



"""simple docstring"""

import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
	import jax
	import jax.numpy as jnp
	from flax.jax_utils import replicate
	from flax.training.common_utils import shard




@slow
@require_flax
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().tearDown()
													gc.collect()



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Any									=       FlaxStableDiffusionPipeline.from_pretrained(
													    """stabilityai/stable-diffusion-2"""						,     revision="""bf16"""						,     dtype=jnp.bfloataa						,     )

													__a		:		List[str]									=       """A painting of a squirrel eating a burger"""
													__a		:		Any									=       jax.device_count()
													__a		:		List[str]									=       num_samples * [prompt]
													__a		:		Optional[int]									=       sd_pipe.prepare_inputs(_lowercase							)

													__a		:		Optional[Any]									=       replicate(_lowercase							)
													__a		:		Any									=       shard(_lowercase							)

													__a		:		List[Any]									=       jax.random.PRNGKey(0							)
													__a		:		Dict									=       jax.random.split(_lowercase						,     jax.device_count()							)

													__a		:		List[str]									=       sd_pipe(_lowercase						,     _lowercase						,     _lowercase						,     num_inference_steps=25						,     jit=_lowercase							)[0]
													assert images.shape == (jax.device_count(), 1, 768, 768, 3)

													__a		:		Optional[int]									=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
													__a		:		int									=       images[0, 253:256, 253:256, -1]

													__a		:		Union[str, Any]									=       jnp.asarray(jax.device_get(image_slice.flatten()							)							)
													__a		:		str									=       jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]							)
													print(F'''output_slice: {output_slice}'''							)
													assert jnp.abs(output_slice - expected_slice							).max() < 1e-2





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       """stabilityai/stable-diffusion-2"""
													__a							,     __a		:		int									=       FlaxDPMSolverMultistepScheduler.from_pretrained(_lowercase						,     subfolder="""scheduler"""							)
													__a							,     __a		:		Any									=       FlaxStableDiffusionPipeline.from_pretrained(
													    _lowercase						,     scheduler=_lowercase						,     revision="""bf16"""						,     dtype=jnp.bfloataa						,     )
													__a		:		List[str]									=       scheduler_params

													__a		:		List[str]									=       """A painting of a squirrel eating a burger"""
													__a		:		List[str]									=       jax.device_count()
													__a		:		Dict									=       num_samples * [prompt]
													__a		:		str									=       sd_pipe.prepare_inputs(_lowercase							)

													__a		:		Dict									=       replicate(_lowercase							)
													__a		:		Tuple									=       shard(_lowercase							)

													__a		:		Tuple									=       jax.random.PRNGKey(0							)
													__a		:		Dict									=       jax.random.split(_lowercase						,     jax.device_count()							)

													__a		:		Union[str, Any]									=       sd_pipe(_lowercase						,     _lowercase						,     _lowercase						,     num_inference_steps=25						,     jit=_lowercase							)[0]
													assert images.shape == (jax.device_count(), 1, 768, 768, 3)

													__a		:		Optional[Any]									=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
													__a		:		Any									=       images[0, 253:256, 253:256, -1]

													__a		:		List[Any]									=       jnp.asarray(jax.device_get(image_slice.flatten()							)							)
													__a		:		List[str]									=       jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]							)
													print(F'''output_slice: {output_slice}'''							)
													assert jnp.abs(output_slice - expected_slice							).max() < 1e-2






"""simple docstring"""

import importlib

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





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





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





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





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





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

