






from collections import deque


def __A    (      __lowerCAmelCase	)->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						_UpperCAmelCase									=			deque()
						_UpperCAmelCase									=			[False for _ in range(__lowerCAmelCase	)]
						_UpperCAmelCase									=			[-1 for _ in range(__lowerCAmelCase	)]
						_UpperCAmelCase									=			index_of[:]

						def strong_connect(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	):
												_UpperCAmelCase									=			index  # the number when this node is seen
												_UpperCAmelCase									=			index  # lowest rank node reachable from here
												index += 1
												stack.append(__lowerCAmelCase	)
												_UpperCAmelCase									=			True

												for w in g[v]:
																		if index_of[w] == -1:
																								_UpperCAmelCase									=			strong_connect(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
																								_UpperCAmelCase									=			(
																								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																								)
																		elif on_stack[w]:
																								_UpperCAmelCase									=			(
																								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																								)

												if lowlink_of[v] == index_of[v]:
																		_UpperCAmelCase									=			[]
																		_UpperCAmelCase									=			stack.pop()
																		_UpperCAmelCase									=			False
																		component.append(__lowerCAmelCase	)
																		while w != v:
																								_UpperCAmelCase									=			stack.pop()
																								_UpperCAmelCase									=			False
																								component.append(__lowerCAmelCase	)
																		components.append(__lowerCAmelCase	)
												return index

						_UpperCAmelCase									=			[]
						for v in range(__lowerCAmelCase	):
												if index_of[v] == -1:
																		strong_connect(__lowerCAmelCase ,		0 ,		__lowerCAmelCase	)

						return components


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			[[] for _ in range(__lowerCAmelCase	)]
						for u, v in edges:
												g[u].append(__lowerCAmelCase	)
						return g


if __name__ == "__main__":
		# Test
		_a      	=  7
		_a      	=  [0, 0, 1, 2, 3, 3, 4, 4, 6]
		_a      	=  [1, 3, 2, 0, 1, 4, 5, 6, 5]
		_a      	=  [(u, v) for u, v in zip(source, target)]
		_a      	=  create_graph(n_vertices, edges)

		assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE							):




  @register_to_config
  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = False							,   )  -> Tuple:
        '''simple docstring'''






        super().__init__()

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)
        _lowercase	       =False

        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)

        _lowercase	       =TaConfig(
            vocab_size=lowerCAmelCase							,   d_model=lowerCAmelCase							,   num_heads=lowerCAmelCase							,   d_kv=lowerCAmelCase							,   d_ff=lowerCAmelCase							,   dropout_rate=lowerCAmelCase							,   feed_forward_proj=lowerCAmelCase							,   is_decoder=lowerCAmelCase							,   is_encoder_decoder=lowerCAmelCase							,   )

        _lowercase	       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase					):
              _lowercase	       =TaBlock(lowerCAmelCase					)
              self.encoders.append(lowerCAmelCase					)

        _lowercase	       =TaLayerNorm(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.token_embedder(lowerCAmelCase					)

        _lowercase	       =encoder_input_tokens.shape[1]
        _lowercase	       =torch.arange(lowerCAmelCase							,   device=encoder_input_tokens.device					)
        x += self.position_encoding(lowerCAmelCase					)

        _lowercase	       =self.dropout_pre(lowerCAmelCase					)

        # inverted the attention mask
        _lowercase	       =encoder_input_tokens.size()
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase					)

        for lyr in self.encoders:
              _lowercase	       =lyr(lowerCAmelCase							,   lowerCAmelCase					)[0]
        _lowercase	       =self.layer_norm(lowerCAmelCase					)

        return self.dropout_post(lowerCAmelCase					), encoder_inputs_mask


