





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					@property
					def  _lowerCamelCase		(						self     ):
									torch.manual_seed(0     )
									__a :						List[str]			       =  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







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.dummy_uncond_unet
									__a :						str			       =  ScoreSdeVeScheduler()

									__a :						Tuple			       =  ScoreSdeVePipeline(unet=_UpperCAmelCase	,			scheduler=_UpperCAmelCase     )
									sde_ve.to(_UpperCAmelCase     )
									sde_ve.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						str			       =  torch.manual_seed(0     )
									__a :						str			       =  sde_ve(num_inference_steps=2	,			output_type='''numpy'''	,			generator=_UpperCAmelCase     ).images

									__a :						List[str]			       =  torch.manual_seed(0     )
									__a :						Tuple			       =  sde_ve(num_inference_steps=2	,			output_type='''numpy'''	,			generator=_UpperCAmelCase	,			return_dict=_UpperCAmelCase     )[
									    0
									]

									__a :						str			       =  image[0, -3:, -3:, -1]
									__a :						Tuple			       =  image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									__a :						Optional[int]			       =  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

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






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







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  '''google/ncsnpp-church-256'''
									__a :						List[str]			       =  UNetaDModel.from_pretrained(_UpperCAmelCase     )

									__a :						str			       =  ScoreSdeVeScheduler.from_pretrained(_UpperCAmelCase     )

									__a :						List[Any]			       =  ScoreSdeVePipeline(unet=_UpperCAmelCase	,			scheduler=_UpperCAmelCase     )
									sde_ve.to(_UpperCAmelCase     )
									sde_ve.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						int			       =  torch.manual_seed(0     )
									__a :						List[Any]			       =  sde_ve(num_inference_steps=10	,			output_type='''numpy'''	,			generator=_UpperCAmelCase     ).images

									__a :						List[str]			       =  image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)

									__a :						Any			       =  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]     )

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






"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A 			=  logging.get_logger(__name__)  # pylint: disable=invalid-name




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=768     ):
									super().__init__(_UpperCAmelCase     )
									__a :						str			       =  proj_size

									__a :						Optional[Any]			       =  CLIPVisionModel(_UpperCAmelCase     )
									__a :						List[Any]			       =  PaintByExampleMapper(_UpperCAmelCase     )
									__a :						int			       =  nn.LayerNorm(config.hidden_size     )
									__a :						List[Any]			       =  nn.Linear(config.hidden_size	,			self.proj_size     )

									# uncondition for scaling
									__a :						int			       =  nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=False     ):
									__a :						str			       =  self.model(pixel_values=_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  clip_output.pooler_output
									__a :						Optional[int]			       =  self.mapper(latent_states[:, None]     )
									__a :						int			       =  self.final_layer_norm(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  self.proj_out(_UpperCAmelCase     )
									if return_uncond_vector:
													return latent_states, self.uncond_vector

									return latent_states






class 				__lowercase			(			nn.Module							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									super().__init__()
									__a :						List[str]			       =  (config.num_hidden_layers + 1) // 5
									__a :						Optional[Any]			       =  config.hidden_size
									__a :						str			       =  1
									__a :						Union[str, Any]			       =  nn.ModuleList(
									    [
									        BasicTransformerBlock(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			activation_fn='''gelu'''	,			attention_bias=_UpperCAmelCase     )
									        for _ in range(_UpperCAmelCase     )
									    ]     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									for block in self.blocks:
													__a :						Union[str, Any]			       =  block(_UpperCAmelCase     )

									return hidden_states