lowerCamelCase__							(						cls						:Any				):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							torch.manual_seed(51				)

								a										=							StableDiffusionAttendAndExcitePipeline.from_pretrained(
								    """CompVis/stable-diffusion-v1-4"""		,      safety_checker=__magic_name__		,      torch_dtype=torch.floataa				)
								pipe.to("""cuda"""				)

								a										=							"""a painting of an elephant with glasses"""
								a										=							[5, 7]

								a										=							pipe(
								    prompt=__magic_name__		,      token_indices=__magic_name__		,      guidance_scale=7.5		,      generator=__magic_name__		,      num_inference_steps=5		,      max_iter_to_alter=5		,      output_type="""numpy"""		,      ).images[0]

								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""				)
								assert np.abs((expected_image - image).max()				) < 5E-1

