
"""simple docstring"""






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 				a__					(						_UpperCamelCase       ):
     UpperCAmelCase__         =      ''''''
     UpperCAmelCase__         =      '''hf-legacy'''  # "hf://"" is reserved for hffs



     def __init__(							self :Union[str, Any]      ,  _lowerCamelCase :List[Any] = None      ,  _lowerCamelCase :Any = None      ,  **_lowerCamelCase :str      ,  ):







      '''simple docstring'''



      super().__init__(self      ,  **lowercase__   )
      UpperCamelCase_     :					List[Any]   	=repo_info
      UpperCamelCase_     :					List[Any]   	=token
      UpperCamelCase_     :					List[Any]   	=None



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :int = "rb"      ,  **_lowerCamelCase :List[Any]      ,  ):







      '''simple docstring'''



      if not isinstance(self.repo_info      ,  lowercase__   ):
       raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''   )
      UpperCamelCase_     :					str   	=hf_hub_url(self.repo_info.id      ,  lowercase__      ,  revision=self.repo_info.sha   )
      return fsspec.open(
          lowercase__      ,  mode=lowercase__      ,  headers=get_authentication_headers_for_url(lowercase__      ,  use_auth_token=self.token   )      ,  client_kwargs={'trust_env': True}      ,  ).open()



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :int      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					int   	=self._strip_protocol(lowercase__   )
      if path in self.dir_cache:
       return self.dir_cache[path]
      else:
       raise FileNotFoundError(lowercase__   )




     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :Dict=False      ,  **_lowerCamelCase :List[str]   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					Optional[Any]   	=PurePosixPath(path.strip('/'   )   )
      UpperCamelCase_     :					Any   	={}
      for p, f in self.dir_cache.items():
       UpperCamelCase_     :					str   	=PurePosixPath(p.strip('/'   )   )
       UpperCamelCase_     :					Optional[int]   	=p.parent
       if root == path:
        UpperCamelCase_     :					List[str]   	=f
      UpperCamelCase_     :					Tuple   	=list(paths.values()   )
      if detail:
       return out
      else:
       return sorted(f['name'] for f in out   )




import importlib

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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	OmegaConf.load(lowercase_      )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase_      )      )      )
						return config




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	model.encode(lowercase_      )
						print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"      )
						__UpperCAmelCase			:							List[str]	    =	model.decode(lowercase_      )
						return xrec




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     int:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=True      ,       lowercase_=True      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							Dict	    =	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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







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

