



import importlib

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







def snake_case__   (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=False )		->							Optional[int]:
		UpperCAmelCase_						=				OmegaConf.load(__UpperCamelCase )
		if display:
				print(yaml.dump(OmegaConf.to_container(__UpperCamelCase ) ) )
		return config







def snake_case__   (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None )		->							Any:






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







def snake_case__   (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		UpperCAmelCase_						=				model.encode(__UpperCamelCase )
		print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''' )
		UpperCAmelCase_						=				model.decode(__UpperCamelCase )
		return xrec







def snake_case__   (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=False )		->							Dict:
		UpperCAmelCase_						=				string.rsplit("."			,     1 )
		if reload:
				UpperCAmelCase_						=				importlib.import_module(__UpperCamelCase )
				importlib.reload(__UpperCamelCase )
		return getattr(importlib.import_module(__UpperCamelCase			,     package=__UpperCamelCase )			,     cls )







def snake_case__   (			__SCREAMING_SNAKE_CASE )		->							str:






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







def snake_case__   (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=True )		->							Dict:
		UpperCAmelCase_						=				instantiate_from_config(__UpperCamelCase )
		if sd is not None:
				model.load_state_dict(__UpperCamelCase )
		if gpu:
				model.cuda()
		if eval_mode:
				model.eval()
		return {"model": model}







def snake_case__   (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:






		if ckpt:
				UpperCAmelCase_						=				torch.load(__UpperCamelCase			,     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=__UpperCamelCase			,     eval_mode=__UpperCamelCase )["""model"""]
		return model, global_step






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["XLNetTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["XLNetTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLNetForMultipleChoice",
			    "XLNetForQuestionAnswering",
			    "XLNetForQuestionAnsweringSimple",
			    "XLNetForSequenceClassification",
			    "XLNetForTokenClassification",
			    "XLNetLMHeadModel",
			    "XLNetModel",
			    "XLNetPreTrainedModel",
			    "load_tf_weights_in_xlnet",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXLNetForMultipleChoice",
			    "TFXLNetForQuestionAnsweringSimple",
			    "TFXLNetForSequenceClassification",
			    "TFXLNetForTokenClassification",
			    "TFXLNetLMHeadModel",
			    "TFXLNetMainLayer",
			    "TFXLNetModel",
			    "TFXLNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet import XLNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet_fast import XLNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlnet import (
						    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLNetForMultipleChoice,
						    XLNetForQuestionAnswering,
						    XLNetForQuestionAnsweringSimple,
						    XLNetForSequenceClassification,
						    XLNetForTokenClassification,
						    XLNetLMHeadModel,
						    XLNetModel,
						    XLNetPreTrainedModel,
						    load_tf_weights_in_xlnet,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlnet import (
						    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLNetForMultipleChoice,
						    TFXLNetForQuestionAnsweringSimple,
						    TFXLNetForSequenceClassification,
						    TFXLNetForTokenClassification,
						    TFXLNetLMHeadModel,
						    TFXLNetMainLayer,
						    TFXLNetModel,
						    TFXLNetPreTrainedModel,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

