

import importlib

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







def 						lowerCamelCase__      ( __A    :str	,__A    :int=False		):


       """simple docstring"""




       __snake_case       	=							OmegaConf.load(__A		)
       if display:
              print(yaml.dump(OmegaConf.to_container(__A		)		)		)
       return config







def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :List[Any]=None	,__A    :List[str]=None		):


       """simple docstring"""




       if conf_path is None:
              __snake_case       	=							"""./model_checkpoints/vqgan_only.yaml"""
       __snake_case       	=							load_config(__A	,display=__A		)
       __snake_case       	=							VQModel(**config.model.params		)
       if ckpt_path is None:
              __snake_case       	=							"""./model_checkpoints/vqgan_only.pt"""
       __snake_case       	=							torch.load(__A	,map_location=__A		)
       if ".ckpt" in ckpt_path:
              __snake_case       	=							sd["""state_dict"""]
       model.load_state_dict(__A	,strict=__A		)
       model.to(__A		)
       del sd
       return model







def 						lowerCamelCase__      ( __A    :Dict	,__A    :Dict		):


       """simple docstring"""




       __snake_case	,    __snake_case	,    __snake_case       	=							model.encode(__A		)
       print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'		)
       __snake_case       	=							model.decode(__A		)
       return xrec







def 						lowerCamelCase__      ( __A    :List[Any]	,__A    :str=False		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							string.rsplit("""."""	,1		)
       if reload:
              __snake_case       	=							importlib.import_module(__A		)
              importlib.reload(__A		)
       return getattr(importlib.import_module(__A	,package=__A		)	,cls		)







def 						lowerCamelCase__      ( __A    :str		):


       """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 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :str	,__A    :List[Any]=True	,__A    :Any=True		):


       """simple docstring"""




       __snake_case       	=							instantiate_from_config(__A		)
       if sd is not None:
              model.load_state_dict(__A		)
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}







def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :List[Any]	,__A    :List[str]	,__A    :Tuple		):


       """simple docstring"""




       if ckpt:
              __snake_case       	=							torch.load(__A	,map_location="""cpu"""		)
              __snake_case       	=							pl_sd["""global_step"""]
              print(F'loaded model from global step {global_step}.'		)
       else:
              __snake_case       	=							{"""state_dict""": None}
              __snake_case       	=							None
       __snake_case       	=							load_model_from_config(config.model	,pl_sd["""state_dict"""]	,gpu=__A	,eval_mode=__A		)["""model"""]
       return model, global_step




from __future__ import annotations

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							text, pattern
              __snake_case	,    __snake_case       	=							len(_UpperCamelCase   ), len(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              for i in range(self.patLen - 1      , -1      , -1   ):
                     if char == self.pattern[i]:
                            return i
              return -1


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              for i in range(self.patLen - 1      , -1      , -1   ):
                     if self.pattern[i] != self.text[current_pos + i]:
                            return current_pos + i
              return -1






       def 			a		(		self   )      ->							list[int]:






              """simple docstring"""


              __snake_case       	=							[]
              for i in range(self.textLen - self.patLen + 1   ):
                     __snake_case       	=							self.mismatch_in_text(_UpperCamelCase   )
                     if mismatch_index == -1:
                            positions.append(_UpperCamelCase   )
                     else:
                            __snake_case       	=							self.match_in_pattern(self.text[mismatch_index]   )
                            __snake_case       	=							(
                                mismatch_index - match_index
                            )  # shifting index lgtm [py/multiple-definition]
              return positions






UpperCamelCase__		 =     '''ABAABA'''
UpperCamelCase__		 =     '''AB'''
UpperCamelCase__		 =     BoyerMooreSearch(text, pattern)
UpperCamelCase__		 =     bms.bad_character_heuristic()

if len(positions) == 0:
    print('''No match found''')
else:
    print('''Pattern found in following positions: ''')
    print(positions)

