

'''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 						A  (		self      :	Any )	->    List[Any]:





       '''simple docstring'''






       torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






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





       '''simple docstring'''






       torch.manual_seed(0 )
       _SCREAMING_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							,					)
       return CLIPTextModel(_a )





  def 						A  (		self      :	List[str] )	->    Tuple:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							    =LDMPipeline(unet=_a							,					vqvae=_a							,					scheduler=_a )
       ldm.to(_a )
       ldm.set_progress_bar_config(disable=_a )

       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =ldm(generator=_a							,					num_inference_steps=2							,					output_type='numpy' ).images

       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =ldm(generator=_a							,					num_inference_steps=2							,					output_type='numpy'							,					return_dict=_a )[0]

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

       assert image.shape == (1, 64, 64, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72] )
       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	str )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
       ldm.to(_a )
       ldm.set_progress_bar_config(disable=_a )

       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =ldm(generator=_a							,					num_inference_steps=5							,					output_type='numpy' ).images

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

       assert image.shape == (1, 256, 256, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47] )
       _SCREAMING_SNAKE_CASE							    =1e-2 if torch_device != 'mps' else 3e-2

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



'''simple docstring'''
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =VQModel(**config.model.params				)
     if ckpt_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.pt'
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """simple docstring"""




     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params'							, {}				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =instantiate_from_config(_UpperCamelCase				)
     if sd is not None:
          model.load_state_dict(_UpperCamelCase				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
          _SCREAMING_SNAKE_CASE							    =pl_sd['global_step']
          print(f"loaded model from global step {global_step}."				)
     else:
          _SCREAMING_SNAKE_CASE							    ={'state_dict': None}
          _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =load_model_from_config(config.model							, pl_sd['state_dict']							, gpu=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
