
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   UpperCAmelCase		(						unittest.TestCase ):



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Union[str, Any]:


          '''simple docstring'''


          torch.manual_seed(0 )
          snake_case       :   Dict							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	int:


          '''simple docstring'''


          torch.manual_seed(0 )
          snake_case       :   Optional[Any]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	List[Any]:


          '''simple docstring'''


          torch.manual_seed(0 )
          snake_case       :   int							=      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(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Dict:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.dummy_uncond_unet
          snake_case       :   List[Any]							=      DDIMScheduler()
          snake_case       :   Optional[Any]							=      self.dummy_vq_model

          snake_case       :   str							=      LDMPipeline(unet=snake_case__    ,						vqvae=snake_case__    ,						scheduler=snake_case__ )
          ldm.to(snake_case__ )
          ldm.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   Tuple							=      torch.manual_seed(0 )
          snake_case       :   Any							=      ldm(generator=snake_case__    ,						num_inference_steps=2    ,						output_type="numpy" ).images

          snake_case       :   List[Any]							=      torch.manual_seed(0 )
          snake_case       :   Tuple							=      ldm(generator=snake_case__    ,						num_inference_steps=2    ,						output_type="numpy"    ,						return_dict=snake_case__ )[0]

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

          assert image.shape == (1, 64, 64, 3)
          snake_case       :   int							=      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
          snake_case       :   Any							=      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   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	str:


          '''simple docstring'''


          snake_case       :   int							=      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256" )
          ldm.to(snake_case__ )
          ldm.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   List[str]							=      torch.manual_seed(0 )
          snake_case       :   Any							=      ldm(generator=snake_case__    ,						num_inference_steps=5    ,						output_type="numpy" ).images

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

          assert image.shape == (1, 2_56, 2_56, 3)
          snake_case       :   List[str]							=      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447] )
          snake_case       :   Optional[int]							=      1e-2 if torch_device != "mps" else 3e-2

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






'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

       def __init__(						self	,   **__lowerCAmelCase )  ->  int:
        super().__init__(**__lowerCAmelCase )
        assert (
            "encoder" in kwargs and "decoder" in kwargs
        ), "Config has to be initialized with encoder and decoder config"
        lowercase__				:    Optional[int]  =					kwargs.pop('''encoder''' )
        lowercase__				:    Union[str, Any]  =					encoder_config.pop('''model_type''' )
        lowercase__				:    Any  =					kwargs.pop('''decoder''' )
        lowercase__				:    Any  =					decoder_config.pop('''model_type''' )

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

        return cls(encoder=encoder_config.to_dict()	,   decoder=decoder_config.to_dict()	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

