


'''simple docstring'''




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

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()




class  _lowercase   (  __lowercase	,				__lowercase	,				unittest.TestCase       ):
   _SCREAMING_SNAKE_CASE     :     int									=  StableDiffusionParadigmsPipeline
   _SCREAMING_SNAKE_CASE     :     str									=  TEXT_TO_IMAGE_PARAMS
   _SCREAMING_SNAKE_CASE     :     int									=  TEXT_TO_IMAGE_BATCH_PARAMS
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  TEXT_TO_IMAGE_IMAGE_PARAMS
   _SCREAMING_SNAKE_CASE     :     Dict									=  TEXT_TO_IMAGE_IMAGE_PARAMS
   def a     (	self					:			Union[str, Any]			)   ->       Optional[int]:
        torch.manual_seed(0			)
        __snake_case    			=	UNetaDConditionModel(
            block_out_channels=(32, 64)					,      layers_per_block=2					,      sample_size=32					,      in_channels=4					,      out_channels=4					,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,      cross_attention_dim=32					,      attention_head_dim=(2, 4)					,      use_linear_projection=SCREAMING_SNAKE_CASE_					,      )
        __snake_case    			=	DDIMParallelScheduler(
            beta_start=0.0_0_0_8_5					,      beta_end=0.0_1_2					,      beta_schedule='scaled_linear'					,      clip_sample=SCREAMING_SNAKE_CASE_					,      set_alpha_to_one=SCREAMING_SNAKE_CASE_					,      )
        torch.manual_seed(0			)
        __snake_case    			=	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					,      sample_size=128					,      )
        torch.manual_seed(0			)
        __snake_case    			=	CLIPTextConfig(
            bos_token_id=0					,      eos_token_id=2					,      hidden_size=32					,      intermediate_size=37					,      layer_norm_eps=1e-05					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=1000					,      hidden_act='gelu'					,      projection_dim=512					,      )
        __snake_case    			=	CLIPTextModel(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

        __snake_case    			=	{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components
   def a     (	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			Tuple=0			)   ->       str:
        if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
             __snake_case    			=	torch.manual_seed(SCREAMING_SNAKE_CASE_			)
        else:
             __snake_case    			=	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	{
            'prompt': 'a photograph of an astronaut riding a horse',
            'generator': generator,
            'num_inference_steps': 10,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'parallel': 3,
            'debug': True,
        }
        return inputs
   def a     (	self					:			Dict			)   ->       List[str]:
        __snake_case    			=	'cpu'  # ensure determinism for the device-dependent torch.Generator
        __snake_case    			=	self.get_dummy_components()
        __snake_case    			=	StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	sd_pipe(**SCREAMING_SNAKE_CASE_			).images
        __snake_case    			=	image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        __snake_case    			=	np.array([0.4_7_7_3, 0.5_4_1_7, 0.4_7_2_3, 0.4_9_2_5, 0.5_6_3_1, 0.4_7_5_2, 0.5_2_4_0, 0.4_9_3_5, 0.5_0_2_3]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
   def a     (	self					:			List[str]			)   ->       int:
        __snake_case    			=	'cpu'  # ensure determinism for the device-dependent torch.Generator
        __snake_case    			=	self.get_dummy_components()
        torch.manual_seed(0			)
        __snake_case    			=	DDPMParallelScheduler()
        torch.manual_seed(0			)
        __snake_case    			=	StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	sd_pipe(**SCREAMING_SNAKE_CASE_			).images
        __snake_case    			=	image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        __snake_case    			=	np.array([0.3_5_7_3, 0.4_4_2_0, 0.4_9_6_0, 0.4_7_9_9, 0.3_7_9_6, 0.3_8_7_9, 0.4_8_1_9, 0.4_3_6_5, 0.4_4_6_8]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
   def a     (	self					:			Any			)   ->       List[str]:
        super().test_inference_batch_consistent(batch_sizes=[1, 2]			)
   def a     (	self					:			Union[str, Any]			)   ->       int:
        super().test_inference_batch_single_identical(batch_size=2					,      expected_max_diff=3e-3			)





   def a     (	self					:			Union[str, Any]			)   ->       List[Any]:
        __snake_case    			=	'cpu'  # ensure determinism for the device-dependent torch.Generator
        __snake_case    			=	self.get_dummy_components()
        __snake_case    			=	StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

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

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

        __snake_case    			=	np.array([0.4_7_7_1, 0.5_4_2_0, 0.4_6_8_3, 0.4_9_1_8, 0.5_6_3_6, 0.4_7_2_5, 0.5_2_3_0, 0.4_9_2_3, 0.5_0_1_5]			)

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




@slow
@require_torch_gpu
class  _lowercase   (  unittest.TestCase       ):
   def a     (	self					:			List[Any]			)   ->       Union[str, Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			List[str]=0			)   ->       Dict:
        __snake_case    			=	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	{
            'prompt': 'a photograph of an astronaut riding a horse',
            'generator': generator,
            'num_inference_steps': 10,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
            'parallel': 3,
            'debug': True,
        }
        return inputs





   def a     (	self					:			List[Any]			)   ->       Optional[int]:
        __snake_case    			=	'stabilityai/stable-diffusion-2-base'
        __snake_case    			=	DDIMParallelScheduler.from_pretrained(SCREAMING_SNAKE_CASE_					,      subfolder='scheduler'			)
        __snake_case    			=	StableDiffusionParadigmsPipeline.from_pretrained(SCREAMING_SNAKE_CASE_					,      scheduler=SCREAMING_SNAKE_CASE_					,      safety_checker=SCREAMING_SNAKE_CASE_			)
        pipe.to(SCREAMING_SNAKE_CASE_			)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
        pipe.enable_attention_slicing()

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

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

        __snake_case    			=	np.array([0.9_6_2_2, 0.9_6_0_2, 0.9_7_4_8, 0.9_5_9_1, 0.9_6_3_0, 0.9_6_9_1, 0.9_6_6_1, 0.9_6_3_1, 0.9_7_4_1]			)

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








'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        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=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3




