
class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				''''''
           UpperCamelCase__    :   int					=				''''''
           UpperCamelCase__    :   Optional[int]					=				[]






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           if m == -1:
                 return n + 1
           elif n == -1:
                 return m + 1
           elif self.dp[m][n] > -1:
                 return self.dp[m][n]
           else:
                 if self.worda[m] == self.worda[n]:
                       UpperCamelCase__    :   Union[str, Any]					=				self.__min_dist_top_down_dp(m - 1						,						n - 1  )
                 else:
                       UpperCamelCase__    :   int					=				self.__min_dist_top_down_dp(__SCREAMING_SNAKE_CASE						,						n - 1  )
                       UpperCamelCase__    :   Tuple					=				self.__min_dist_top_down_dp(m - 1						,						__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   Optional[Any]					=				self.__min_dist_top_down_dp(m - 1						,						n - 1  )
                       UpperCamelCase__    :   Optional[int]					=				1 + min(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

                 return self.dp[m][n]






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				worda
           UpperCamelCase__    :   Any					=				worda
           UpperCamelCase__    :   List[str]					=				[[-1 for _ in range(len(__SCREAMING_SNAKE_CASE  )  )] for _ in range(len(__SCREAMING_SNAKE_CASE  )  )]

           return self.__min_dist_top_down_dp(len(__SCREAMING_SNAKE_CASE  ) - 1						,						len(__SCREAMING_SNAKE_CASE  ) - 1  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   int					=				worda
           UpperCamelCase__    :   List[str]					=				worda
           UpperCamelCase__    :   int					=				len(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				len(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int					=				[[0 for _ in range(n + 1  )] for _ in range(m + 1  )]

           for i in range(m + 1  ):
                 for j in range(n + 1  ):
                       if i == 0:  # first string is empty
                             UpperCamelCase__    :   List[Any]					=				j
                       elif j == 0:  # second string is empty
                             UpperCamelCase__    :   Union[str, Any]					=				i
                       elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                             UpperCamelCase__    :   List[Any]					=				self.dp[i - 1][j - 1]
                       else:
                             UpperCamelCase__    :   Union[str, Any]					=				self.dp[i][j - 1]
                             UpperCamelCase__    :   str					=				self.dp[i - 1][j]
                             UpperCamelCase__    :   Union[str, Any]					=				self.dp[i - 1][j - 1]
                             UpperCamelCase__    :   List[Any]					=				1 + min(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
           return self.dp[m][n]


if __name__ == "__main__":
    lowerCamelCase      =EditDistance()

    print("****************** Testing Edit Distance DP Algorithm ******************")
    print()

    lowerCamelCase      =input("Enter the first string: ").strip()
    lowerCamelCase      =input("Enter the second string: ").strip()

    print()
    print(F'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
    print(F'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
    print()
    print("*************** End of Testing Edit Distance DP Algorithm ***************")




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase      ={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =["MobileBertTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MobileBertForMaskedLM",
        "MobileBertForMultipleChoice",
        "MobileBertForNextSentencePrediction",
        "MobileBertForPreTraining",
        "MobileBertForQuestionAnswering",
        "MobileBertForSequenceClassification",
        "MobileBertForTokenClassification",
        "MobileBertLayer",
        "MobileBertModel",
        "MobileBertPreTrainedModel",
        "load_tf_weights_in_mobilebert",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMobileBertForMaskedLM",
        "TFMobileBertForMultipleChoice",
        "TFMobileBertForNextSentencePrediction",
        "TFMobileBertForPreTraining",
        "TFMobileBertForQuestionAnswering",
        "TFMobileBertForSequenceClassification",
        "TFMobileBertForTokenClassification",
        "TFMobileBertMainLayer",
        "TFMobileBertModel",
        "TFMobileBertPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mobilebert import (
        MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileBertConfig,
        MobileBertOnnxConfig,
    )
    from .tokenization_mobilebert import MobileBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mobilebert_fast import MobileBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilebert import (
            MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileBertForMaskedLM,
            MobileBertForMultipleChoice,
            MobileBertForNextSentencePrediction,
            MobileBertForPreTraining,
            MobileBertForQuestionAnswering,
            MobileBertForSequenceClassification,
            MobileBertForTokenClassification,
            MobileBertLayer,
            MobileBertModel,
            MobileBertPreTrainedModel,
            load_tf_weights_in_mobilebert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mobilebert import (
            TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMobileBertForMaskedLM,
            TFMobileBertForMultipleChoice,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertMainLayer,
            TFMobileBertModel,
            TFMobileBertPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


