"""simple docstring"""






import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
						pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__  :  List[str]						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  Dict						     =	{
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "align_text_model"







					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	str=30_522    ,lowerCamelCase__   :	Tuple=768    ,lowerCamelCase__   :	Optional[Any]=12    ,lowerCamelCase__   :	int=12    ,lowerCamelCase__   :	Union[str, Any]=3_072    ,lowerCamelCase__   :	List[Any]="gelu"    ,lowerCamelCase__   :	str=0.1    ,lowerCamelCase__   :	Dict=0.1    ,lowerCamelCase__   :	Tuple=512    ,lowerCamelCase__   :	int=2    ,lowerCamelCase__   :	Optional[Any]=0.0_2    ,lowerCamelCase__   :	Optional[Any]=1e-12    ,lowerCamelCase__   :	Any=0    ,lowerCamelCase__   :	Optional[int]="absolute"    ,lowerCamelCase__   :	Dict=True    ,**lowerCamelCase__   :	Dict    ,):
									super().__init__(**lowerCamelCase__			)

									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							hidden_size
									UpperCAmelCase__             =							num_hidden_layers
									UpperCAmelCase__             =							num_attention_heads
									UpperCAmelCase__             =							hidden_act
									UpperCAmelCase__             =							intermediate_size
									UpperCAmelCase__             =							hidden_dropout_prob
									UpperCAmelCase__             =							attention_probs_dropout_prob
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							type_vocab_size
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							layer_norm_eps
									UpperCAmelCase__             =							position_embedding_type
									UpperCAmelCase__             =							use_cache
									UpperCAmelCase__             =							pad_token_id







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	Union[str, Any]    ,lowerCamelCase__   :	Union[str, os.PathLike]    ,**lowerCamelCase__   :	List[str]			):
									cls._set_token_in_kwargs(lowerCamelCase__			)

									UpperCAmelCase__      ,	UpperCAmelCase__             =							cls.get_config_dict(lowerCamelCase__    ,**lowerCamelCase__			)

									# get the text config dict if we are loading from AlignConfig
									if config_dict.get('model_type'			) == "align":
													UpperCAmelCase__             =							config_dict['text_config']

									if "model_type" in config_dict and hasattr(cls    ,'model_type'			) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
													    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

									return cls.from_dict(lowerCamelCase__    ,**lowerCamelCase__			)






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "align_vision_model"







					def __init__(				self   :	Any    ,lowerCamelCase__   :	int = 3    ,lowerCamelCase__   :	int = 600    ,lowerCamelCase__   :	float = 2.0    ,lowerCamelCase__   :	float = 3.1    ,lowerCamelCase__   :	int = 8    ,lowerCamelCase__   :	List[int] = [3, 3, 5, 3, 5, 5, 3]    ,lowerCamelCase__   :	List[int] = [32, 16, 24, 40, 80, 112, 192]    ,lowerCamelCase__   :	List[int] = [16, 24, 40, 80, 112, 192, 320]    ,lowerCamelCase__   :	List[int] = []    ,lowerCamelCase__   :	List[int] = [1, 2, 2, 2, 1, 2, 1]    ,lowerCamelCase__   :	List[int] = [1, 2, 2, 3, 3, 4, 1]    ,lowerCamelCase__   :	List[int] = [1, 6, 6, 6, 6, 6, 6]    ,lowerCamelCase__   :	float = 0.2_5    ,lowerCamelCase__   :	str = "swish"    ,lowerCamelCase__   :	int = 2_560    ,lowerCamelCase__   :	str = "mean"    ,lowerCamelCase__   :	float = 0.0_2    ,lowerCamelCase__   :	float = 0.0_0_1    ,lowerCamelCase__   :	float = 0.9_9    ,lowerCamelCase__   :	float = 0.2    ,**lowerCamelCase__   :	Tuple    ,):
									super().__init__(**lowerCamelCase__			)

