


"""simple docstring"""
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					(DDPMScheduler,)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					Optional[int]   =		{
          """num_train_timesteps""": 1000,
          """beta_start""": 0.00_01,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
          """variance_type""": """fixed_small""",
          """clip_sample""": True,
      }

      config.update(**SCREAMING_SNAKE_CASE)
      return config





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]      ,   [0.0_02, 0.02, 0.2, 2]):
         self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE      ,   beta_end=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  str:
      for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE)
      for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
            self.check_over_configs(
                thresholding=SCREAMING_SNAKE_CASE      ,   prediction_type=SCREAMING_SNAKE_CASE      ,   sample_max_value=SCREAMING_SNAKE_CASE      ,   )





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      for t in [0, 500, 999]:
         self.check_over_forward(time_step=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Dict   =		self.get_scheduler_config()
      _lowerCamelCase	:					Union[str, Any]   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_09_79)) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					List[Any]   =		self.scheduler_classes[0]
      _lowerCamelCase	:					List[Any]   =		self.get_scheduler_config()
      _lowerCamelCase	:					List[str]   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		len(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		self.dummy_model()
      _lowerCamelCase	:					List[Any]   =		self.dummy_sample_deter
      _lowerCamelCase	:					int   =		torch.manual_seed(0)

      for t in reversed(range(SCREAMING_SNAKE_CASE)):
         # 1. predict noise residual
         _lowerCamelCase	:					Tuple   =		model(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         # 2. predict previous mean of sample x_t-1
         _lowerCamelCase	:					Tuple   =		scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE).prev_sample

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         _lowerCamelCase	:					Any   =		pred_prev_sample

      _lowerCamelCase	:					Dict   =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
      _lowerCamelCase	:					Dict   =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

      assert abs(result_sum.item() - 2_58.96_06) < 1e-2
      assert abs(result_mean.item() - 0.33_72) < 1e-3





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					Optional[Any]   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Dict   =		self.get_scheduler_config(prediction_type="""v_prediction""")
      _lowerCamelCase	:					Tuple   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		len(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		self.dummy_model()
      _lowerCamelCase	:					Tuple   =		self.dummy_sample_deter
      _lowerCamelCase	:					List[Any]   =		torch.manual_seed(0)

      for t in reversed(range(SCREAMING_SNAKE_CASE)):
         # 1. predict noise residual
         _lowerCamelCase	:					Tuple   =		model(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         # 2. predict previous mean of sample x_t-1
         _lowerCamelCase	:					Union[str, Any]   =		scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE).prev_sample

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         _lowerCamelCase	:					Optional[Any]   =		pred_prev_sample

      _lowerCamelCase	:					Union[str, Any]   =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
      _lowerCamelCase	:					int   =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

      assert abs(result_sum.item() - 2_02.02_96) < 1e-2
      assert abs(result_mean.item() - 0.26_31) < 1e-3





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Any   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Union[str, Any]   =		self.get_scheduler_config()
      _lowerCamelCase	:					Union[str, Any]   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		[100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		scheduler.timesteps

      for i, timestep in enumerate(SCREAMING_SNAKE_CASE):
         if i == len(SCREAMING_SNAKE_CASE) - 1:
            _lowerCamelCase	:					Dict   =		-1
         else:
            _lowerCamelCase	:					int   =		timesteps[i + 1]

         _lowerCamelCase	:					Union[str, Any]   =		scheduler.previous_timestep(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Optional[Any]   =		prev_t.item()

         self.assertEqual(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					Dict   =		self.scheduler_classes[0]
      _lowerCamelCase	:					List[str]   =		self.get_scheduler_config()
      _lowerCamelCase	:					Any   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		[100, 87, 50, 51, 0]

      with self.assertRaises(SCREAMING_SNAKE_CASE      ,   msg="""`custom_timesteps` must be in descending order."""):
         scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					str   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Dict   =		self.get_scheduler_config()
      _lowerCamelCase	:					Any   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		[100, 87, 50, 1, 0]
      _lowerCamelCase	:					List[str]   =		len(SCREAMING_SNAKE_CASE)

      with self.assertRaises(SCREAMING_SNAKE_CASE      ,   msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
         scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE      ,   timesteps=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Optional[Any]   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Optional[Any]   =		self.get_scheduler_config()
      _lowerCamelCase	:					Dict   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          SCREAMING_SNAKE_CASE      ,   msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""      ,   ):
         scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE)






"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A_						:    Union[str, Any]				=      False
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	pass




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


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[str]:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'    )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							int							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Dict							=     VersatileDiffusionTextToImagePipeline.from_pretrained(_SCREAMING_SNAKE_CASE    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     generator.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       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 			_lowerCAmelCase     (      self				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     VersatileDiffusionTextToImagePipeline.from_pretrained(
					    'shi-labs/versatile-diffusion'     ,       torch_dtype=torch.floataa    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=50     ,       output_type='numpy'    ).images

					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     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-2





