





import importlib

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


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Dict=False				)->		int:
  _snake_case   :				int														=	OmegaConf.load(__UpperCamelCase				)
  if display:
    print(yaml.dump(OmegaConf.to_container(__UpperCamelCase				)				)				)
  return config


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Optional[int]=None    ,							lowerCAmelCase:	List[str]=None				)->		Optional[Any]:






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


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	str				)->		List[str]:
  _snake_case   :				Union[str, Any]														=	model.encode(__UpperCamelCase				)
  print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""				)
  _snake_case   :				int														=	model.decode(__UpperCamelCase				)
  return xrec


def     lowerCamelCase_	(      lowerCAmelCase:	Dict    ,							lowerCAmelCase:	List[Any]=False				)->		List[Any]:
  _snake_case   :				Tuple														=	string.rsplit('.'    ,							1				)
  if reload:
    _snake_case   :				Tuple														=	importlib.import_module(__UpperCamelCase				)
    importlib.reload(__UpperCamelCase				)
  return getattr(importlib.import_module(__UpperCamelCase    ,							package=__UpperCamelCase				)    ,							cls				)


def     lowerCamelCase_	(      lowerCAmelCase:	Dict				)->		List[str]:






  if "target" not in config:
    raise KeyError('Expected key `target` to instantiate.'				)
  return get_obj_from_str(config['target']				)(**config.get('params'    ,							{}				)				)


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	int=True    ,							lowerCAmelCase:	str=True				)->		str:
  _snake_case   :				int														=	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     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	Dict    ,							lowerCAmelCase:	Dict    ,							lowerCAmelCase:	Tuple				)->		Tuple:






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











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
      'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
      'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
  }
  _snake_case   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




