
"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 		a_						( _UpperCAmelCase	):
    a      :				UNetaDModel
    a      :				ScoreSdeVeScheduler







    def __init__(			self			:       Tuple     ,					__UpperCamelCase			:       UNetaDModel     ,					__UpperCamelCase			:       ScoreSdeVeScheduler     )		->str:
        super().__init__()
        self.register_modules(unet=__UpperCamelCase     ,					scheduler=__UpperCamelCase     )







    @torch.no_grad()
    def __call__(			self			:       Optional[int]     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       int = 20_00     ,					__UpperCamelCase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,					__UpperCamelCase			:       Optional[str] = "pil"     ,					__UpperCamelCase			:       bool = True     ,					**__UpperCamelCase			:       Union[str, Any]     ,					)		->Union[ImagePipelineOutput, Tuple]:
        _UpperCAmelCase          =   self.unet.config.sample_size
        _UpperCAmelCase          =   (batch_size, 3, img_size, img_size)

        _UpperCAmelCase          =   self.unet

        _UpperCAmelCase          =   randn_tensor(__UpperCamelCase     ,					generator=__UpperCamelCase     ) * self.scheduler.init_noise_sigma
        _UpperCAmelCase          =   sample.to(self.device     )

        self.scheduler.set_timesteps(__UpperCamelCase     )
        self.scheduler.set_sigmas(__UpperCamelCase     )

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
            _UpperCAmelCase          =   self.scheduler.sigmas[i] * torch.ones(shape[0]     ,					device=self.device     )

            # correction step
            for _ in range(self.scheduler.config.correct_steps     ):
                _UpperCAmelCase          =   self.unet(__UpperCamelCase     ,					__UpperCamelCase     ).sample
                _UpperCAmelCase          =   self.scheduler.step_correct(__UpperCamelCase     ,					__UpperCamelCase     ,					generator=__UpperCamelCase     ).prev_sample

            # prediction step
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					__UpperCamelCase     ).sample
            _UpperCAmelCase          =   self.scheduler.step_pred(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     ,					generator=__UpperCamelCase     )

            _UpperCAmelCase    ,_UpperCAmelCase          =   output.prev_sample, output.prev_sample_mean

        _UpperCAmelCase          =   sample_mean.clamp(0     ,					1     )
        _UpperCAmelCase          =   sample.cpu().permute(0     ,					2     ,					3     ,					1     ).numpy()
        if output_type == "pil":
            _UpperCAmelCase          =   self.numpy_to_pil(__UpperCamelCase     )

        if not return_dict:
            return (sample,)

        return ImagePipelineOutput(images=__UpperCamelCase     )

"""simple docstring"""



import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 		a_						( unittest.TestCase	):







    @property
    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   UNetaDModel(
            block_out_channels=(32, 64)     ,					layers_per_block=2     ,					sample_size=32     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,					)
        return model







    @property
    def      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   VQModel(
            block_out_channels=[32, 64]     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,					up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,					latent_channels=3     ,					)
        return model







    @property
    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   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=10_00     ,					)
        return CLIPTextModel(__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.dummy_uncond_unet
        _UpperCAmelCase          =   DDIMScheduler()
        _UpperCAmelCase          =   self.dummy_vq_model

        _UpperCAmelCase          =   LDMPipeline(unet=__UpperCamelCase     ,					vqvae=__UpperCamelCase     ,					scheduler=__UpperCamelCase     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ).images

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ,					return_dict=__UpperCamelCase     )[0]

        _UpperCAmelCase          =   image[0, -3:, -3:, -1]
        _UpperCAmelCase          =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCAmelCase          =   np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < tolerance







@slow
@require_torch
class 		a_						( unittest.TestCase	):







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






        '''simple docstring'''






        _UpperCAmelCase          =   LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=5     ,					output_type="""numpy"""     ).images

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

        assert image.shape == (1, 2_56, 2_56, 3)
        _UpperCAmelCase          =   np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

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