





lowercase      :       List[Any]									=  2_5_6
# Modulus to hash a string
lowercase      :       Any									=  1_0_0_0_0_0_3



def A_	(		A__		,      A__				)  ->		bool:
       a__						:	List[str] =				len(A__				)
       a__						:	List[Any] =				len(A__				)
       if p_len > t_len:
              return False

       a__						:	Union[str, Any] =				0
       a__						:	Union[str, Any] =				0
       a__						:	Tuple =				1

       # Calculating the hash of pattern and substring of text
       for i in range(A__				):
              a__						:	Any =				(ord(pattern[i]				) + p_hash * alphabet_size) % modulus
              a__						:	int =				(ord(text[i]				) + text_hash * alphabet_size) % modulus
              if i == p_len - 1:
                     continue
              a__						:	List[Any] =				(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
              a__						:	Optional[int] =				(
                  (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
                  + ord(text[i + p_len]				)
              ) % modulus
       return False



def A_	(		)  ->		None:
       a__						:	str =				'abc1abc12'
       a__						:	Optional[Any] =				'alskfjaldsabc1abc1abc12k23adsfabcabc'
       a__						:	List[Any] =				'alskfjaldsk23adsfabcabc'
       assert rabin_karp(A__		,      A__				) and not rabin_karp(A__		,      A__				)

       # Test 2)
       a__						:	int =				'ABABX'
       a__						:	int =				'ABABZABABYABABX'
       assert rabin_karp(A__		,      A__				)

       # Test 3)
       a__						:	List[Any] =				'AAAB'
       a__						:	Optional[Any] =				'ABAAAAAB'
       assert rabin_karp(A__		,      A__				)

       # Test 4)
       a__						:	List[str] =				'abcdabcy'
       a__						:	Optional[int] =				'abcxabcdabxabcdabcdabcy'
       assert rabin_karp(A__		,      A__				)

       # Test 5)
       a__						:	Optional[Any] =				'Lü'
       a__						:	Tuple =				'Lüsai'
       assert rabin_karp(A__		,      A__				)
       a__						:	Dict =				'Lue'
       assert not rabin_karp(A__		,      A__				)
       print('Success.'				)


if __name__ == "__main__":
    test_rabin_karp()




"""simple docstring"""




import importlib

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


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


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


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]=False  ) ->				int:
       lowercase		,    lowercase    :				Union[str, Any]       		=							string.rsplit("."     , 1  )
       if reload:
              lowercase    :				Any       		=							importlib.import_module(__snake_case  )
              importlib.reload(__snake_case  )
       return getattr(importlib.import_module(__snake_case     , package=__snake_case  )     , cls  )


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


