






from typing import TYPE_CHECKING

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


A_						:						Optional[Any]						      =					{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_						:						int						      =					['RoFormerTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_						:						Any						      =					[
       '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:
   A_						:						Optional[int]						      =					[
       '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:
   A_						:						Optional[Any]						      =					[
       '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

   A_						:						List[str]						      =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



snake_case							:       Any										=	{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

snake_case							:       Any										=	{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def 							__lowerCamelCase		(					UpperCAmelCase_		:						dict[int, list[int]]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						list[bool]		):




      """simple docstring"""





      a		:Any									=			True
      a		:Any									=			[]

      for neighbour in graph[vert]:
            if not visited[neighbour]:
                  order += topology_sort(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      order.append(UpperCAmelCase_		)

      return order
def 							__lowerCamelCase		(					UpperCAmelCase_		:						dict[int, list[int]]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						list[bool]		):




      """simple docstring"""





      a		:Optional[Any]									=			True
      a		:int									=			[vert]

      for neighbour in reversed_graph[vert]:
            if not visited[neighbour]:
                  component += find_components(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      return component






def 							__lowerCamelCase		(					UpperCAmelCase_		:						dict[int, list[int]]		):




      """simple docstring"""





      a		:int									=			len(UpperCAmelCase_		) * [False]
      a		:dict[int, list[int]]									=			{vert: [] for vert in range(len(UpperCAmelCase_		)		)}

      for vert, neighbours in graph.items():
            for neighbour in neighbours:
                  reversed_graph[neighbour].append(UpperCAmelCase_		)

      a		:Optional[Any]									=			[]
      for i, was_visited in enumerate(UpperCAmelCase_		):
            if not was_visited:
                  order += topology_sort(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      a		:Tuple									=			[]
      a		:int									=			len(UpperCAmelCase_		) * [False]

      for i in range(len(UpperCAmelCase_		)		):
            a		:Union[str, Any]									=			order[len(UpperCAmelCase_		) - i - 1]
            if not visited[vert]:
                  a		:Union[str, Any]									=			find_components(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)
                  components_list.append(UpperCAmelCase_		)

      return components_list





