




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 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         torch.manual_seed(0	)
         snake_case      :				str				=       UNetaDModel(
             block_out_channels=(3_2, 6_4)				,      layers_per_block=2				,      sample_size=3_2				,      in_channels=3				,      out_channels=3				,      down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")				,      up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")				,      )
         return model

      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         snake_case      :				Any				=       self.dummy_uncond_unet
         snake_case      :				Optional[Any]				=       ScoreSdeVeScheduler()

         snake_case      :				Tuple				=       ScoreSdeVePipeline(unet=A				,      scheduler=A	)
         sde_ve.to(A	)
         sde_ve.set_progress_bar_config(disable=A	)

         snake_case      :				Union[str, Any]				=       torch.manual_seed(0	)
         snake_case      :				Tuple				=       sde_ve(num_inference_steps=2				,      output_type="""numpy"""				,      generator=A	).images

         snake_case      :				Dict				=       torch.manual_seed(0	)
         snake_case      :				Tuple				=       sde_ve(num_inference_steps=2				,      output_type="""numpy"""				,      generator=A				,      return_dict=A	)[
             0
         ]

         snake_case      :				Any				=       image[0, -3:, -3:, -1]
         snake_case      :				Tuple				=       image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         snake_case      :				Any				=       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 	UpperCAmelCase      (      self	)		->				Tuple:
         snake_case      :				List[str]				=       """google/ncsnpp-church-256"""
         snake_case      :				Optional[int]				=       UNetaDModel.from_pretrained(A	)

         snake_case      :				Union[str, Any]				=       ScoreSdeVeScheduler.from_pretrained(A	)

         snake_case      :				Optional[Any]				=       ScoreSdeVePipeline(unet=A				,      scheduler=A	)
         sde_ve.to(A	)
         sde_ve.set_progress_bar_config(disable=A	)

         snake_case      :				int				=       torch.manual_seed(0	)
         snake_case      :				Optional[int]				=       sde_ve(num_inference_steps=1_0				,      output_type="""numpy"""				,      generator=A	).images

         snake_case      :				int				=       image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_5_6, 2_5_6, 3)

         snake_case      :				str				=       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'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  torch.FloatTensor







class A__       (						UpperCAmelCase__ ,     UpperCAmelCase__      ):






      @register_to_config
      def __init__( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:Tuple[str] = ("DownEncoderBlock2D",)     ,						SCREAMING_SNAKE_CASE			:Tuple[str] = ("UpDecoderBlock2D",)     ,						SCREAMING_SNAKE_CASE			:Tuple[int] = (6_4,)     ,						SCREAMING_SNAKE_CASE			:int = 1     ,						SCREAMING_SNAKE_CASE			:str = "silu"     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:int = 3_2     ,						SCREAMING_SNAKE_CASE			:int = 2_5_6     ,						SCREAMING_SNAKE_CASE			:int = 3_2     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:float = 0.18_215     ,						SCREAMING_SNAKE_CASE			:str = "group"     ,						)       ->					Optional[int]:




             '''simple docstring'''
             super().__init__()

             # pass init params to Encoder
             _a    : Union[str, Any]     					=Encoder(
                 in_channels=SCREAMING_SNAKE_CASE     ,						out_channels=SCREAMING_SNAKE_CASE     ,						down_block_types=SCREAMING_SNAKE_CASE     ,						block_out_channels=SCREAMING_SNAKE_CASE     ,						layers_per_block=SCREAMING_SNAKE_CASE     ,						act_fn=SCREAMING_SNAKE_CASE     ,						norm_num_groups=SCREAMING_SNAKE_CASE     ,						double_z=SCREAMING_SNAKE_CASE     ,						)

             _a    : Optional[int]     					=vq_embed_dim if vq_embed_dim is not None else latent_channels

             _a    : Optional[int]     					=nn.Convad(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						1	)
             _a    : str     					=VectorQuantizer(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						beta=0.25     ,						remap=SCREAMING_SNAKE_CASE     ,						sane_index_shape=SCREAMING_SNAKE_CASE	)
             _a    : List[str]     					=nn.Convad(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						1	)

             # pass init params to Decoder
             _a    : List[str]     					=Decoder(
                 in_channels=SCREAMING_SNAKE_CASE     ,						out_channels=SCREAMING_SNAKE_CASE     ,						up_block_types=SCREAMING_SNAKE_CASE     ,						block_out_channels=SCREAMING_SNAKE_CASE     ,						layers_per_block=SCREAMING_SNAKE_CASE     ,						act_fn=SCREAMING_SNAKE_CASE     ,						norm_num_groups=SCREAMING_SNAKE_CASE     ,						norm_type=SCREAMING_SNAKE_CASE     ,						)






      @apply_forward_hook
      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					VQEncoderOutput:




             '''simple docstring'''
             _a    : Optional[int]     					=self.encoder(SCREAMING_SNAKE_CASE	)
             _a    : int     					=self.quant_conv(SCREAMING_SNAKE_CASE	)

             if not return_dict:
                    return (h,)

             return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE	)






      @apply_forward_hook
      def __UpperCAmelCase       ( self			:List[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = False     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					Union[DecoderOutput, torch.FloatTensor]:




             '''simple docstring'''
             # also go through quantization layer
             if not force_not_quantize:
                    _a					,    _a					,    _a    : Tuple     					=self.quantize(SCREAMING_SNAKE_CASE	)
             else:
                    _a    : str     					=h
             _a    : Dict     					=self.post_quant_conv(SCREAMING_SNAKE_CASE	)
             _a    : Union[str, Any]     					=self.decoder(SCREAMING_SNAKE_CASE     ,						quant if self.config.norm_type == """spatial""" else None	)

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					Union[DecoderOutput, torch.FloatTensor]:




             '''simple docstring'''
             _a    : Tuple     					=sample
             _a    : int     					=self.encode(SCREAMING_SNAKE_CASE	).latents
             _a    : List[Any]     					=self.decode(SCREAMING_SNAKE_CASE	).sample

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=SCREAMING_SNAKE_CASE	)


