


"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case             =   logging.get_logger(__name__)

__snake_case             =   {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Union[str, Any]							  =  '''yolos'''







   def __init__(     self						,     UpperCamelCase__=768						,     UpperCamelCase__=12						,     UpperCamelCase__=12						,     UpperCamelCase__=3072						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.02						,     UpperCamelCase__=1e-12						,     UpperCamelCase__=[512, 864]						,     UpperCamelCase__=16						,     UpperCamelCase__=3						,     UpperCamelCase__=True						,     UpperCamelCase__=100						,     UpperCamelCase__=True						,     UpperCamelCase__=False						,     UpperCamelCase__=1						,     UpperCamelCase__=5						,     UpperCamelCase__=2						,     UpperCamelCase__=5						,     UpperCamelCase__=2						,     UpperCamelCase__=0.1						,     **UpperCamelCase__						,     )     ->		Tuple:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__						)

       snake_case   :				List[str]				 =   hidden_size
       snake_case   :				Dict				 =   num_hidden_layers
       snake_case   :				str				 =   num_attention_heads
       snake_case   :				List[str]				 =   intermediate_size
       snake_case   :				Tuple				 =   hidden_act
       snake_case   :				Dict				 =   hidden_dropout_prob
       snake_case   :				Tuple				 =   attention_probs_dropout_prob
       snake_case   :				List[str]				 =   initializer_range
       snake_case   :				Union[str, Any]				 =   layer_norm_eps
       snake_case   :				List[str]				 =   image_size
       snake_case   :				Tuple				 =   patch_size
       snake_case   :				Any				 =   num_channels
       snake_case   :				Optional[Any]				 =   qkv_bias
       snake_case   :				Optional[Any]				 =   num_detection_tokens
       snake_case   :				Union[str, Any]				 =   use_mid_position_embeddings
       snake_case   :				int				 =   auxiliary_loss
       # Hungarian matcher
       snake_case   :				List[Any]				 =   class_cost
       snake_case   :				int				 =   bbox_cost
       snake_case   :				Any				 =   giou_cost
       # Loss coefficients
       snake_case   :				List[Any]				 =   bbox_loss_coefficient
       snake_case   :				Optional[Any]				 =   giou_loss_coefficient
       snake_case   :				Dict				 =   eos_coefficient







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	List[Any]							  =  version.parse('''1.11'''     )







   @property
   def 			lowerCamelCase		(     self						)     ->		Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ]						)







   @property
   def 			lowerCamelCase		(     self						)     ->		float:

       '''simple docstring'''




       return 1e-4







   @property
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       return 12









"""simple docstring"""



import importlib

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







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]=False			)     ->						Union[str, Any]:


    """simple docstring"""





    snake_case   :				Dict				 =   OmegaConf.load(lowercase			)
    if display:
        print(yaml.dump(OmegaConf.to_container(lowercase			)			)			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Dict=None						,							lowercase  :  Dict=None			)     ->						Union[str, Any]:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  List[str]			)     ->						List[str]:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  str=False			)     ->						Optional[int]:


    """simple docstring"""





    snake_case ,snake_case   :				Any				 =   string.rsplit("."						,							1			)
    if reload:
        snake_case   :				List[Any]				 =   importlib.import_module(lowercase			)
        importlib.reload(lowercase			)
    return getattr(importlib.import_module(lowercase						,							package=lowercase			)						,							cls			)







def   __lowerCAmelCase						(						lowercase  :  List[str]			)     ->						Union[str, Any]:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  List[str]						,							lowercase  :  Tuple=True						,							lowercase  :  Optional[Any]=True			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   instantiate_from_config(lowercase			)
    if sd is not None:
        model.load_state_dict(lowercase			)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Union[str, Any]			)     ->						Union[str, Any]:


    """simple docstring"""





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





