





from typing import TYPE_CHECKING

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


a__: Optional[int]							   =						{
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[str]							   =						['XLMRobertaTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Optional[Any]							   =						['XLMRobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: int							   =						[
           'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XLMRobertaForCausalLM',
           'XLMRobertaForMaskedLM',
           'XLMRobertaForMultipleChoice',
           'XLMRobertaForQuestionAnswering',
           'XLMRobertaForSequenceClassification',
           'XLMRobertaForTokenClassification',
           'XLMRobertaModel',
           'XLMRobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Optional[int]							   =						[
           'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFXLMRobertaForCausalLM',
           'TFXLMRobertaForMaskedLM',
           'TFXLMRobertaForMultipleChoice',
           'TFXLMRobertaForQuestionAnswering',
           'TFXLMRobertaForSequenceClassification',
           'TFXLMRobertaForTokenClassification',
           'TFXLMRobertaModel',
           'TFXLMRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Dict							   =						[
           'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FlaxXLMRobertaForMaskedLM',
           'FlaxXLMRobertaForCausalLM',
           'FlaxXLMRobertaForMultipleChoice',
           'FlaxXLMRobertaForQuestionAnswering',
           'FlaxXLMRobertaForSequenceClassification',
           'FlaxXLMRobertaForTokenClassification',
           'FlaxXLMRobertaModel',
           'FlaxXLMRobertaPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_xlm_roberta import (
           XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XLMRobertaConfig,
           XLMRobertaOnnxConfig,
       )

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlm_roberta import XLMRobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm_roberta import (
                  XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMRobertaForCausalLM,
                  XLMRobertaForMaskedLM,
                  XLMRobertaForMultipleChoice,
                  XLMRobertaForQuestionAnswering,
                  XLMRobertaForSequenceClassification,
                  XLMRobertaForTokenClassification,
                  XLMRobertaModel,
                  XLMRobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlm_roberta import (
                  TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLMRobertaForCausalLM,
                  TFXLMRobertaForMaskedLM,
                  TFXLMRobertaForMultipleChoice,
                  TFXLMRobertaForQuestionAnswering,
                  TFXLMRobertaForSequenceClassification,
                  TFXLMRobertaForTokenClassification,
                  TFXLMRobertaModel,
                  TFXLMRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xlm_roberta import (
                  FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxXLMRobertaForCausalLM,
                  FlaxXLMRobertaForMaskedLM,
                  FlaxXLMRobertaForMultipleChoice,
                  FlaxXLMRobertaForQuestionAnswering,
                  FlaxXLMRobertaForSequenceClassification,
                  FlaxXLMRobertaForTokenClassification,
                  FlaxXLMRobertaModel,
                  FlaxXLMRobertaPreTrainedModel,
              )

else:
       import sys

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












def       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str    )->str:
       assert x is not None
       assert y is not None

       A__		   =		len(UpperCamelCase__    )
       A__		   =		len(UpperCamelCase__    )

       # declaring the array for storing the dp values
       A__		   =		[[0] * (n + 1) for _ in range(m + 1    )]  # noqa: E741

       for i in range(1					,	m + 1    ):
              for j in range(1					,	n + 1    ):
                     A__		   =		1 if x[i - 1] == y[j - 1] else 0

                     A__		   =		max(l[i - 1][j]					,	l[i][j - 1]					,	l[i - 1][j - 1] + match    )

       A__		   =		''''''
       A__   ,      A__		   =		m, n
       while i > 0 and j > 0:
              A__		   =		1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            A__		   =		x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
       a__: Tuple							   =						'AGGTAB'
       a__: Tuple							   =						'GXTXAYB'
       a__: List[Any]							   =						4
       a__: Optional[Any]							   =						'GTAB'

       a__   ,     a__: List[str]							   =						longest_common_subsequence(a, b)
       print('len =', ln, ', sub-sequence =', subseq)
       import doctest

       doctest.testmod()





