
"""simple docstring"""

class   _UpperCAmelCase				:




      def __init__(				self				)    ->       str:
          UpperCAmelCase          =       ''
          UpperCAmelCase          =       ''
          UpperCAmelCase          =       []




      def      a_   (				self ,							lowercase_ ,							lowercase_				)    ->       int:
          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          =       self.__min_dist_top_down_dp(m - 1 ,							n - 1				)
              else:
                  UpperCAmelCase          =       self.__min_dist_top_down_dp(lowercase_ ,							n - 1				)
                  UpperCAmelCase          =       self.__min_dist_top_down_dp(m - 1 ,							lowercase_				)
                  UpperCAmelCase          =       self.__min_dist_top_down_dp(m - 1 ,							n - 1				)
                  UpperCAmelCase          =       1 + min(lowercase_ ,							lowercase_ ,							lowercase_				)

              return self.dp[m][n]




      def      a_   (				self ,							lowercase_ ,							lowercase_				)    ->       int:
          UpperCAmelCase          =       worda
          UpperCAmelCase          =       worda
          UpperCAmelCase          =       [[-1 for _ in range(len(lowercase_				)				)] for _ in range(len(lowercase_				)				)]

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




      def      a_   (				self ,							lowercase_ ,							lowercase_				)    ->       int:
          UpperCAmelCase          =       worda
          UpperCAmelCase          =       worda
          UpperCAmelCase          =       len(lowercase_				)
          UpperCAmelCase          =       len(lowercase_				)
          UpperCAmelCase          =       [[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          =       j
                  elif j == 0:  # second string is empty
                      UpperCAmelCase          =       i
                  elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                      UpperCAmelCase          =       self.dp[i - 1][j - 1]
                  else:
                      UpperCAmelCase          =       self.dp[i][j - 1]
                      UpperCAmelCase          =       self.dp[i - 1][j]
                      UpperCAmelCase          =       self.dp[i - 1][j - 1]
                      UpperCAmelCase          =       1 + min(lowercase_ ,							lowercase_ ,							lowercase_				)
          return self.dp[m][n]


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

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

   SCREAMING_SNAKE_CASE_            =    input('''Enter the first string: ''').strip()
   SCREAMING_SNAKE_CASE_            =    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 ***************''')





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