									UpperCAmelCase__             =							num_channels
									UpperCAmelCase__             =							image_size
									UpperCAmelCase__             =							width_coefficient
									UpperCAmelCase__             =							depth_coefficient
									UpperCAmelCase__             =							depth_divisor
									UpperCAmelCase__             =							kernel_sizes
									UpperCAmelCase__             =							in_channels
									UpperCAmelCase__             =							out_channels
									UpperCAmelCase__             =							depthwise_padding
									UpperCAmelCase__             =							strides
									UpperCAmelCase__             =							num_block_repeats
									UpperCAmelCase__             =							expand_ratios
									UpperCAmelCase__             =							squeeze_expansion_ratio
									UpperCAmelCase__             =							hidden_act
									UpperCAmelCase__             =							hidden_dim
									UpperCAmelCase__             =							pooling_type
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							batch_norm_eps
									UpperCAmelCase__             =							batch_norm_momentum
									UpperCAmelCase__             =							drop_connect_rate
									UpperCAmelCase__             =							sum(lowerCamelCase__			) * 4







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	str    ,lowerCamelCase__   :	Union[str, os.PathLike]    ,**lowerCamelCase__   :	Optional[int]			):
									cls._set_token_in_kwargs(lowerCamelCase__			)

									UpperCAmelCase__      ,	UpperCAmelCase__             =							cls.get_config_dict(lowerCamelCase__    ,**lowerCamelCase__			)

									# get the vision config dict if we are loading from AlignConfig
									if config_dict.get('model_type'			) == "align":
													UpperCAmelCase__             =							config_dict['vision_config']

									if "model_type" in config_dict and hasattr(cls    ,'model_type'			) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
													    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

									return cls.from_dict(lowerCamelCase__    ,**lowerCamelCase__			)






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "align"
					snake_case__				    = True







					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	List[str]=None    ,lowerCamelCase__   :	Union[str, Any]=None    ,lowerCamelCase__   :	int=640    ,lowerCamelCase__   :	List[Any]=1.0    ,lowerCamelCase__   :	Optional[Any]=0.0_2    ,**lowerCamelCase__   :	str    ,):
									super().__init__(**lowerCamelCase__			)

									if text_config is None:
													UpperCAmelCase__             =							{}
													logger.info('text_config is None. Initializing the AlignTextConfig with default values.'			)

									if vision_config is None:
													UpperCAmelCase__             =							{}
													logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'			)

									UpperCAmelCase__             =							AlignTextConfig(**lowerCamelCase__			)
									UpperCAmelCase__             =							AlignVisionConfig(**lowerCamelCase__			)

									UpperCAmelCase__             =							projection_dim
									UpperCAmelCase__             =							temperature_init_value
									UpperCAmelCase__             =							initializer_range







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	str    ,lowerCamelCase__   :	AlignTextConfig    ,lowerCamelCase__   :	AlignVisionConfig    ,**lowerCamelCase__   :	Optional[int]			):
									return cls(text_config=text_config.to_dict()    ,vision_config=vision_config.to_dict()    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	str			):
									UpperCAmelCase__             =							copy.deepcopy(self.__dict__			)
									UpperCAmelCase__             =							self.text_config.to_dict()
									UpperCAmelCase__             =							self.vision_config.to_dict()
									UpperCAmelCase__             =							self.__class__.model_type
									return output




"""simple docstring"""






import functools



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							len(lowerCamelCase       )
				UpperCAmelCase__             =							len(lowerCamelCase       )

				@functools.cache
				def min_distance(lowerCamelCase					,			lowerCamelCase       ) -> int:
								# if first word index is overflow - delete all from the second word
								if indexa >= len_worda:
												return len_worda - indexa
								# if second word index is overflow - delete all from the first word
								if indexa >= len_worda:
												return len_worda - indexa
								UpperCAmelCase__             =							int(worda[indexa] != worda[indexa]       )  # current letters not identical
								return min(
								    1 + min_distance(indexa + 1					,			lowerCamelCase       )					,			1 + min_distance(lowerCamelCase					,			indexa + 1       )					,			diff + min_distance(indexa + 1					,			indexa + 1       )					,			)

				return min_distance(0					,			0       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



