



from collections import deque


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 return input(UpperCAmelCase__       ).strip().split(" "       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int			,       UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		{}
 for i in range(UpperCAmelCase__       ):
  a							=		[]

 for e in range(UpperCAmelCase__       ):
  a							=		(int(UpperCAmelCase__       ) for i in _input(F"""Edge {e + 1}: <node1> <node2> """       ))
  graph[x].append(UpperCAmelCase__       )
 return graph


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int			,       UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		{}
 for i in range(UpperCAmelCase__       ):
  a							=		[]

 for e in range(UpperCAmelCase__       ):
  a							=		(int(UpperCAmelCase__       ) for i in _input(F"""Edge {e + 1}: <node1> <node2> """       ))
  graph[x].append(UpperCAmelCase__       )
  graph[y].append(UpperCAmelCase__       )
 return graph


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int			,       UpperCAmelCase__			:int       ):






 '''simple docstring'''

 a							=		{}
 for i in range(UpperCAmelCase__       ):
  a							=		[]

 for e in range(UpperCAmelCase__       ):
  a							=		(int(UpperCAmelCase__       ) for i in _input(F"""Edge {e + 1}: <node1> <node2> <weight> """       ))
  graph[x].append((y, w)       )
  graph[y].append((x, w)       )
 return graph


if __name__ == "__main__":
     A_			,	A_		:  Any										=  (int(i) for i in _input('''Number of nodes and edges: '''))

     A_		:  Optional[Any]										=  int(
         _input(
             '''Press 1 or 2 or 3 \n'''
             '''1. Unweighted directed \n'''
             '''2. Unweighted undirected \n'''
             '''3. Weighted undirected \n'''
         )[0]
     )

     A_		:  List[Any]										=  {
         1: initialize_unweighted_directed_graph,
         2: initialize_unweighted_undirected_graph,
         3: initialize_weighted_undirected_graph,
     }[graph_choice](n, m)


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int			,       UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 a							=		{s}, [s]
 print(UpperCAmelCase__       )
 while _s:
  a							=		0
  for i in g[_s[-1]]:
   if i not in vis:
    _s.append(UpperCAmelCase__       )
    vis.add(UpperCAmelCase__       )
    a							=		1
    print(UpperCAmelCase__       )
    break
  if not flag:
   _s.pop()


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Dict			,       UpperCAmelCase__			:List[Any]       ):






 '''simple docstring'''

 a							=		{s}, deque([s]       )
 print(UpperCAmelCase__       )
 while q:
  a							=		q.popleft()
  for v in g[u]:
   if v not in vis:
    vis.add(UpperCAmelCase__       )
    q.append(UpperCAmelCase__       )
    print(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

 a							=		{s: 0}, set(), {s: 0}
 while True:
  if len(UpperCAmelCase__       ) == len(UpperCAmelCase__       ) - 1:
   break
  a							=		10_00_00
  for i in dist:
   if i not in known and dist[i] < mini:
    a							=		dist[i]
    a							=		i
  known.add(UpperCAmelCase__       )
  for v in g[u]:
   if v[0] not in known and dist[u] + v[1] < dist.get(v[0]			,       10_00_00       ):
    a							=		dist[u] + v[1]
    a							=		u
 for i in dist:
  if i != s:
   print(dist[i]       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:List[Any]=None			,       UpperCAmelCase__			:int=None       ):






 '''simple docstring'''

 if q is None:
  a							=		[1]
 if ind is None:
  a							=		[0] * (len(UpperCAmelCase__       ) + 1)  # SInce oth Index is ignored
  for u in g:
   for v in g[u]:
    ind[v] += 1
  a							=		deque()
  for i in g:
   if ind[i] == 0:
    q.append(UpperCAmelCase__       )
 if len(UpperCAmelCase__       ) == 0:
  return
 a							=		q.popleft()
 print(UpperCAmelCase__       )
 for w in g[v]:
  ind[w] -= 1
  if ind[w] == 0:
   q.append(UpperCAmelCase__       )
 topo(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		input().strip()
 a							=		[]
 for _ in range(UpperCAmelCase__       ):
  a.append(map(UpperCAmelCase__			,       input().strip().split()       )       )
 return a, n


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

 (a)							=		a_and_n
 a							=		list(UpperCAmelCase__       )
 a							=		[[0] * n for i in range(UpperCAmelCase__       )]
 for k in range(UpperCAmelCase__       ):
  for i in range(UpperCAmelCase__       ):
   for j in range(UpperCAmelCase__       ):
    if dist[i][j] > dist[i][k] + dist[k][j]:
     a							=		dist[i][k] + dist[k][j]
     a							=		k
 print(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 a							=		{s: 0}, set(), {s: 0}
 while True:
  if len(UpperCAmelCase__       ) == len(UpperCAmelCase__       ) - 1:
   break
  a							=		10_00_00
  for i in dist:
   if i not in known and dist[i] < mini:
    a							=		dist[i]
    a							=		i
  known.add(UpperCAmelCase__       )
  for v in g[u]:
   if v[0] not in known and v[1] < dist.get(v[0]			,       10_00_00       ):
    a							=		v[1]
    a							=		u
 return dist


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		map(UpperCAmelCase__			,       input().split(" "       )       )
 a							=		[]
 for _ in range(UpperCAmelCase__       ):
  edges.append(map(UpperCAmelCase__			,       input().split(" "       )       )       )
 return edges, n


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 (a)							=		e_and_n
 e.sort(reverse=UpperCAmelCase__			,       key=lambda UpperCAmelCase__       : x[2]       )
 a							=		[{i} for i in range(1			,       n + 1       )]
 while True:
  if len(UpperCAmelCase__       ) == 1:
   break
  print(UpperCAmelCase__       )
  a							=		e.pop()
  for i in range(len(UpperCAmelCase__       )       ):
   if x[0] in s[i]:
    break
  for j in range(len(UpperCAmelCase__       )       ):
   if x[1] in s[j]:
    if i == j:
     break
    s[j].update(s[i]       )
    s.pop(UpperCAmelCase__       )
    break




def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[int]       ):






 '''simple docstring'''

 a							=		[]
 for node in graph:
  if not graph[node]:
   isolated.append(UpperCAmelCase__       )
 return isolated









'''simple docstring'''






import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoModel,
      )
      from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class a_     :







   def __init__(self       , __a       , __a=1_3       , __a=7       , __a=True       , __a=True       , __a=True       , __a=True       , __a=9_9       , __a=3_2       , __a=5       , __a=4       , __a=3_7       , __a="gelu"       , __a=0.1       , __a=0.1       , __a=5_1_2       , __a=1_6       , __a=2       , __a=0.02       , __a=3       , __a=4       , __a=None       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			parent
      __snake_case					:   Dict  				=			batch_size
      __snake_case					:   Union[str, Any]  				=			seq_length
      __snake_case					:   str  				=			is_training
      __snake_case					:   Optional[Any]  				=			use_input_mask
      __snake_case					:   Dict  				=			use_token_type_ids
      __snake_case					:   Union[str, Any]  				=			use_labels
      __snake_case					:   Optional[Any]  				=			vocab_size
      __snake_case					:   Optional[Any]  				=			hidden_size
      __snake_case					:   int  				=			num_hidden_layers
      __snake_case					:   Any  				=			num_attention_heads
      __snake_case					:   Optional[Any]  				=			intermediate_size
      __snake_case					:   List[str]  				=			hidden_act
      __snake_case					:   List[Any]  				=			hidden_dropout_prob
      __snake_case					:   Optional[Any]  				=			attention_probs_dropout_prob
      __snake_case					:   str  				=			max_position_embeddings
      __snake_case					:   str  				=			type_vocab_size
      __snake_case					:   Dict  				=			type_sequence_label_size
      __snake_case					:   int  				=			initializer_range
      __snake_case					:   Dict  				=			num_labels
      __snake_case					:   Optional[int]  				=			num_choices
      __snake_case					:   Dict  				=			scope







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size)

      __snake_case					:   Optional[Any]  				=			None
      if self.use_input_mask:
         __snake_case					:   Union[str, Any]  				=			random_attention_mask([self.batch_size, self.seq_length])

      __snake_case					:   Tuple  				=			None
      if self.use_token_type_ids:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.type_vocab_size)

      __snake_case					:   Dict  				=			None
      __snake_case					:   List[str]  				=			None
      __snake_case					:   List[str]  				=			None
      if self.use_labels:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size]       , self.type_sequence_label_size)
         __snake_case					:   Tuple  				=			ids_tensor([self.batch_size, self.seq_length]       , self.num_labels)
         __snake_case					:   str  				=			ids_tensor([self.batch_size]       , self.num_choices)

      __snake_case					:   List[str]  				=			self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      return YosoConfig(
          vocab_size=self.vocab_size       , hidden_size=self.hidden_size       , num_hidden_layers=self.num_hidden_layers       , num_attention_heads=self.num_attention_heads       , intermediate_size=self.intermediate_size       , hidden_act=self.hidden_act       , hidden_dropout_prob=self.hidden_dropout_prob       , attention_probs_dropout_prob=self.attention_probs_dropout_prob       , max_position_embeddings=self.max_position_embeddings       , type_vocab_size=self.type_vocab_size       , is_decoder=__a       , initializer_range=self.initializer_range       , )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.get_config()
      __snake_case					:   Tuple  				=			3_0_0
      return config







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Optional[Any]  				=			self.prepare_config_and_inputs()

      __snake_case					:   Optional[int]  				=			True
      __snake_case					:   Optional[int]  				=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      __snake_case					:   Dict  				=			ids_tensor([self.batch_size, self.seq_length]       , vocab_size=2)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModel(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      __snake_case					:   int  				=			model(__a       , token_type_ids=__a)
      __snake_case					:   Optional[Any]  				=			model(__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			True
      __snake_case					:   Dict  				=			YosoModel(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   List[str]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , encoder_attention_mask=__a       , )
      __snake_case					:   List[Any]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , )
      __snake_case					:   List[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			YosoForMaskedLM(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Union[str, Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			YosoForQuestionAnswering(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   str  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , start_positions=__a       , end_positions=__a       , )
      self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length))
      self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForSequenceClassification(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[int]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForTokenClassification(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Tuple  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   str:
      """simple docstring"""






      __snake_case					:   int  				=			self.num_choices
      __snake_case					:   Tuple  				=			YosoForMultipleChoice(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Any  				=			input_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Tuple  				=			token_type_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   List[str]  				=			input_mask.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Optional[int]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , labels=__a       , )
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices))







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   int  				=			self.prepare_config_and_inputs()
      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Union[str, Any]  				=			config_and_inputs
      __snake_case					:   Optional[int]  				=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict




@require_torch
class a_     (     UpperCamelCase_		,   UpperCamelCase_		,   unittest.TestCase				):
   _snake_case	   =				(
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False

   _snake_case	   =				()
   _snake_case	   =				(
       {
           """feature-extraction""": YosoModel,
           """fill-mask""": YosoForMaskedLM,
           """question-answering""": YosoForQuestionAnswering,
           """text-classification""": YosoForSequenceClassification,
           """token-classification""": YosoForTokenClassification,
           """zero-shot""": YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModelTester(self)
      __snake_case					:   List[Any]  				=			ConfigTester(self       , config_class=__a       , hidden_size=3_7)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      self.config_tester.run_common_tests()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __snake_case					:   List[Any]  				=			type
         self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   str  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__a)







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __snake_case					:   Tuple  				=			YosoModel.from_pretrained(__a)
         self.assertIsNotNone(__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      return




@require_torch
class a_     (     unittest.TestCase				):







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   str  				=			YosoModel.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   str  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   str  				=			model(__a)[0]

      __snake_case					:   Union[str, Any]  				=			torch.Size((1, 6, 7_6_8))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Dict  				=			torch.tensor(
          [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Tuple  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   Any  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[str]  				=			torch.Size((1, 6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   str  				=			torch.tensor(
          [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Dict  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Optional[Any]  				=			torch.arange(4_0_9_6).unsqueeze(0)

      with torch.no_grad():
         __snake_case					:   Dict  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[Any]  				=			torch.Size((1, 4_0_9_6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Tuple  				=			torch.tensor(
          [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))