'''simple docstring'''

import argparse
import os
import re


A_     :						Dict    							='''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
A_     :						Any    							=re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
A_     :						List[Any]    							=re.compile(R'''\s*\(\s*"(\S[^"]+)"''')



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							bool = False)			->					Union[str, Any]:
   with open(__snake_case	,   '''r'''	,   encoding='''utf-8''') as f:
      lowerCAmelCase_			=      f.read()

   lowerCAmelCase_			=      content.split('''\n''')
   lowerCAmelCase_			=      []
   lowerCAmelCase_			=      0
   while line_idx < len(__snake_case):
      if _re_intro_mapping.search(lines[line_idx]) is not None:
         lowerCAmelCase_			=      len(re.search(R'''^(\s*)\S'''	,   lines[line_idx]).groups()[0]) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(''' ''' * indent + '''('''):
            new_lines.append(lines[line_idx])
            line_idx += 1

         lowerCAmelCase_			=      []
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               lowerCAmelCase_			=      line_idx
               while not lines[line_idx].startswith(''' ''' * indent + ''')'''):
                  line_idx += 1
               blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]))
            else:
               blocks.append(lines[line_idx])
            line_idx += 1

         # Sort blocks by their identifiers
         lowerCAmelCase_			=      sorted(__snake_case	,   key=lambda __snake_case: _re_identifier.search(__snake_case).groups()[0])
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx])
         line_idx += 1

   if overwrite:
      with open(__snake_case	,   '''w'''	,   encoding='''utf-8''') as f:
         f.write('''\n'''.join(__snake_case))
   elif "\n".join(__snake_case) != content:
      return True



def snake_case_      (					__snake_case  :							bool = False)			->					List[Any]:
   lowerCAmelCase_			=      [os.path.join(__snake_case	,   __snake_case) for f in os.listdir(__snake_case) if f.endswith('''.py''')]
   lowerCAmelCase_			=      [sort_auto_mapping(__snake_case	,   overwrite=__snake_case) for fname in fnames]

   if not overwrite and any(__snake_case):
      lowerCAmelCase_			=      [f for f, d in zip(__snake_case	,   __snake_case) if d]
      raise ValueError(
          F'''The following files have auto mappings that need sorting: {', '.join(__snake_case)}. Run `make style` to fix'''
          ''' this.''')


if __name__ == "__main__":
 A_     :						Optional[Any]    							=argparse.ArgumentParser()
 parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
 A_     :						List[Any]    							=parser.parse_args()

 sort_all_auto_mappings(not args.check_only)






'''simple docstring'''

import importlib

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



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



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



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



def snake_case_      (					__snake_case  :							Tuple)			->					Union[str, Any]:
   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_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							str)			->					Any:
   # load the specified checkpoint
   if ckpt:
      lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
      lowerCAmelCase_			=      pl_sd['''global_step''']
      print(F'''loaded model from global step {global_step}.''')
   else:
      lowerCAmelCase_			=      {'''state_dict''': None}
      lowerCAmelCase_			=      None
   lowerCAmelCase_			=      load_model_from_config(config.model	,   pl_sd['''state_dict''']	,   gpu=__snake_case	,   eval_mode=__snake_case)['''model''']
   return model, global_step





