

from typing import TYPE_CHECKING

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


a_     =			{
    'configuration_longformer': [
        'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LongformerConfig',
        'LongformerOnnxConfig',
    ],
    'tokenization_longformer': ['LongformerTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			['LongformerTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LongformerForMaskedLM',
			    'LongformerForMultipleChoice',
			    'LongformerForQuestionAnswering',
			    'LongformerForSequenceClassification',
			    'LongformerForTokenClassification',
			    'LongformerModel',
			    'LongformerPreTrainedModel',
			    'LongformerSelfAttention',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFLongformerForMaskedLM',
			    'TFLongformerForMultipleChoice',
			    'TFLongformerForQuestionAnswering',
			    'TFLongformerForSequenceClassification',
			    'TFLongformerForTokenClassification',
			    'TFLongformerModel',
			    'TFLongformerPreTrainedModel',
			    'TFLongformerSelfAttention',
			]


if TYPE_CHECKING:
			from .configuration_longformer import (
			    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    LongformerConfig,
			    LongformerOnnxConfig,
			)
			from .tokenization_longformer import LongformerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_longformer_fast import LongformerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_longformer import (
						    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LongformerForMaskedLM,
						    LongformerForMultipleChoice,
						    LongformerForQuestionAnswering,
						    LongformerForSequenceClassification,
						    LongformerForTokenClassification,
						    LongformerModel,
						    LongformerPreTrainedModel,
						    LongformerSelfAttention,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_longformer import (
						    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLongformerForMaskedLM,
						    TFLongformerForMultipleChoice,
						    TFLongformerForQuestionAnswering,
						    TFLongformerForSequenceClassification,
						    TFLongformerForTokenClassification,
						    TFLongformerModel,
						    TFLongformerPreTrainedModel,
						    TFLongformerSelfAttention,
						)

else:
			import sys

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



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

_A		       =						{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					list[bool]				):
     __UpperCamelCase    							=True
     __UpperCamelCase    							=[]

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

     order.append(SCREAMING_SNAKE_CASE__				)

     return order

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					list[bool]				):
     __UpperCamelCase    							=True
     __UpperCamelCase    							=[vert]

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

     return component





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]				):
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) * [False]
     __UpperCamelCase    							={vert: [] for vert in range(len(SCREAMING_SNAKE_CASE__				)				)}

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

     __UpperCamelCase    							=[]
     for i, was_visited in enumerate(SCREAMING_SNAKE_CASE__				):
          if not was_visited:
               order += topology_sort(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=[]
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) * [False]

     for i in range(len(SCREAMING_SNAKE_CASE__				)				):
          __UpperCamelCase    							=order[len(SCREAMING_SNAKE_CASE__				) - i - 1]
          if not visited[vert]:
               __UpperCamelCase    							=find_components(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
               components_list.append(SCREAMING_SNAKE_CASE__				)

     return components_list





