



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 						UpperCAmelCase_					( a     ,      a     ,      a):







							@register_to_config
							def __init__(     self,    __a,    __a,    __a,    __a,    __a,    __a,    __a,    __a,    __a,    __a = False,    ):

														'''simple docstring'''

														super().__init__()

														_lowerCAmelCase  :							Union[str, Any]				     =					nn.Embedding(__a,    __a)

														_lowerCAmelCase  :							Optional[Any]				     =					nn.Embedding(__a,    __a)
														_lowerCAmelCase  :							int				     =					False

														_lowerCAmelCase  :							List[str]				     =					nn.Dropout(p=__a)

														_lowerCAmelCase  :							str				     =					TaConfig(
														    vocab_size=__a,    d_model=__a,    num_heads=__a,    d_kv=__a,    d_ff=__a,    dropout_rate=__a,    feed_forward_proj=__a,    is_decoder=__a,    is_encoder_decoder=__a,    )

														_lowerCAmelCase  :							Tuple				     =					nn.ModuleList()
														for lyr_num in range(__a):
																					_lowerCAmelCase  :							Union[str, Any]				     =					TaBlock(__a)
																					self.encoders.append(__a)

														_lowerCAmelCase  :							Any				     =					TaLayerNorm(__a)
														_lowerCAmelCase  :							Any				     =					nn.Dropout(p=__a)







							def      snake_case__   (     self,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					self.token_embedder(__a)

														_lowerCAmelCase  :							Tuple				     =					encoder_input_tokens.shape[1]
														_lowerCAmelCase  :							Dict				     =					torch.arange(__a,    device=encoder_input_tokens.device)
														x += self.position_encoding(__a)

														_lowerCAmelCase  :							Dict				     =					self.dropout_pre(__a)

														# inverted the attention mask
														_lowerCAmelCase  :							Optional[Any]				     =					encoder_input_tokens.size()
														_lowerCAmelCase  :							Optional[Any]				     =					self.get_extended_attention_mask(__a,    __a)

														for lyr in self.encoders:
																					_lowerCAmelCase  :							Tuple				     =					lyr(__a,    __a)[0]
														_lowerCAmelCase  :							str				     =					self.layer_norm(__a)

														return self.dropout_post(__a), encoder_inputs_mask





import importlib

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=None       ,				_lowerCamelCase=None						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					string.rsplit("."       ,				1						)
							if reload:
														_lowerCAmelCase  :							Dict				     =					importlib.import_module(_lowerCamelCase						)
														importlib.reload(_lowerCamelCase						)
							return getattr(importlib.import_module(_lowerCamelCase       ,				package=_lowerCamelCase						)       ,				cls						)






def        A       (      _lowerCamelCase						):





							'''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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=True       ,				_lowerCamelCase=True						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if ckpt:
														_lowerCAmelCase  :							Optional[int]				     =					torch.load(_lowerCamelCase       ,				map_location="cpu"						)
														_lowerCAmelCase  :							int				     =					pl_sd["global_step"]
														print(F"loaded model from global step {global_step}."						)
							else:
														_lowerCAmelCase  :							Optional[int]				     =					{"state_dict": None}
														_lowerCAmelCase  :							Any				     =					None
							_lowerCAmelCase  :							Optional[int]				     =					load_model_from_config(config.model       ,				pl_sd["state_dict"]       ,				gpu=_lowerCamelCase       ,				eval_mode=_lowerCamelCase						)["model"]
							return model, global_step
