





"""simple docstring"""






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()




@skip_mps
class 			__lowercase     (     _UpperCamelCase				, _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					StableDiffusionPanoramaPipeline
					__lowerCAmelCase					=					TEXT_TO_IMAGE_PARAMS
					__lowerCAmelCase					=					TEXT_TO_IMAGE_BATCH_PARAMS
					__lowerCAmelCase					=					TEXT_TO_IMAGE_IMAGE_PARAMS
					__lowerCAmelCase					=					TEXT_TO_IMAGE_IMAGE_PARAMS







					def 	_lowerCamelCase						(  self       ):
									torch.manual_seed(0       )
									__a	:  int         =					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		,     )
									__a	:  Optional[int]         =					DDIMScheduler()
									torch.manual_seed(0       )
									__a	:  int         =					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		,     )
									torch.manual_seed(0       )
									__a	:  Dict         =					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		,     )
									__a	:  Any         =					CLIPTextModel(_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

									__a	:  Dict         =					{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=0       ):
									__a	:  Any         =					torch.manual_seed(_UpperCAmelCase       )
									__a	:  Any         =					{
									    '''prompt''': '''a photo of the dolomites''',
									    '''generator''': generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    '''height''': None,
									    '''width''': None,
									    '''num_inference_steps''': 1,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a	:  Optional[Any]         =					self.get_dummy_components()
									__a	:  List[Any]         =					StableDiffusionPanoramaPipeline(**_UpperCAmelCase       )
									__a	:  Optional[int]         =					sd_pipe.to(_UpperCAmelCase       )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  int         =					self.get_dummy_inputs(_UpperCAmelCase       )
									__a	:  Optional[Any]         =					sd_pipe(**_UpperCAmelCase       ).images
									__a	:  Tuple         =					image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									__a	:  int         =					np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7]       )

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







					def 	_lowerCamelCase						(  self       ):
									super().test_inference_batch_consistent(batch_sizes=[1, 2]       )







					def 	_lowerCamelCase						(  self       ):
									super().test_inference_batch_single_identical(batch_size=2		,     expected_max_diff=3.2_5e-3       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a	:  Optional[int]         =					self.get_dummy_components()
									__a	:  Optional[int]         =					StableDiffusionPanoramaPipeline(**_UpperCAmelCase       )
									__a	:  Any         =					sd_pipe.to(_UpperCAmelCase       )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Optional[Any]         =					self.get_dummy_inputs(_UpperCAmelCase       )
									__a	:  Tuple         =					'''french fries'''
									__a	:  Tuple         =					sd_pipe(**_UpperCAmelCase		,     negative_prompt=_UpperCAmelCase       )
									__a	:  Any         =					output.images
									__a	:  Any         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__a	:  Union[str, Any]         =					np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a	:  Optional[int]         =					self.get_dummy_components()
									__a	:  Any         =					StableDiffusionPanoramaPipeline(**_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					sd_pipe.to(_UpperCAmelCase       )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Any         =					self.get_dummy_inputs(_UpperCAmelCase       )
									__a	:  Optional[Any]         =					sd_pipe(**_UpperCAmelCase		,     view_batch_size=2       )
									__a	:  Dict         =					output.images
									__a	:  str         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__a	:  Union[str, Any]         =					np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a	:  int         =					self.get_dummy_components()
									__a	:  Optional[Any]         =					EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0_0_8_5		,     beta_end=0.0_1_2		,     beta_schedule='''scaled_linear'''       )
									__a	:  Optional[Any]         =					StableDiffusionPanoramaPipeline(**_UpperCAmelCase       )
									__a	:  Dict         =					sd_pipe.to(_UpperCAmelCase       )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  List[Any]         =					self.get_dummy_inputs(_UpperCAmelCase       )
									__a	:  int         =					sd_pipe(**_UpperCAmelCase       ).images
									__a	:  Dict         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__a	:  Dict         =					np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a	:  int         =					self.get_dummy_components()
									__a	:  str         =					PNDMScheduler(
									    beta_start=0.0_0_0_8_5		,     beta_end=0.0_1_2		,     beta_schedule='''scaled_linear'''		,     skip_prk_steps=_UpperCAmelCase       )
									__a	:  Optional[Any]         =					StableDiffusionPanoramaPipeline(**_UpperCAmelCase       )
									__a	:  int         =					sd_pipe.to(_UpperCAmelCase       )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  str         =					self.get_dummy_inputs(_UpperCAmelCase       )
									__a	:  Dict         =					sd_pipe(**_UpperCAmelCase       ).images
									__a	:  str         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__a	:  Optional[Any]         =					np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9]       )

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






@slow
@require_torch_gpu
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self       ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=0       ):
									__a	:  int         =					torch.manual_seed(_UpperCAmelCase       )
									__a	:  List[str]         =					{
									    '''prompt''': '''a photo of the dolomites''',
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					'''stabilityai/stable-diffusion-2-base'''
									__a	:  Any         =					DDIMScheduler.from_pretrained(_UpperCAmelCase		,     subfolder='''scheduler'''       )
									__a	:  List[str]         =					StableDiffusionPanoramaPipeline.from_pretrained(_UpperCAmelCase		,     scheduler=_UpperCAmelCase		,     safety_checker=_UpperCAmelCase       )
									pipe.to(_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
									pipe.enable_attention_slicing()

									__a	:  Any         =					self.get_inputs()
									__a	:  List[Any]         =					pipe(**_UpperCAmelCase       ).images
									__a	:  Tuple         =					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 2048, 3)

