






"""simple docstring"""
import importlib

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

def        snake_case   (     A__			,A__=False		):
			UpperCAmelCase_  :				int      =      OmegaConf.load(__UpperCamelCase		)
			if display:
						print(yaml.dump(OmegaConf.to_container(__UpperCamelCase		)		)		)
			return config

def        snake_case   (     A__			,A__=None			,A__=None		):






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

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      model.encode(__UpperCamelCase		)
			print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""		)
			UpperCAmelCase_  :				int      =      model.decode(__UpperCamelCase		)
			return xrec

def        snake_case   (     A__			,A__=False		):
			UpperCAmelCase_  :				Tuple      =      string.rsplit("."			,1		)
			if reload:
						UpperCAmelCase_  :				Tuple      =      importlib.import_module(__UpperCamelCase		)
						importlib.reload(__UpperCamelCase		)
			return getattr(importlib.import_module(__UpperCamelCase			,package=__UpperCamelCase		)			,cls		)

def        snake_case   (     A__		):






			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   (     A__			,A__			,A__=True			,A__=True		):
			UpperCAmelCase_  :				int      =      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   (     A__			,A__			,A__			,A__		):






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











"""simple docstring"""
from typing import TYPE_CHECKING

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


lowerCamelCase_       			=   {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''FunnelTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FunnelBaseModel''',
			    '''FunnelForMaskedLM''',
			    '''FunnelForMultipleChoice''',
			    '''FunnelForPreTraining''',
			    '''FunnelForQuestionAnswering''',
			    '''FunnelForSequenceClassification''',
			    '''FunnelForTokenClassification''',
			    '''FunnelModel''',
			    '''FunnelPreTrainedModel''',
			    '''load_tf_weights_in_funnel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFFunnelBaseModel''',
			    '''TFFunnelForMaskedLM''',
			    '''TFFunnelForMultipleChoice''',
			    '''TFFunnelForPreTraining''',
			    '''TFFunnelForQuestionAnswering''',
			    '''TFFunnelForSequenceClassification''',
			    '''TFFunnelForTokenClassification''',
			    '''TFFunnelModel''',
			    '''TFFunnelPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

			lowerCamelCase_       			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



