


'''simple docstring'''



import importlib

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


def    lowerCamelCase__   ( A     :    List[str]   ,     A     :    Any=False  ):
						UpperCAmelCase         =			OmegaConf.load(lowercase__  )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase__  )  )  )
						return config


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    str=None   ,     A     :    List[str]=None  ):







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


def    lowerCamelCase__   ( A     :    str   ,     A     :    Tuple  ):
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			model.encode(lowercase__  )
						print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
						UpperCAmelCase         =			model.decode(lowercase__  )
						return xrec


def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    List[Any]=False  ):
						UpperCAmelCase       ,       UpperCAmelCase         =			string.rsplit('''.'''   ,     1  )
						if reload:
												UpperCAmelCase         =			importlib.import_module(lowercase__  )
												importlib.reload(lowercase__  )
						return getattr(importlib.import_module(lowercase__   ,     package=lowercase__  )   ,     cls  )


def    lowerCamelCase__   ( A     :    Union[str, Any]  ):







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


def    lowerCamelCase__   ( A     :    int   ,     A     :    str   ,     A     :    Union[str, Any]=True   ,     A     :    Dict=True  ):
						UpperCAmelCase         =			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__   ( A     :    Tuple   ,     A     :    str   ,     A     :    int   ,     A     :    Optional[Any]  ):







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









'''simple docstring'''



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.generation import DisjunctiveConstraint






@require_torch
class  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				Optional[int]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 4], [1, 2, 3, 4]]
										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )
										self.assertTrue(isinstance(dc.token_ids   ,					lowerCAmelCase   )   )

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )




				def   a__(	self    :				Union[str, Any]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2], [1, 2, 3, 4]]

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint(lowerCAmelCase   )  # fails here




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 3], [1, 2, 4]]

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										UpperCAmelCase         =			stepped is True and completed is False and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										UpperCAmelCase         =			stepped is True and completed is False and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(3   )
										UpperCAmelCase         =			stepped is True and completed is True and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 3]   )





				def   a__(	self    :				int   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(4   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2, 4]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(5   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

										dc.reset()

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.remaining() == 3   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.remaining() == 2   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(5   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.remaining() == 0   )
										self.assertTrue(dc.current_seq == [1, 2, 5]   )





