


"""simple docstring"""



import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__snake_case             =   logging.get_logger(__name__)

enable_full_determinism()







class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	int							  =  UNetaDModel
   __UpperCAmelCase			:	Union[str, Any]							  =  '''sample'''







   @property
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				str				 =   4
       snake_case   :				Optional[int]				 =   3
       snake_case   :				Any				 =   (32, 32)

       snake_case   :				Any				 =   floats_tensor((batch_size, num_channels) + sizes						).to(UpperCamelCase__						)
       snake_case   :				Any				 =   torch.tensor([10]						).to(UpperCamelCase__						)

       return {"sample": noise, "timestep": time_step}







   @property
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       return (3, 32, 32)







   @property
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       return (3, 32, 32)







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   {
           "block_out_channels": (32, 64),
           "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
           "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
           "attention_head_dim": 3,
           "out_channels": 3,
           "in_channels": 3,
           "layers_per_block": 2,
           "sample_size": 32,
       }
       snake_case   :				int				 =   self.dummy_input
       return init_dict, inputs_dict







class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	str							  =  UNetaDModel
   __UpperCAmelCase			:	Dict							  =  '''sample'''







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   4
       snake_case   :				Tuple				 =   4
       snake_case   :				List[str]				 =   (32, 32)

       snake_case   :				Tuple				 =   floats_tensor((batch_size, num_channels) + sizes						).to(UpperCamelCase__						)
       snake_case   :				Tuple				 =   torch.tensor([10]						).to(UpperCamelCase__						)

       return {"sample": noise, "timestep": time_step}







   @property
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       return (4, 32, 32)







   @property
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       return (4, 32, 32)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   {
           "sample_size": 32,
           "in_channels": 4,
           "out_channels": 4,
           "layers_per_block": 2,
           "block_out_channels": (32, 64),
           "attention_head_dim": 32,
           "down_block_types": ("DownBlock2D", "DownBlock2D"),
           "up_block_types": ("UpBlock2D", "UpBlock2D"),
       }
       snake_case   :				str				 =   self.dummy_input
       return init_dict, inputs_dict







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case ,snake_case   :				List[Any]				 =   UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,     output_loading_info=UpperCamelCase__						)

       self.assertIsNotNone(UpperCamelCase__						)
       self.assertEqual(len(loading_info["missing_keys"]						)						,     0						)

       model.to(UpperCamelCase__						)
       snake_case   :				str				 =   model(**self.dummy_input						).sample

       assert image is not None, "Make sure output is not None"







   @unittest.skipIf(torch_device != "cuda"						,     "This test is supposed to run on GPU"						)
   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case ,snake_case   :				Optional[int]				 =   UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,     output_loading_info=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   model(**self.dummy_input						).sample

       assert image is not None, "Make sure output is not None"







   @unittest.skipIf(torch_device != "cuda"						,     "This test is supposed to run on GPU"						)
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       snake_case ,snake_case   :				Union[str, Any]				 =   UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,     output_loading_info=UpperCamelCase__						)
       model_accelerate.to(UpperCamelCase__						)
       model_accelerate.eval()

       snake_case   :				List[str]				 =   torch.randn(
           1						,     model_accelerate.config.in_channels						,     model_accelerate.config.sample_size						,     model_accelerate.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
       snake_case   :				str				 =   noise.to(UpperCamelCase__						)
       snake_case   :				Tuple				 =   torch.tensor([10] * noise.shape[0]						).to(UpperCamelCase__						)

       snake_case   :				Any				 =   model_accelerate(UpperCamelCase__						,     UpperCamelCase__						)["sample"]

