

from typing import TYPE_CHECKING

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


_a:    Any 							=			{
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    Any 							=			["""ConvBertTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    List[str] 							=			[
				    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """ConvBertForMaskedLM""",
				    """ConvBertForMultipleChoice""",
				    """ConvBertForQuestionAnswering""",
				    """ConvBertForSequenceClassification""",
				    """ConvBertForTokenClassification""",
				    """ConvBertLayer""",
				    """ConvBertModel""",
				    """ConvBertPreTrainedModel""",
				    """load_tf_weights_in_convbert""",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    Union[str, Any] 							=			[
				    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFConvBertForMaskedLM""",
				    """TFConvBertForMultipleChoice""",
				    """TFConvBertForQuestionAnswering""",
				    """TFConvBertForSequenceClassification""",
				    """TFConvBertForTokenClassification""",
				    """TFConvBertLayer""",
				    """TFConvBertModel""",
				    """TFConvBertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
				from .tokenization_convbert import ConvBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_convbert_fast import ConvBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convbert import (
								    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvBertForMaskedLM,
								    ConvBertForMultipleChoice,
								    ConvBertForQuestionAnswering,
								    ConvBertForSequenceClassification,
								    ConvBertForTokenClassification,
								    ConvBertLayer,
								    ConvBertModel,
								    ConvBertPreTrainedModel,
								    load_tf_weights_in_convbert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_convbert import (
								    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFConvBertForMaskedLM,
								    TFConvBertForMultipleChoice,
								    TFConvBertForQuestionAnswering,
								    TFConvBertForSequenceClassification,
								    TFConvBertForTokenClassification,
								    TFConvBertLayer,
								    TFConvBertModel,
								    TFConvBertPreTrainedModel,
								)


else:
				import sys

				_a:    Optional[int] 							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


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