


"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       Tuple:

							'''simple docstring'''



							def wrapper(*_UpperCamelCase		:      Union[str, Any]       ,			**_UpperCamelCase		:      List[Any]							):
														__UpperCAmelCase   :						List[Any]      							=					timeit.default_timer()
														__UpperCAmelCase   :						Dict      							=					func(*SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_							)
														__UpperCAmelCase   :						Optional[Any]      							=					timeit.default_timer() - starttime
														return delta

							__UpperCAmelCase   :						str      							=					func.__name__

							return wrapper
def      lowerCamelCase       (     _UpperCamelCase		:      dict       ,			_UpperCamelCase		:      List[Any]=1_0_0       ,			_UpperCamelCase		:      Union[str, Any]=None							)		->       Dict:

							'''simple docstring'''



							__UpperCAmelCase   :						List[str]      							=					[]
							__UpperCAmelCase   :						Optional[int]      							=					seq_shapes or {}
							for i in range(SCREAMING_SNAKE_CASE_							):
														__UpperCAmelCase   :						List[Any]      							=					{}
														for col_id, (k, v) in enumerate(features.items()							):
																					if isinstance(SCREAMING_SNAKE_CASE_       ,			_ArrayXD							):
																												__UpperCAmelCase   :						Union[str, Any]      							=					np.random.rand(*v.shape							).astype(v.dtype							)
																					elif isinstance(SCREAMING_SNAKE_CASE_       ,			datasets.Value							):
																												if v.dtype == "string":
																																			__UpperCAmelCase   :						Union[str, Any]      							=					"""The small grey turtle was surprisingly fast when challenged."""
																												else:
																																			__UpperCAmelCase   :						List[str]      							=					np.random.randint(1_0       ,			size=1							).astype(v.dtype							).item()
																					elif isinstance(SCREAMING_SNAKE_CASE_       ,			datasets.Sequence							):
																												while isinstance(SCREAMING_SNAKE_CASE_       ,			datasets.Sequence							):
																																			__UpperCAmelCase   :						Tuple      							=					v.feature
																												__UpperCAmelCase   :						List[Any]      							=					seq_shapes[k]
																												__UpperCAmelCase   :						int      							=					np.random.rand(*SCREAMING_SNAKE_CASE_							).astype(v.dtype							)
																					__UpperCAmelCase   :						Dict      							=					data

														dummy_data.append((i, example)							)

							return dummy_data
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Dict=1_0_0       ,			_UpperCamelCase		:      Tuple=None							)		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					generate_examples(SCREAMING_SNAKE_CASE_       ,			num_examples=SCREAMING_SNAKE_CASE_       ,			seq_shapes=SCREAMING_SNAKE_CASE_							)

							with ArrowWriter(features=SCREAMING_SNAKE_CASE_       ,			path=SCREAMING_SNAKE_CASE_							) as writer:
														for key, record in dummy_data:
																					__UpperCAmelCase   :						Union[str, Any]      							=					features.encode_example(SCREAMING_SNAKE_CASE_							)
																					writer.write(SCREAMING_SNAKE_CASE_							)

														__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					writer.finalize()

							if not num_final_examples == num_examples:
														raise ValueError(
														    f'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''							)

							__UpperCAmelCase   :						Union[str, Any]      							=					datasets.Dataset.from_file(filename=SCREAMING_SNAKE_CASE_       ,			info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_							)							)

							return dataset




"""simple docstring"""




import importlib

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						str:
						SCREAMING_SNAKE_CASE													=					OmegaConf.load(SCREAMING_SNAKE_CASE_  )
						if display:
												print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_  )  )  )
						return config


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=None  )   ->						Any:
						if conf_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.yaml'
						SCREAMING_SNAKE_CASE													=					load_config(SCREAMING_SNAKE_CASE_ ,			display=SCREAMING_SNAKE_CASE_  )
						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(SCREAMING_SNAKE_CASE_ ,			map_location=SCREAMING_SNAKE_CASE_  )
						if ".ckpt" in ckpt_path:
												SCREAMING_SNAKE_CASE													=					sd['state_dict']
						model.load_state_dict(SCREAMING_SNAKE_CASE_ ,			strict=SCREAMING_SNAKE_CASE_  )
						model.to(SCREAMING_SNAKE_CASE_  )
						del sd
						return model


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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					string.rsplit('.' ,			1  )
						if reload:
												SCREAMING_SNAKE_CASE													=					importlib.import_module(SCREAMING_SNAKE_CASE_  )
												importlib.reload(SCREAMING_SNAKE_CASE_  )
						return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_ ,			package=SCREAMING_SNAKE_CASE_  ) ,			cls  )


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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				List[Any]=True ,			SCREAMING_SNAKE_CASE_		:				int=True  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					instantiate_from_config(SCREAMING_SNAKE_CASE_  )
						if sd is not None:
												model.load_state_dict(SCREAMING_SNAKE_CASE_  )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						Any:
						# load the specified checkpoint
						if ckpt:
												SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			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=SCREAMING_SNAKE_CASE_ ,			eval_mode=SCREAMING_SNAKE_CASE_  )['model']
						return model, global_step