       # two models don't need to stay in the device at the same time
       del model_accelerate
       torch.cuda.empty_cache()
       gc.collect()

       snake_case ,snake_case   :				Union[str, Any]				 =   UNetaDModel.from_pretrained(
           "fusing/unet-ldm-dummy-update"						,     output_loading_info=UpperCamelCase__						,     low_cpu_mem_usage=UpperCamelCase__						)
       model_normal_load.to(UpperCamelCase__						)
       model_normal_load.eval()
       snake_case   :				Optional[int]				 =   model_normal_load(UpperCamelCase__						,     UpperCamelCase__						)["sample"]

       assert torch_all_close(UpperCamelCase__						,     UpperCamelCase__						,     rtol=1e-3						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						)
       model.eval()
       model.to(UpperCamelCase__						)

       snake_case   :				List[str]				 =   torch.randn(
           1						,     model.config.in_channels						,     model.config.sample_size						,     model.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
       snake_case   :				Any				 =   noise.to(UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   torch.tensor([10] * noise.shape[0]						).to(UpperCamelCase__						)

       with torch.no_grad():
           snake_case   :				Tuple				 =   model(UpperCamelCase__						,     UpperCamelCase__						).sample

       snake_case   :				Optional[Any]				 =   output[0, -1, -3:, -3:].flatten().cpu()
       # fmt: off
       snake_case   :				int				 =   torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]						)
       # fmt: on

       self.assertTrue(torch_all_close(UpperCamelCase__						,     UpperCamelCase__						,     rtol=1e-3						)						)







class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	Any							  =  UNetaDModel
   __UpperCAmelCase			:	Any							  =  '''sample'''







   @property
   def 			lowerCamelCase		(     self						,     UpperCamelCase__=(32, 32)						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[str]				 =   4
       snake_case   :				Tuple				 =   3

       snake_case   :				int				 =   floats_tensor((batch_size, num_channels) + sizes						).to(UpperCamelCase__						)
       snake_case   :				Tuple				 =   torch.tensor(batch_size * [10]						).to(dtype=torch.intaa						,     device=UpperCamelCase__						)

       return {"sample": noise, "timestep": time_step}







   @property
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       return (3, 32, 32)







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

       '''simple docstring'''




       return (3, 32, 32)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   {
           "block_out_channels": [32, 64, 64, 64],
           "in_channels": 3,
           "layers_per_block": 1,
           "out_channels": 3,
           "time_embedding_type": "fourier",
           "norm_eps": 1e-6,
           "mid_block_scale_factor": math.sqrt(2.0						),
           "norm_num_groups": None,
           "down_block_types": [
               "SkipDownBlock2D",
               "AttnSkipDownBlock2D",
               "SkipDownBlock2D",
               "SkipDownBlock2D",
           ],
           "up_block_types": [
               "SkipUpBlock2D",
               "SkipUpBlock2D",
               "AttnSkipUpBlock2D",
               "SkipUpBlock2D",
           ],
       }
       snake_case   :				Optional[Any]				 =   self.dummy_input
       return init_dict, inputs_dict







   @slow
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case ,snake_case   :				Tuple				 =   UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"						,     output_loading_info=UpperCamelCase__						)
       self.assertIsNotNone(UpperCamelCase__						)
       self.assertEqual(len(loading_info["missing_keys"]						)						,     0						)

       model.to(UpperCamelCase__						)
       snake_case   :				int				 =   self.dummy_input
       snake_case   :				Optional[int]				 =   floats_tensor((4, 3) + (256, 256)						).to(UpperCamelCase__						)
       snake_case   :				int				 =   noise
       snake_case   :				Dict				 =   model(**UpperCamelCase__						)

       assert image is not None, "Make sure output is not None"







   @slow
   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				Dict				 =   UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"						)
       model.to(UpperCamelCase__						)

       snake_case   :				Union[str, Any]				 =   4
       snake_case   :				Union[str, Any]				 =   3
       snake_case   :				List[str]				 =   (256, 256)

       snake_case   :				Dict				 =   torch.ones((batch_size, num_channels) + sizes						).to(UpperCamelCase__						)
       snake_case   :				Tuple				 =   torch.tensor(batch_size * [1e-4]						).to(UpperCamelCase__						)

       with torch.no_grad():
           snake_case   :				List[Any]				 =   model(UpperCamelCase__						,     UpperCamelCase__						).sample

       snake_case   :				int				 =   output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       snake_case   :				Dict				 =   torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]						)
       # fmt: on

       self.assertTrue(torch_all_close(UpperCamelCase__						,     UpperCamelCase__						,     rtol=1e-2						)						)







