

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a:    Any 							=			logging.get_logger(__name__)

_a:    Union[str, Any] 							=			{
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}

class 						__UpperCamelCase    (					a__						):
	SCREAMING_SNAKE_CASE__    				=     """xlnet"""
	SCREAMING_SNAKE_CASE__    				=     ["""mems"""]
	SCREAMING_SNAKE_CASE__    				=     {
	    """n_token""": """vocab_size""",  # Backward compatibility
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}




	def __init__(		self	:						Optional[Any]		,	lowerCAmelCase	:						Optional[Any]=32_000		,	lowerCAmelCase	:						List[Any]=1_024		,	lowerCAmelCase	:						Optional[Any]=24		,	lowerCAmelCase	:						Dict=16		,	lowerCAmelCase	:						Any=4_096		,	lowerCAmelCase	:						Optional[Any]="gelu"		,	lowerCAmelCase	:						Optional[Any]=True		,	lowerCAmelCase	:						Any="bi"		,	lowerCAmelCase	:						Tuple=0.02		,	lowerCAmelCase	:						Tuple=1e-12		,	lowerCAmelCase	:						List[str]=0.1		,	lowerCAmelCase	:						List[Any]=512		,	lowerCAmelCase	:						Union[str, Any]=None		,	lowerCAmelCase	:						Tuple=True		,	lowerCAmelCase	:						str=False		,	lowerCAmelCase	:						Union[str, Any]=False		,	lowerCAmelCase	:						Union[str, Any]=-1		,	lowerCAmelCase	:						Any=False		,	lowerCAmelCase	:						Dict="last"		,	lowerCAmelCase	:						List[Any]=True		,	lowerCAmelCase	:						List[str]="tanh"		,	lowerCAmelCase	:						Any=0.1		,	lowerCAmelCase	:						Optional[int]=5		,	lowerCAmelCase	:						str=5		,	lowerCAmelCase	:						Dict=5		,	lowerCAmelCase	:						Union[str, Any]=1		,	lowerCAmelCase	:						Tuple=2		,	**lowerCAmelCase	:						Union[str, Any]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			d_model
		UpperCAmelCase_       				=			n_layer
		UpperCAmelCase_       				=			n_head
		if d_model % n_head != 0:
			raise ValueError(F"\'d_model % n_head\' ({d_model % n_head}) should be equal to 0"							)
		if "d_head" in kwargs:
			if kwargs["d_head"] != d_model // n_head:
				raise ValueError(
				    F"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"							)
		UpperCAmelCase_       				=			d_model // n_head
		UpperCAmelCase_       				=			ff_activation
		UpperCAmelCase_       				=			d_inner
		UpperCAmelCase_       				=			untie_r
		UpperCAmelCase_       				=			attn_type

		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			layer_norm_eps

		UpperCAmelCase_       				=			dropout
		UpperCAmelCase_       				=			mem_len
		UpperCAmelCase_       				=			reuse_len
		UpperCAmelCase_       				=			bi_data
		UpperCAmelCase_       				=			clamp_len
		UpperCAmelCase_       				=			same_length

		UpperCAmelCase_       				=			summary_type
		UpperCAmelCase_       				=			summary_use_proj
		UpperCAmelCase_       				=			summary_activation
		UpperCAmelCase_       				=			summary_last_dropout
		UpperCAmelCase_       				=			start_n_top
		UpperCAmelCase_       				=			end_n_top

		UpperCAmelCase_       				=			bos_token_id
		UpperCAmelCase_       				=			pad_token_id
		UpperCAmelCase_       				=			eos_token_id

		if "use_cache" in kwargs:
			warnings.warn(
			    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
			    " instead."		,	lowercase__		,	)
			UpperCAmelCase_       				=			kwargs['''use_cache''']

		UpperCAmelCase_       				=			use_mems_eval
		UpperCAmelCase_       				=			use_mems_train
		super().__init__(pad_token_id=lowercase__		,	bos_token_id=lowercase__		,	eos_token_id=lowercase__		,	**lowercase__							)




	@property
	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit."							)
		return -1




	@max_position_embeddings.setter
	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		raise NotImplementedError(
		    F"The model {self.model_type} is one of the few models that has no sequence length limit."							)









import importlib

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



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			OmegaConf.load(A    )
	if display:
		print(yaml.dump(OmegaConf.to_container(A    )    )    )
	return config



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



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



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			string.rsplit("."       ,     1    )
	if reload:
		UpperCAmelCase_       				=			importlib.import_module(A    )
		importlib.reload(A    )
	return getattr(importlib.import_module(A       ,     package=A    )       ,     cls    )



def       __lowerCAmelCase      (						A    ):
	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       ,     A       ,     A=True       ,     A=True    ):
	UpperCAmelCase_       				=			instantiate_from_config(A    )
	if sd is not None:
		model.load_state_dict(A    )
	if gpu:
		model.cuda()
	if eval_mode:
		model.eval()
	return {"model": model}




def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	# load the specified checkpoint
	if ckpt:
		UpperCAmelCase_       				=			torch.load(A       ,     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=A       ,     eval_mode=A    )["model"]
	return model, global_step