

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        lowerCAmelCase__  (					__magic_name__				,							__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    StableDiffusionPanoramaPipeline
       lowercase_						=    TEXT_TO_IMAGE_PARAMS
       lowercase_						=    TEXT_TO_IMAGE_BATCH_PARAMS
       lowercase_						=    TEXT_TO_IMAGE_IMAGE_PARAMS
       lowercase_						=    TEXT_TO_IMAGE_IMAGE_PARAMS

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )
             __A    =UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4) ,   layers_per_block=1 ,   sample_size=3_2 ,   in_channels=4 ,   out_channels=4 ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,   cross_attention_dim=3_2 ,   )
             __A    =DDIMScheduler()
             torch.manual_seed(0     )
             __A    =AutoencoderKL(
                 block_out_channels=[3_2, 6_4] ,   in_channels=3 ,   out_channels=3 ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,   latent_channels=4 ,   )
             torch.manual_seed(0     )
             __A    =CLIPTextConfig(
                 bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=3_2 ,   intermediate_size=3_7 ,   layer_norm_eps=1E-05 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=1_0_0_0 ,   )
             __A    =CLIPTextModel(lowercase__     )
             __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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =torch.manual_seed(lowercase__     )
             __A    ={
                 '''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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __A    =self.get_dummy_components()
             __A    =StableDiffusionPanoramaPipeline(**lowercase__     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =self.get_dummy_inputs(lowercase__     )
             __A    =sd_pipe(**lowercase__     ).images
             __A    =image[0, -3:, -3:, -1]
             assert image.shape == (1, 6_4, 6_4, 3)

             __A    =np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             super().test_inference_batch_consistent(batch_sizes=[1, 2]     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             super().test_inference_batch_single_identical(batch_size=2 ,   expected_max_diff=3.25E-3     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __A    =self.get_dummy_components()
             __A    =StableDiffusionPanoramaPipeline(**lowercase__     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =self.get_dummy_inputs(lowercase__     )
             __A    ='''french fries'''
             __A    =sd_pipe(**lowercase__ ,   negative_prompt=lowercase__     )
             __A    =output.images
             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             __A    =np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __A    =self.get_dummy_components()
             __A    =StableDiffusionPanoramaPipeline(**lowercase__     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =self.get_dummy_inputs(lowercase__     )
             __A    =sd_pipe(**lowercase__ ,   view_batch_size=2     )
             __A    =output.images
             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             __A    =np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __A    =self.get_dummy_components()
             __A    =EulerAncestralDiscreteScheduler(
                 beta_start=0.0_0085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear'''     )
             __A    =StableDiffusionPanoramaPipeline(**lowercase__     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =self.get_dummy_inputs(lowercase__     )
             __A    =sd_pipe(**lowercase__     ).images
             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             __A    =np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]     )

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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __A    =self.get_dummy_components()
             __A    =PNDMScheduler(
                 beta_start=0.0_0085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   skip_prk_steps=lowercase__     )
             __A    =StableDiffusionPanoramaPipeline(**lowercase__     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =self.get_dummy_inputs(lowercase__     )
             __A    =sd_pipe(**lowercase__     ).images
             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             __A    =np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]     )

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





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



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =torch.manual_seed(lowercase__     )
             __A    ={
                 '''prompt''': '''a photo of the dolomites''',
                 '''generator''': generator,
                 '''num_inference_steps''': 3,
                 '''guidance_scale''': 7.5,
                 '''output_type''': '''numpy''',
             }
             return inputs

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''stabilityai/stable-diffusion-2-base'''
             __A    =DDIMScheduler.from_pretrained(lowercase__ ,   subfolder='''scheduler'''     )
             __A    =StableDiffusionPanoramaPipeline.from_pretrained(lowercase__ ,   scheduler=lowercase__ ,   safety_checker=lowercase__     )
             pipe.to(lowercase__     )
             pipe.set_progress_bar_config(disable=lowercase__     )
             pipe.enable_attention_slicing()