   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				str				 =   UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"						)
       model.to(UpperCamelCase__						)

       snake_case   :				Any				 =   4
       snake_case   :				Tuple				 =   3
       snake_case   :				Any				 =   (32, 32)

       snake_case   :				Optional[int]				 =   torch.ones((batch_size, num_channels) + sizes						).to(UpperCamelCase__						)
       snake_case   :				Optional[int]				 =   torch.tensor(batch_size * [1e-4]						).to(UpperCamelCase__						)

       with torch.no_grad():
           snake_case   :				int				 =   model(UpperCamelCase__						,     UpperCamelCase__						).sample

       snake_case   :				Any				 =   output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       snake_case   :				Optional[Any]				 =   torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]						)
       # fmt: on

       self.assertTrue(torch_all_close(UpperCamelCase__						,     UpperCamelCase__						,     rtol=1e-2						)						)







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       pass









"""simple docstring"""



import importlib

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







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]=False			)     ->						Union[str, Any]:


    """simple docstring"""





    snake_case   :				Dict				 =   OmegaConf.load(lowercase			)
    if display:
        print(yaml.dump(OmegaConf.to_container(lowercase			)			)			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Dict=None						,							lowercase  :  Dict=None			)     ->						Union[str, Any]:


    """simple docstring"""





    if conf_path is None:
        snake_case   :				Optional[Any]				 =   "./model_checkpoints/vqgan_only.yaml"
    snake_case   :				Union[str, Any]				 =   load_config(lowercase						,							display=lowercase			)
    snake_case   :				List[Any]				 =   VQModel(**config.model.params			)
    if ckpt_path is None:
        snake_case   :				Optional[int]				 =   "./model_checkpoints/vqgan_only.pt"
    snake_case   :				Union[str, Any]				 =   torch.load(lowercase						,							map_location=lowercase			)
    if ".ckpt" in ckpt_path:
        snake_case   :				Union[str, Any]				 =   sd["state_dict"]
    model.load_state_dict(lowercase						,							strict=lowercase			)
    model.to(lowercase			)
    del sd
    return model







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  List[str]			)     ->						List[str]:


    """simple docstring"""





    snake_case ,snake_case ,snake_case   :				List[Any]				 =   model.encode(lowercase			)
    print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'			)
    snake_case   :				Union[str, Any]				 =   model.decode(lowercase			)
    return xrec







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  str=False			)     ->						Optional[int]:


    """simple docstring"""





    snake_case ,snake_case   :				Any				 =   string.rsplit("."						,							1			)
    if reload:
        snake_case   :				List[Any]				 =   importlib.import_module(lowercase			)
        importlib.reload(lowercase			)
    return getattr(importlib.import_module(lowercase						,							package=lowercase			)						,							cls			)







def   __lowerCAmelCase						(						lowercase  :  List[str]			)     ->						Union[str, Any]:


    """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						(						lowercase  :  Tuple						,							lowercase  :  List[str]						,							lowercase  :  Tuple=True						,							lowercase  :  Optional[Any]=True			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   instantiate_from_config(lowercase			)
    if sd is not None:
        model.load_state_dict(lowercase			)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Union[str, Any]			)     ->						Union[str, Any]:


    """simple docstring"""





    if ckpt:
        snake_case   :				Dict				 =   torch.load(lowercase						,							map_location="cpu"			)
        snake_case   :				Any				 =   pl_sd["global_step"]
        print(F'loaded model from global step {global_step}.'			)
    else:
        snake_case   :				Any				 =   {"state_dict": None}
        snake_case   :				List[str]				 =   None
    snake_case   :				Dict				 =   load_model_from_config(config.model						,							pl_sd["state_dict"]						,							gpu=lowercase						,							eval_mode=lowercase			)["model"]
    return model, global_step





