
'''simple docstring'''





import importlib

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

def      A (       A_							:					str    ,							A_							:					Optional[int]=False       ):
						snake_case       :     List[Any] =    OmegaConf.load(A_       )
						if display:
												print(yaml.dump(OmegaConf.to_container(A_       )       )       )
						return config

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

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

def      A (       A_							:					int    ,							A_							:					Optional[Any]=False       ):
						snake_case,					snake_case       :     List[str] =    string.rsplit('''.'''    ,							1       )
						if reload:
												snake_case       :     Union[str, Any] =    importlib.import_module(A_       )
												importlib.reload(A_       )
						return getattr(importlib.import_module(A_    ,							package=A_       )    ,							cls       )

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

def      A (       A_							:					List[Any]    ,							A_							:					int    ,							A_							:					Tuple=True    ,							A_							:					Dict=True       ):
						snake_case       :     int =    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      A (       A_							:					List[Any]    ,							A_							:					Any    ,							A_							:					Union[str, Any]    ,							A_							:					Dict       ):
						# load the specified checkpoint
						if ckpt:
												snake_case       :     Optional[int] =    torch.load(A_    ,							map_location='''cpu'''       )
												snake_case       :     Tuple =    pl_sd['''global_step''']
												print(F"""loaded model from global step {global_step}."""       )
						else:
												snake_case       :     Optional[Any] =    {'''state_dict''': None}
												snake_case       :     int =    None
						snake_case       :     List[Any] =    load_model_from_config(config.model    ,							pl_sd['''state_dict''']    ,							gpu=A_    ,							eval_mode=A_       )['''model''']
						return model, global_step



'''simple docstring'''





import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase    					=   "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase    					=   direct_transformers_import(PATH_TO_TRANSFORMERS)

UpperCAmelCase    					=   transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase    					=   re.compile(r"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


UpperCAmelCase    					=   {
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}

def      A (       A_							:					Union[str, Any]       ):
						snake_case       :     Tuple =    None

						# source code of `config_class`
						snake_case       :     Union[str, Any] =    inspect.getsource(A_       )
						snake_case       :     List[Any] =    _re_checkpoint.findall(A_       )

						# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
						# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
						for ckpt_name, ckpt_link in checkpoints:
												# allow the link to end with `/`
												if ckpt_link.endswith('''/'''       ):
																		snake_case       :     Optional[int] =    ckpt_link[:-1]

												# verify the checkpoint name corresponds to the checkpoint link
												snake_case       :     Dict =    F"""https://huggingface.co/{ckpt_name}"""
												if ckpt_link == ckpt_link_from_name:
																		snake_case       :     Dict =    ckpt_name
																		break

						return checkpoint

def      A (       ):
						snake_case       :     List[str] =    []

						for config_class in list(CONFIG_MAPPING.values()       ):
												# Skip deprecated models
												if "models.deprecated" in config_class.__module__:
																		continue
												snake_case       :     Tuple =    get_checkpoint_from_config_class(A_       )

												snake_case       :     Optional[Any] =    config_class.__name__
												if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
																		configs_without_checkpoint.append(A_       )

						if len(A_       ) > 0:
												snake_case       :     Optional[int] =    '''\n'''.join(sorted(A_       )       )
												raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}"""       )


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()