             __A    =self.get_inputs()
             __A    =pipe(**lowercase__     ).images
             __A    =image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

             __A    =np.array(
                 [
                     0.3696_8392,
                     0.2702_5372,
                     0.3244_6766,
                     0.2837_9387,
                     0.3636_3274,
                     0.3073_3347,
                     0.2710_0027,
                     0.2705_4125,
                     0.2553_6096,
                 ]     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionPanoramaPipeline.from_pretrained(
                 '''stabilityai/stable-diffusion-2-base''' ,   safety_checker=lowercase__     )
             __A    =LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.to(lowercase__     )
             pipe.set_progress_bar_config(disable=lowercase__     )
             pipe.enable_attention_slicing()

             __A    =self.get_inputs()
             __A    =pipe(**lowercase__     ).images
             __A    =image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

             __A    =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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =0

             def callback_fn(lowercase__ ,   lowercase__ ,   lowercase__     ) -> None:
                   __A    =True
                   nonlocal number_of_steps
                   number_of_steps += 1
                   if step == 1:
                         __A    =latents.detach().cpu().numpy()
                         assert latents.shape == (1, 4, 6_4, 2_5_6)
                         __A    =latents[0, -3:, -3:, -1]

                         __A    =np.array(
                             [
                                 0.1868_1869,
                                 0.3390_7816,
                                 0.536_1276,
                                 0.1443_2865,
                                 -0.0285_6611,
                                 -0.7394_1123,
                                 0.2339_7987,
                                 0.4732_2682,
                                 -0.3782_3164,
                             ]     )
                         assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2
                   elif step == 2:
                         __A    =latents.detach().cpu().numpy()
                         assert latents.shape == (1, 4, 6_4, 2_5_6)
                         __A    =latents[0, -3:, -3:, -1]

                         __A    =np.array(
                             [
                                 0.1853_9645,
                                 0.3398_7248,
                                 0.537_8559,
                                 0.1443_7142,
                                 -0.0245_5261,
                                 -0.733_8317,
                                 0.2399_0755,
                                 0.4735_6272,
                                 -0.378_6505,
                             ]     )

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

             __A    =False

             __A    ='''stabilityai/stable-diffusion-2-base'''
             __A    =DDIMScheduler.from_pretrained(lowercase__ ,   subfolder='''scheduler'''     )
             __A    =StableDiffusionPanoramaPipeline.from_pretrained(lowercase__ ,   scheduler=lowercase__ ,   safety_checker=lowercase__     )
             __A    =pipe.to(lowercase__     )
             pipe.set_progress_bar_config(disable=lowercase__     )
             pipe.enable_attention_slicing()

             __A    =self.get_inputs()
             pipe(**lowercase__ ,   callback=lowercase__ ,   callback_steps=1     )
             assert callback_fn.has_been_called
             assert number_of_steps == 3


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             __A    ='''stabilityai/stable-diffusion-2-base'''
             __A    =DDIMScheduler.from_pretrained(lowercase__ ,   subfolder='''scheduler'''     )
             __A    =StableDiffusionPanoramaPipeline.from_pretrained(lowercase__ ,   scheduler=lowercase__ ,   safety_checker=lowercase__     )
             __A    =pipe.to(lowercase__     )
             pipe.set_progress_bar_config(disable=lowercase__     )
             pipe.enable_attention_slicing(1     )
             pipe.enable_sequential_cpu_offload()

             __A    =self.get_inputs()
             __A    =pipe(**lowercase__     )

             __A    =torch.cuda.max_memory_allocated()
             # make sure that less than 5.2 GB is allocated
             assert mem_bytes < 5.5 * 1_0**9





import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





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



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-1


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_dpmpp_2m'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe(
                 [prompt] ,   generator=lowercase__ ,   guidance_scale=7.5 ,   num_inference_steps=1_5 ,   output_type='''np''' ,   use_karras_sigmas=lowercase__ ,   )

             __A    =output.images

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array(
                 [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]     )

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


