import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 				__lowerCAmelCase					(    A__  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict			      =	(DDIMScheduler,)
 _lowerCamelCase  :		Union[str, Any]			      =	(('eta', 0.0), ('num_inference_steps', 50))

 def    __snake_case   (     self    :						Union[str, Any]    ,     **lowerCamelCase    :						Optional[Any]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Any					  =					{
            '''num_train_timesteps''': 1_0_0_0,
            '''beta_start''': 0.0_0_0_1,
            '''beta_end''': 0.0_2,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__snake_case					)
        return config

 def    __snake_case   (     self    :						str    ,     **lowerCamelCase    :						Any					)	->							List[str]:
        _UpperCamelCase       :    int					  =					self.scheduler_classes[0]
        _UpperCamelCase       :    int					  =					self.get_scheduler_config(**__snake_case					)
        _UpperCamelCase       :    Optional[Any]					  =					scheduler_class(**__snake_case					)

        _UpperCamelCase       :    Optional[Any]					  =					1_0, 0.0

        _UpperCamelCase       :    Union[str, Any]					  =					self.dummy_model()
        _UpperCamelCase       :    Optional[Any]					  =					self.dummy_sample_deter

        scheduler.set_timesteps(__snake_case					)

        for t in scheduler.timesteps:
               _UpperCamelCase       :    Tuple					  =					model(__snake_case    ,     __snake_case					)
               _UpperCamelCase       :    Optional[Any]					  =					scheduler.step(__snake_case    ,     __snake_case    ,     __snake_case    ,     __snake_case					).prev_sample

        return sample

 def    __snake_case   (     self    :						Tuple					)	->							Optional[Any]:
        for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__snake_case					)

 def    __snake_case   (     self    :						Tuple					)	->							List[str]:
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__snake_case					)

        _UpperCamelCase       :    Union[str, Any]					  =					self.scheduler_classes[0]
        _UpperCamelCase       :    Dict					  =					self.get_scheduler_config(steps_offset=1					)
        _UpperCamelCase       :    Any					  =					scheduler_class(**__snake_case					)
        scheduler.set_timesteps(5					)
        assert torch.equal(scheduler.timesteps    ,     torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]					)					)

 def    __snake_case   (     self    :						Union[str, Any]					)	->							Any:
        for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,     [0.0_0_2, 0.0_2, 0.2, 2]					):
               self.check_over_configs(beta_start=__snake_case    ,     beta_end=__snake_case					)

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

 def    __snake_case   (     self    :						Tuple					)	->							Optional[int]:
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__snake_case					)

 def    __snake_case   (     self    :						Optional[int]					)	->							Optional[Any]:
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__snake_case					)

 def    __snake_case   (     self    :						Optional[int]					)	->							Dict:
        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__snake_case					)

 def    __snake_case   (     self    :						List[str]					)	->							Any:
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__snake_case					)

 def    __snake_case   (     self    :						str					)	->							List[Any]:
        self.check_over_configs(thresholding=__snake_case					)
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__snake_case    ,     prediction_type=__snake_case    ,     sample_max_value=__snake_case    ,     )

 def    __snake_case   (     self    :						Optional[int]					)	->							Tuple:
        for t in [1, 1_0, 4_9]:
               self.check_over_forward(time_step=__snake_case					)

 def    __snake_case   (     self    :						Any					)	->							int:
        for t, num_inference_steps in zip([1, 1_0, 5_0]    ,     [1_0, 5_0, 5_0_0]					):
               self.check_over_forward(time_step=__snake_case    ,     num_inference_steps=__snake_case					)

 def    __snake_case   (     self    :						List[Any]					)	->							Dict:
        for t, eta in zip([1, 1_0, 4_9]    ,     [0.0, 0.5, 1.0]					):
               self.check_over_forward(time_step=__snake_case    ,     eta=__snake_case					)

