'''simple docstring'''




from typing import TYPE_CHECKING

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


_A								=   {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BertForMaskedLM""",
     """BertForMultipleChoice""",
     """BertForNextSentencePrediction""",
     """BertForPreTraining""",
     """BertForQuestionAnswering""",
     """BertForSequenceClassification""",
     """BertForTokenClassification""",
     """BertLayer""",
     """BertLMHeadModel""",
     """BertModel""",
     """BertPreTrainedModel""",
     """load_tf_weights_in_bert""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFBertEmbeddings""",
     """TFBertForMaskedLM""",
     """TFBertForMultipleChoice""",
     """TFBertForNextSentencePrediction""",
     """TFBertForPreTraining""",
     """TFBertForQuestionAnswering""",
     """TFBertForSequenceClassification""",
     """TFBertForTokenClassification""",
     """TFBertLMHeadModel""",
     """TFBertMainLayer""",
     """TFBertModel""",
     """TFBertPreTrainedModel""",
 ]
try:
 if not is_tensorflow_text_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   ["""TFBertTokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     """FlaxBertForCausalLM""",
     """FlaxBertForMaskedLM""",
     """FlaxBertForMultipleChoice""",
     """FlaxBertForNextSentencePrediction""",
     """FlaxBertForPreTraining""",
     """FlaxBertForQuestionAnswering""",
     """FlaxBertForSequenceClassification""",
     """FlaxBertForTokenClassification""",
     """FlaxBertModel""",
     """FlaxBertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
 from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_fast import BertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bert import (
      BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BertForMaskedLM,
      BertForMultipleChoice,
      BertForNextSentencePrediction,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      BertForTokenClassification,
      BertLayer,
      BertLMHeadModel,
      BertModel,
      BertPreTrainedModel,
      load_tf_weights_in_bert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_bert import (
      TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFBertEmbeddings,
      TFBertForMaskedLM,
      TFBertForMultipleChoice,
      TFBertForNextSentencePrediction,
      TFBertForPreTraining,
      TFBertForQuestionAnswering,
      TFBertForSequenceClassification,
      TFBertForTokenClassification,
      TFBertLMHeadModel,
      TFBertMainLayer,
      TFBertModel,
      TFBertPreTrainedModel,
  )

 try:
  if not is_tensorflow_text_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_tf import TFBertTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_bert import (
      FlaxBertForCausalLM,
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
      FlaxBertPreTrainedModel,
  )

else:
 import sys

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





'''simple docstring'''




from __future__ import annotations

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




