





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
      from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline











import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       Optional[Union[str, Path]]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=True
  a_							:       Optional[int]      					=None
  a_							:       int      					=1
  a_							:       Optional[Union[str, bool]]      					=None
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    return self.__class__(**{k: copy.deepcopy(UpperCamelCase							) for k, v in self.__dict__.items()}							)




