"""simple docstring"""


def 					__a   (		A    ,				A      ):






					'''simple docstring'''



					lowercase__				 =							len(A      ) + 1
					lowercase__				 =							len(A      ) + 1

					# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
					# length i of input_string matches with prefix string of length j of
					# given pattern.
					# "dp" stands for dynamic programming.
					lowercase__				 =							[[0 for i in range(A      )] for j in range(A      )]

					# since string of zero length match pattern of zero length
					lowercase__				 =							1

					# since pattern of zero length will never match with string of non-zero length
					for i in range(1    ,				A      ):
										lowercase__				 =							0

					# since string of zero length will match with pattern where there
					# is at least one * alternatively
					for j in range(1    ,				A      ):
										lowercase__				 =							dp[0][j - 2] if pattern[j - 1] == "*" else 0

					# now using bottom-up approach to find for all remaining lengths
					for i in range(1    ,				A      ):
										for j in range(1    ,				A      ):
															if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																				lowercase__				 =							dp[i - 1][j - 1]

															elif pattern[j - 1] == "*":
																				if dp[i][j - 2] == 1:
																									lowercase__				 =							1
																				elif pattern[j - 2] in (input_string[i - 1], "."):
																									lowercase__				 =							dp[i - 1][j]
																				else:
																									lowercase__				 =							0
															else:
																				lowercase__				 =							0

					return bool(dp[-1][-1]      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# inputing the strings
	# input_string = input("input a string :")
	# pattern = input("input a pattern :")

	lowerCAmelCase_:					List[str]     =					"aab"
	lowerCAmelCase_:					Any     =					"c*a*b"

	# using function to check whether given string matches the given pattern
	if match_pattern(input_string, pattern):
		print(F'{input_string} matches the given pattern {pattern}')
	else:
		print(F'{input_string} does not match with the given pattern {pattern}')





"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase_:					Union[str, Any]     =					{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Union[str, Any]     =					["DistilBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Any     =					[
     "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DistilBertForMaskedLM",
     "DistilBertForMultipleChoice",
     "DistilBertForQuestionAnswering",
     "DistilBertForSequenceClassification",
     "DistilBertForTokenClassification",
     "DistilBertModel",
     "DistilBertPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Tuple     =					[
     "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDistilBertForMaskedLM",
     "TFDistilBertForMultipleChoice",
     "TFDistilBertForQuestionAnswering",
     "TFDistilBertForSequenceClassification",
     "TFDistilBertForTokenClassification",
     "TFDistilBertMainLayer",
     "TFDistilBertModel",
     "TFDistilBertPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Optional[Any]     =					[
     "FlaxDistilBertForMaskedLM",
     "FlaxDistilBertForMultipleChoice",
     "FlaxDistilBertForQuestionAnswering",
     "FlaxDistilBertForSequenceClassification",
     "FlaxDistilBertForTokenClassification",
     "FlaxDistilBertModel",
     "FlaxDistilBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase_:					Optional[int]     =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




