




import importlib

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     Union[str, Any]=False							) ->			int:
      SCREAMING_SNAKE_CASE_     						=      OmegaConf.load(__UpperCAmelCase							)
      if display:
            print(yaml.dump(OmegaConf.to_container(__UpperCAmelCase							)							)							)
      return config

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     str							) ->			str:
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      model.encode(__UpperCAmelCase							)
      print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"							)
      SCREAMING_SNAKE_CASE_     						=      model.decode(__UpperCAmelCase							)
      return xrec

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

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





from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   ""
 lowercase_       =   "hf-legacy"  # "hf://"" is reserved for hffs



 def __init__(							self  :		Optional[int]    ,    _lowerCAmelCase  :		Optional[DatasetInfo] = None    ,    _lowerCAmelCase  :		Optional[str] = None    ,    **_lowerCAmelCase  :		int    ,    ):
       super().__init__(self    ,    **_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      repo_info
       SCREAMING_SNAKE_CASE_     						=      token
       SCREAMING_SNAKE_CASE_     						=      None



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       if self.dir_cache is None:
             SCREAMING_SNAKE_CASE_     						=      {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   SCREAMING_SNAKE_CASE_     						=      {
                       'name': hf_file.rfilename,
                       'size': None,
                       'type': 'file',
                   }
                   self.dir_cache.update(
                       {
                           str(_lowerCAmelCase						): {'name': str(_lowerCAmelCase						), 'size': None, 'type': 'directory'}
                           for d in list(PurePosixPath(hf_file.rfilename						).parents						)[:-1]
                       }						)



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		str = "rb"    ,    **_lowerCAmelCase  :		Optional[Any]    ,    ):
       if not isinstance(self.repo_info    ,    _lowerCAmelCase						):
             raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"						)
       SCREAMING_SNAKE_CASE_     						=      hf_hub_url(self.repo_info.id    ,    _lowerCAmelCase    ,    revision=self.repo_info.sha						)
       return fsspec.open(
           _lowerCAmelCase    ,    mode=_lowerCAmelCase    ,    headers=get_authentication_headers_for_url(_lowerCAmelCase    ,    use_auth_token=self.token						)    ,    client_kwargs={'trust_env': True}    ,    ).open()



 def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		Any    ,    **_lowerCAmelCase  :		Dict						):
       self._get_dirs()
       SCREAMING_SNAKE_CASE_     						=      self._strip_protocol(_lowerCAmelCase						)
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Any=False    ,    **_lowerCAmelCase  :		str						):
       self._get_dirs()
       SCREAMING_SNAKE_CASE_     						=      PurePosixPath(path.strip('/'						)						)
       SCREAMING_SNAKE_CASE_     						=      {}
       for p, f in self.dir_cache.items():
             SCREAMING_SNAKE_CASE_     						=      PurePosixPath(p.strip('/'						)						)
             SCREAMING_SNAKE_CASE_     						=      p.parent
             if root == path:
                   SCREAMING_SNAKE_CASE_     						=      f
       SCREAMING_SNAKE_CASE_     						=      list(paths.values()						)
       if detail:
             return out
       else:
             return sorted(f['name'] for f in out						)