									__a	:  Union[str, Any]         =					np.array(
									    [
									        0.3_6_9_6_8_3_9_2,
									        0.2_7_0_2_5_3_7_2,
									        0.3_2_4_4_6_7_6_6,
									        0.2_8_3_7_9_3_8_7,
									        0.3_6_3_6_3_2_7_4,
									        0.3_0_7_3_3_3_4_7,
									        0.2_7_1_0_0_0_2_7,
									        0.2_7_0_5_4_1_2_5,
									        0.2_5_5_3_6_0_9_6,
									    ]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					StableDiffusionPanoramaPipeline.from_pretrained(
									    '''stabilityai/stable-diffusion-2-base'''		,     safety_checker=_UpperCAmelCase       )
									__a	:  Any         =					LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
									pipe.to(_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
									pipe.enable_attention_slicing()

									__a	:  Optional[int]         =					self.get_inputs()
									__a	:  List[Any]         =					pipe(**_UpperCAmelCase       ).images
									__a	:  Dict         =					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 2048, 3)

									__a	:  Tuple         =					np.array(
									    [
									        [
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									        ]
									    ]       )

									assert np.abs(expected_slice - image_slice       ).max() < 1e-3







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					0

									def callback_fn(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ) -> None:
													__a	:  Tuple         =					True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																	__a	:  Tuple         =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 256)
																	__a	:  Tuple         =					latents[0, -3:, -3:, -1]

																	__a	:  Tuple         =					np.array(
																	    [
																	        0.1_8_6_8_1_8_6_9,
																	        0.3_3_9_0_7_8_1_6,
																	        0.5_3_6_1_2_7_6,
																	        0.1_4_4_3_2_8_6_5,
																	        -0.0_2_8_5_6_6_1_1,
																	        -0.7_3_9_4_1_1_2_3,
																	        0.2_3_3_9_7_9_8_7,
																	        0.4_7_3_2_2_6_8_2,
																	        -0.3_7_8_2_3_1_6_4,
																	    ]       )
																	assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2
													elif step == 2:
																	__a	:  List[str]         =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 256)
																	__a	:  List[str]         =					latents[0, -3:, -3:, -1]

																	__a	:  Tuple         =					np.array(
																	    [
																	        0.1_8_5_3_9_6_4_5,
																	        0.3_3_9_8_7_2_4_8,
																	        0.5_3_7_8_5_5_9,
																	        0.1_4_4_3_7_1_4_2,
																	        -0.0_2_4_5_5_2_6_1,
																	        -0.7_3_3_8_3_1_7,
																	        0.2_3_9_9_0_7_5_5,
																	        0.4_7_3_5_6_2_7_2,
																	        -0.3_7_8_6_5_0_5,
																	    ]       )

																	assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2

									__a	:  List[Any]         =					False

									__a	:  Dict         =					'''stabilityai/stable-diffusion-2-base'''
									__a	:  Optional[Any]         =					DDIMScheduler.from_pretrained(_UpperCAmelCase		,     subfolder='''scheduler'''       )
									__a	:  Optional[Any]         =					StableDiffusionPanoramaPipeline.from_pretrained(_UpperCAmelCase		,     scheduler=_UpperCAmelCase		,     safety_checker=_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					pipe.to(_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
									pipe.enable_attention_slicing()

									__a	:  Dict         =					self.get_inputs()
									pipe(**_UpperCAmelCase		,     callback=_UpperCAmelCase		,     callback_steps=1       )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







					def 	_lowerCamelCase						(  self       ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									__a	:  int         =					'''stabilityai/stable-diffusion-2-base'''
									__a	:  List[str]         =					DDIMScheduler.from_pretrained(_UpperCAmelCase		,     subfolder='''scheduler'''       )
									__a	:  Optional[int]         =					StableDiffusionPanoramaPipeline.from_pretrained(_UpperCAmelCase		,     scheduler=_UpperCAmelCase		,     safety_checker=_UpperCAmelCase       )
									__a	:  Any         =					pipe.to(_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
									pipe.enable_attention_slicing(1       )
									pipe.enable_sequential_cpu_offload()

									__a	:  Any         =					self.get_inputs()
									__a	:  Tuple         =					pipe(**_UpperCAmelCase       )

									__a	:  str         =					torch.cuda.max_memory_allocated()
									# make sure that less than 5.2 GB is allocated
									assert mem_bytes < 5.5 * 10**9






"""simple docstring"""






import unittest

import numpy as np

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 PIL import Image

		from transformers import ViTImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=3		,     _UpperCAmelCase=224		,     _UpperCAmelCase=30		,     _UpperCAmelCase=400		,     _UpperCAmelCase=True		,     _UpperCAmelCase=None		,     _UpperCAmelCase=True		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     ):
									__a	:  int         =					size if size is not None else {'''height''': 18, '''width''': 18}
									__a	:  List[Any]         =					parent
									__a	:  Dict         =					batch_size
									__a	:  Dict         =					num_channels
									__a	:  int         =					image_size
									__a	:  Optional[Any]         =					min_resolution
									__a	:  Optional[int]         =					max_resolution
									__a	:  Dict         =					do_resize
									__a	:  List[Any]         =					size
									__a	:  int         =					do_normalize
									__a	:  Optional[Any]         =					image_mean
									__a	:  int         =					image_std







					def 	_lowerCamelCase						(  self       ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}






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







					'''simple docstring'''




					__lowerCAmelCase					=					ViTImageProcessor if is_vision_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					EfficientFormerImageProcessorTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_proc_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''image_mean'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''image_std'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_normalize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_resize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''size'''       )       )







					def 	_lowerCamelCase						(  self       ):
									pass







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Any         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     Image.Image       )

									# Test not batched input
									__a	:  Dict         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Any         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Tuple         =					self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     numpify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     np.ndarray       )

									# Test not batched input
									__a	:  Optional[int]         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  str         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  int         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									__a	:  List[Any]         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     torchify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     torch.Tensor       )

									# Test not batched input
									__a	:  str         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Tuple         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )