import importlib

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





def 				UpperCAmelCase						(    lowercase							,			lowercase=False							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase=None							,			lowercase=None							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase=False							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							):







				"""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 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase=True							,			lowercase=True							):







				"""simple docstring"""



				__lowercase 			=							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 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



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

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__a  :     Optional[Any]       =  logging.get_logger(__name__)


__a  :     List[str]       =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = "first_exhausted"							,			):







				"""simple docstring"""



				from .arrow_dataset import Dataset
				from .iterable_dataset import IterableDataset

				if not datasets:
								raise ValueError('''Unable to interleave an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
								raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
				if dataset_type is Dataset:
								return _interleave_map_style_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)
				else:
								return _interleave_iterable_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = 0							,			):







				"""simple docstring"""



				if not dsets:
								raise ValueError('''Unable to concatenate an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if dataset_type is Dataset:
								return _concatenate_map_style_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)
				else:
								return _concatenate_iterable_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)