'''simple docstring'''






class     A__							:  # Public class to implement a graph





   """simple docstring"""





   def __init__( self     :		Union[str, Any]						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		Any						,     lowerCAmelCase__     :		Dict			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   row
      _UpperCAmelCase							:	int							=   col
      _UpperCAmelCase							:	Optional[int]							=   graph




   def 			_lowerCAmelCase		( self     :		Any						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		int			)       ->    Dict:







      """simple docstring"""




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




   def 			_lowerCAmelCase		( self     :		Union[str, Any]						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		Optional[int]						,     lowerCAmelCase__     :		Optional[int]			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
      _UpperCAmelCase							:	Union[str, Any]							=   [-1, 0, 1, -1, 1, -1, 0, 1]
      _UpperCAmelCase							:	Optional[int]							=   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 			_lowerCAmelCase		( self     :		int			)       ->    str:  # And finally, count all islands.







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   [[False for j in range(self.COL			)] for i in range(self.ROW			)]
      _UpperCAmelCase							:	str							=   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



"""simple docstring"""




from typing import TYPE_CHECKING

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


lowercase_			     =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






