import importlib

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







def       A__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		List[Any]=False   ):
 SCREAMING_SNAKE_CASE__: Optional[int]=   OmegaConf.load(snake_case_   )
 if display:
  print(yaml.dump(OmegaConf.to_container(snake_case_   )   )   )
 return config







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







def       A__   (						snake_case_			:		Optional[int]							,					snake_case_			:		Optional[Any]   ):
 SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[Any]=   model.encode(snake_case_   )
 print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'   )
 SCREAMING_SNAKE_CASE__: Any=   model.decode(snake_case_   )
 return xrec







def       A__   (						snake_case_			:		List[Any]							,					snake_case_			:		List[str]=False   ):
 SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[int]=   string.rsplit('''.'''							,					1   )
 if reload:
  SCREAMING_SNAKE_CASE__: int=   importlib.import_module(snake_case_   )
  importlib.reload(snake_case_   )
 return getattr(importlib.import_module(snake_case_							,					package=snake_case_   )							,					cls   )







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







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





__a	 =      256
# Modulus to hash a string
__a	 =      100_0003
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   if p_len > t_len:
      return False

   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  1

   # Calculating the hash of pattern and substring of text
   for i in range(lowerCAmelCase_ ):
      UpperCAmelCase      					=  (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
      UpperCAmelCase      					=  (ord(text[i] ) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      UpperCAmelCase      					=  (modulus_power * alphabet_size) % modulus

   for i in range(0	,    t_len - p_len + 1 ):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      UpperCAmelCase      					=  (
          (text_hash - ord(text[i] ) * modulus_power) * alphabet_size
          + ord(text[i + p_len] )
      ) % modulus
   return False
def 	_UpperCamelCase					(    )    ->None:
   UpperCAmelCase      					=  """abc1abc12"""
   UpperCAmelCase      					=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
   UpperCAmelCase      					=  """alskfjaldsk23adsfabcabc"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ ) and not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 2)
   UpperCAmelCase      					=  """ABABX"""
   UpperCAmelCase      					=  """ABABZABABYABABX"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 3)
   UpperCAmelCase      					=  """AAAB"""
   UpperCAmelCase      					=  """ABAAAAAB"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 4)
   UpperCAmelCase      					=  """abcdabcy"""
   UpperCAmelCase      					=  """abcxabcdabxabcdabcdabcy"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 5)
   UpperCAmelCase      					=  """Lü"""
   UpperCAmelCase      					=  """Lüsai"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  """Lue"""
   assert not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   print("""Success.""" )


if __name__ == "__main__":
 test_rabin_karp()