 def    __snake_case   (     self    :						str					)	->							Optional[int]:
        _UpperCamelCase       :    Union[str, Any]					  =					self.scheduler_classes[0]
        _UpperCamelCase       :    Optional[int]					  =					self.get_scheduler_config()
        _UpperCamelCase       :    str					  =					scheduler_class(**__snake_case					)

        assert torch.sum(torch.abs(scheduler._get_variance(0    ,     0					) - 0.0					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_2_0    ,     4_0_0					) - 0.1_4_7_7_1					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_8_0    ,     9_6_0					) - 0.3_2_4_6_0					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0    ,     0					) - 0.0					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_8_7    ,     4_8_6					) - 0.0_0_9_7_9					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_9_9    ,     9_9_8					) - 0.0_2					)					) < 1e-5

 def    __snake_case   (     self    :						Any					)	->							Optional[int]:
        _UpperCamelCase       :    Union[str, Any]					  =					self.full_loop()

        _UpperCamelCase       :    Any					  =					torch.sum(torch.abs(__snake_case					)					)
        _UpperCamelCase       :    Optional[Any]					  =					torch.mean(torch.abs(__snake_case					)					)

        assert abs(result_sum.item() - 1_7_2.0_0_6_7					) < 1e-2
        assert abs(result_mean.item() - 0.2_2_3_9_6_7					) < 1e-3

 def    __snake_case   (     self    :						List[str]					)	->							Optional[int]:
        _UpperCamelCase       :    int					  =					self.full_loop(prediction_type='v_prediction'					)

        _UpperCamelCase       :    List[Any]					  =					torch.sum(torch.abs(__snake_case					)					)
        _UpperCamelCase       :    str					  =					torch.mean(torch.abs(__snake_case					)					)

        assert abs(result_sum.item() - 5_2.5_3_0_2					) < 1e-2
        assert abs(result_mean.item() - 0.0_6_8_4					) < 1e-3

 def    __snake_case   (     self    :						Union[str, Any]					)	->							Optional[Any]:
        # We specify different beta, so that the first alpha is 0.99
        _UpperCamelCase       :    List[Any]					  =					self.full_loop(set_alpha_to_one=__snake_case    ,     beta_start=0.0_1					)
        _UpperCamelCase       :    Optional[Any]					  =					torch.sum(torch.abs(__snake_case					)					)
        _UpperCamelCase       :    List[str]					  =					torch.mean(torch.abs(__snake_case					)					)

        assert abs(result_sum.item() - 1_4_9.8_2_9_5					) < 1e-2
        assert abs(result_mean.item() - 0.1_9_5_1					) < 1e-3

 def    __snake_case   (     self    :						Union[str, Any]					)	->							Any:
        # We specify different beta, so that the first alpha is 0.99
        _UpperCamelCase       :    Optional[Any]					  =					self.full_loop(set_alpha_to_one=__snake_case    ,     beta_start=0.0_1					)
        _UpperCamelCase       :    Dict					  =					torch.sum(torch.abs(__snake_case					)					)
        _UpperCamelCase       :    Dict					  =					torch.mean(torch.abs(__snake_case					)					)

        assert abs(result_sum.item() - 1_4_9.0_7_8_4					) < 1e-2
        assert abs(result_mean.item() - 0.1_9_4_1					) < 1e-3




import gc
import tempfile
import unittest

import numpy as np
import torch

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


UpperCAmelCase     						=			False




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



 '''simple docstring'''

 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Dict					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

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

        _UpperCamelCase       :    Optional[int]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase					)

        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    str					  =					generator.manual_seed(0					)
        _UpperCamelCase       :    Tuple					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     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    __snake_case   (     self    :						Tuple					)	->							Optional[int]:
        _UpperCamelCase       :    Optional[Any]					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					'cyberpunk 2077'
        _UpperCamelCase       :    str					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
        _UpperCamelCase       :    List[Any]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt=lowerCamelCase    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=5_0    ,     output_type='numpy'    ,     ).images

        _UpperCamelCase       :    Any					  =					image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        _UpperCamelCase       :    Optional[int]					  =					np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]					)

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



