





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_	:					str			  =					{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	:					List[Any]			  =					["XLNetTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	:					Optional[Any]			  =					["XLNetTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	:					Dict			  =					[
        "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XLNetForMultipleChoice",
        "XLNetForQuestionAnswering",
        "XLNetForQuestionAnsweringSimple",
        "XLNetForSequenceClassification",
        "XLNetForTokenClassification",
        "XLNetLMHeadModel",
        "XLNetModel",
        "XLNetPreTrainedModel",
        "load_tf_weights_in_xlnet",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	:					Union[str, Any]			  =					[
        "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFXLNetForMultipleChoice",
        "TFXLNetForQuestionAnsweringSimple",
        "TFXLNetForSequenceClassification",
        "TFXLNetForTokenClassification",
        "TFXLNetLMHeadModel",
        "TFXLNetMainLayer",
        "TFXLNetModel",
        "TFXLNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xlnet import XLNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xlnet_fast import XLNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlnet import (
            XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLNetForMultipleChoice,
            XLNetForQuestionAnswering,
            XLNetForQuestionAnsweringSimple,
            XLNetForSequenceClassification,
            XLNetForTokenClassification,
            XLNetLMHeadModel,
            XLNetModel,
            XLNetPreTrainedModel,
            load_tf_weights_in_xlnet,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlnet import (
            TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLNetForMultipleChoice,
            TFXLNetForQuestionAnsweringSimple,
            TFXLNetForSequenceClassification,
            TFXLNetForTokenClassification,
            TFXLNetLMHeadModel,
            TFXLNetMainLayer,
            TFXLNetModel,
            TFXLNetPreTrainedModel,
        )

else:
    import sys

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







class  A__       :  # Public class to implement a graph





      def __init__(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       row
       UpperCamelCase     : Any	     =       col
       UpperCamelCase     : Optional[Any]	     =       graph





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       return (
           0 <= i < self.ROW
           and 0 <= j < self.COL
           and not visited[i][j]
           and self.graph[i][j]
       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
       UpperCamelCase     : Dict	     =       [-1, 0, 1, -1, 1, -1, 0, 1]
       UpperCamelCase     : Any	     =       True  # Make those cells visited
       for k in range(8       ):
        if self.is_safe(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       ):
         self.diffs(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       )





      def 	__UpperCamelCase(			self       ):  # And finally, count all islands.
       '''simple docstring'''




       UpperCamelCase     : str	     =       [[False for j in range(self.COL       )] for i in range(self.ROW       )]
       UpperCamelCase     : int	     =       0
       for i in range(self.ROW       ):
        for j in range(self.COL       ):
         if visited[i][j] is False and self.graph[i][j] == 1:
          self.diffs(A_	,							A_	,							A_       )
          count += 1
       return count
