





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCAmelCase							:	Dict			=False






class    UpperCAmelCase	(       unittest.TestCase				):
 pass






@nightly
@require_torch_gpu
class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def      UpperCAmelCase_							(				self      :Tuple							)->							Union[str, Any]:
     A__				=			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"						,       torch_dtype=torch.floataa							)
     pipe.to(lowercase_							)
     pipe.set_progress_bar_config(disable=lowercase_							)

     A__				=			load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)

     A__				=			torch.manual_seed(0							)
     A__				=			pipe.dual_guided(
         prompt="first prompt"						,       image=lowercase_						,       text_to_image_strength=0.7_5						,       generator=lowercase_						,       guidance_scale=7.5						,       num_inference_steps=2						,       output_type="numpy"						,       ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(lowercase_							)
         A__				=			VersatileDiffusionPipeline.from_pretrained(lowercase_						,       torch_dtype=torch.floataa							)
     pipe.to(lowercase_							)
     pipe.set_progress_bar_config(disable=lowercase_							)

     A__				=			generator.manual_seed(0							)
     A__				=			pipe.dual_guided(
         prompt="first prompt"						,       image=lowercase_						,       text_to_image_strength=0.7_5						,       generator=lowercase_						,       guidance_scale=7.5						,       num_inference_steps=2						,       output_type="numpy"						,       ).images

     assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"



 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Optional[Any]:
     A__				=			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"						,       torch_dtype=torch.floataa							)
     pipe.to(lowercase_							)
     pipe.set_progress_bar_config(disable=lowercase_							)

     A__				=			"cyberpunk 2077"
     A__				=			load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
     A__				=			torch.manual_seed(0							)
     A__				=			pipe.dual_guided(
         prompt=lowercase_						,       image=lowercase_						,       text_to_image_strength=0.7_5						,       generator=lowercase_						,       guidance_scale=7.5						,       num_inference_steps=50						,       output_type="numpy"						,       ).images

     A__				=			image[0, 2_53:2_56, 2_53:2_56, -1]

     assert image.shape == (1, 5_12, 5_12, 3)
     A__				=			np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]							)

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

     A__				=			"A painting of a squirrel eating a burger "
     A__				=			torch.manual_seed(0							)
     A__				=			pipe.text_to_image(
         prompt=lowercase_						,       generator=lowercase_						,       guidance_scale=7.5						,       num_inference_steps=50						,       output_type="numpy"							).images

     A__				=			image[0, 2_53:2_56, 2_53:2_56, -1]

     assert image.shape == (1, 5_12, 5_12, 3)
     A__				=			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]							)

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

     A__				=			pipe.image_variation(lowercase_						,       generator=lowercase_						,       output_type="numpy"							).images

     A__				=			image[0, 2_53:2_56, 2_53:2_56, -1]

     assert image.shape == (1, 5_12, 5_12, 3)
     A__				=			np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]							)

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










'''simple docstring'''

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				(DPMSolverSDEScheduler,)
 __lowercase   		=				10
 def      UpperCAmelCase_							(				self      :List[Any]						,       **lowercase_      :Optional[int]							)->							str:
     A__				=			{
         "num_train_timesteps": 11_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
         "noise_sampler_seed": 0,
     }

     config.update(**lowercase_							)
     return config
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Tuple:
     for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,       [0.0_0_0_2, 0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,       beta_end=lowercase_							)
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Union[str, Any]:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

     for i, t in enumerate(scheduler.timesteps							):
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6							) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6							) < 1E-3
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config(prediction_type="v_prediction"							)
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

     for i, t in enumerate(scheduler.timesteps							):
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3							) < 1E-2
         assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3							) < 1E-2
         assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5							) < 1E-2
         assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1							) < 1E-3
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							List[str]:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_							)

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

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter.to(lowercase_							) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6							) < 1E-3



 def      UpperCAmelCase_							(				self      :Tuple							)->							Dict:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_						,       use_karras_sigmas=lowercase_							)

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

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter.to(lowercase_							) * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

     for t in scheduler.timesteps:
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2
     else:
         assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2



