




import importlib

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






def  snake_case(    __magic_name__			,		__magic_name__=False   )			->							Any:





     '''simple docstring'''


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






def  snake_case(    __magic_name__			,		__magic_name__=None			,		__magic_name__=None   )			->							Tuple:





     '''simple docstring'''


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






def  snake_case(    __magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     lowercase      ,	lowercase      ,	lowercase :       Dict       =			model.encode(__magic_name__   )
     print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
     lowercase :       Optional[Any]       =			model.decode(__magic_name__   )
     return xrec






def  snake_case(    __magic_name__			,		__magic_name__=False   )			->							Any:





     '''simple docstring'''


     lowercase      ,	lowercase :       Dict       =			string.rsplit('''.'''			,		1   )
     if reload:
          lowercase :       Optional[int]       =			importlib.import_module(__magic_name__   )
          importlib.reload(__magic_name__   )
     return getattr(importlib.import_module(__magic_name__			,		package=__magic_name__   )			,		cls   )






def  snake_case(    __magic_name__   )			->							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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__=True			,		__magic_name__=True   )			->							Any:





     '''simple docstring'''


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






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


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





from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Tuple			=      '''xmod'''






  def __init__(  self		:     Optional[Any]				,						_A		:     Union[str, Any]=30_522				,						_A		:     List[Any]=768				,						_A		:     Optional[Any]=12				,						_A		:     Any=12				,						_A		:     Tuple=3_072				,						_A		:     Optional[int]="gelu"				,						_A		:     List[Any]=0.1				,						_A		:     str=0.1				,						_A		:     List[Any]=512				,						_A		:     List[str]=2				,						_A		:     str=0.02				,						_A		:     Any=1E-12				,						_A		:     Union[str, Any]=1				,						_A		:     List[Any]=0				,						_A		:     Dict=2				,						_A		:     int="absolute"				,						_A		:     Dict=True				,						_A		:     int=None				,						_A		:     List[str]=False				,						_A		:     Dict=2				,						_A		:     int=False				,						_A		:     Optional[int]=True				,						_A		:     Any=True				,						_A		:     Optional[int]=("en_XX",)				,						_A		:     Any=None				,						**_A		:     Optional[int]				,						)		->   Union[str, Any]:






       """simple docstring"""







       super().__init__(pad_token_id=_A				,						bos_token_id=_A				,						eos_token_id=_A				,						**_A				)

       lowercase :       Optional[Any]       =			vocab_size
       lowercase :       Union[str, Any]       =			hidden_size
       lowercase :       Optional[Any]       =			num_hidden_layers
       lowercase :       Dict       =			num_attention_heads
       lowercase :       Union[str, Any]       =			hidden_act
       lowercase :       Tuple       =			intermediate_size
       lowercase :       List[str]       =			hidden_dropout_prob
       lowercase :       Union[str, Any]       =			attention_probs_dropout_prob
       lowercase :       Dict       =			max_position_embeddings
       lowercase :       Any       =			type_vocab_size
       lowercase :       Optional[Any]       =			initializer_range
       lowercase :       str       =			layer_norm_eps
       lowercase :       Tuple       =			position_embedding_type
       lowercase :       Optional[Any]       =			use_cache
       lowercase :       int       =			classifier_dropout
       lowercase :       Optional[int]       =			pre_norm
       lowercase :       Any       =			adapter_reduction_factor
       lowercase :       Union[str, Any]       =			adapter_layer_norm
       lowercase :       Optional[int]       =			adapter_reuse_layer_norm
       lowercase :       Optional[Any]       =			ln_before_adapter
       lowercase :       Union[str, Any]       =			list(_A				)
       lowercase :       List[Any]       =			default_language


class 						_A (  _lowerCamelCase				):






  @property
  def  __a      (  self		:     Union[str, Any]				)		->   Mapping[str, Mapping[int, str]]:






       """simple docstring"""







       if self.task == "multiple-choice":
            lowercase :       Any       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            lowercase :       Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]				)