






"""simple docstring"""




import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     LiltForQuestionAnswering,
     LiltForSequenceClassification,
     LiltForTokenClassification,
     LiltModel,
 )
 from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST






class        UpperCamelCase			:


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						str=13							,						UpperCAmelCase__		:						Optional[Any]=7							,						UpperCAmelCase__		:						List[Any]=True							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Optional[Any]=True							,						UpperCAmelCase__		:						Any=True							,						UpperCAmelCase__		:						Any=99							,						UpperCAmelCase__		:						Tuple=24							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						List[Any]=6							,						UpperCAmelCase__		:						Any=37							,						UpperCAmelCase__		:						Optional[int]="gelu"							,						UpperCAmelCase__		:						int=0.1							,						UpperCAmelCase__		:						List[Any]=0.1							,						UpperCAmelCase__		:						Union[str, Any]=512							,						UpperCAmelCase__		:						Optional[int]=16							,						UpperCAmelCase__		:						Dict=2							,						UpperCAmelCase__		:						Optional[int]=0.0_2							,						UpperCAmelCase__		:						Union[str, Any]=3							,						UpperCAmelCase__		:						List[str]=None							,						UpperCAmelCase__		:						Dict=1000							,						)					->       str:
   _a  :       Dict             =	parent
   _a  :       Dict             =	batch_size
   _a  :       Tuple             =	seq_length
   _a  :       List[str]             =	is_training
   _a  :       Optional[int]             =	use_input_mask
   _a  :       Any             =	use_token_type_ids
   _a  :       List[Any]             =	use_labels
   _a  :       Dict             =	vocab_size
   _a  :       Optional[Any]             =	hidden_size
   _a  :       Any             =	num_hidden_layers
   _a  :       Union[str, Any]             =	num_attention_heads
   _a  :       List[str]             =	intermediate_size
   _a  :       List[Any]             =	hidden_act
   _a  :       int             =	hidden_dropout_prob
   _a  :       List[Any]             =	attention_probs_dropout_prob
   _a  :       Optional[int]             =	max_position_embeddings
   _a  :       Union[str, Any]             =	type_vocab_size
   _a  :       int             =	type_sequence_label_size
   _a  :       Optional[Any]             =	initializer_range
   _a  :       Union[str, Any]             =	num_labels
   _a  :       List[Any]             =	scope
   _a  :       List[str]             =	range_bbox


 def _lowercase	(     self		:						Tuple							)					->       str:
   _a  :       Dict             =	ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size							)

   _a  :       Optional[Any]             =	ids_tensor([self.batch_size, self.seq_length, 4]							,						self.range_bbox							)
   # Ensure that bbox is legal
   for i in range(bbox.shape[0]							):
     for j in range(bbox.shape[1]							):
       if bbox[i, j, 3] < bbox[i, j, 1]:
         _a  :       List[str]             =	bbox[i, j, 3]
         _a  :       List[Any]             =	bbox[i, j, 1]
         _a  :       List[Any]             =	t
       if bbox[i, j, 2] < bbox[i, j, 0]:
         _a  :       List[str]             =	bbox[i, j, 2]
         _a  :       Tuple             =	bbox[i, j, 0]
         _a  :       Tuple             =	t

   _a  :       Any             =	None
   if self.use_input_mask:
     _a  :       Tuple             =	ids_tensor([self.batch_size, self.seq_length]							,						vocab_size=2							)

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

   _a  :       Optional[Any]             =	None
   _a  :       Tuple             =	None
   if self.use_labels:
     _a  :       List[Any]             =	ids_tensor([self.batch_size]							,						self.type_sequence_label_size							)
     _a  :       Optional[Any]             =	ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels							)

   _a  :       Tuple             =	self.get_config()

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


 def _lowercase	(     self		:						Optional[Any]							)					->       Any:
   return LiltConfig(
       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							,						initializer_range=self.initializer_range							,						)


 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						int							,						)					->       Union[str, Any]:
   _a  :       List[str]             =	LiltModel(config=UpperCAmelCase__							)
   model.to(UpperCAmelCase__							)
   model.eval()
   _a  :       List[str]             =	model(UpperCAmelCase__							,						bbox=UpperCAmelCase__							,						attention_mask=UpperCAmelCase__							,						token_type_ids=UpperCAmelCase__							)
   _a  :       List[Any]             =	model(UpperCAmelCase__							,						bbox=UpperCAmelCase__							,						token_type_ids=UpperCAmelCase__							)
   _a  :       Any             =	model(UpperCAmelCase__							,						bbox=UpperCAmelCase__							)

   self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)							)
   self.parent.assertEqual(result.pooler_output.shape							,						(self.batch_size, self.hidden_size)							)


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						List[str]							,						)					->       Optional[Any]:
   _a  :       str             =	self.num_labels
   _a  :       List[str]             =	LiltForTokenClassification(config=UpperCAmelCase__							)
   model.to(UpperCAmelCase__							)
   model.eval()
   _a  :       Dict             =	model(
       UpperCAmelCase__							,						bbox=UpperCAmelCase__							,						attention_mask=UpperCAmelCase__							,						token_type_ids=UpperCAmelCase__							,						labels=UpperCAmelCase__							)
   self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)							)


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Union[str, Any]							,						)					->       str:
   _a  :       List[str]             =	LiltForQuestionAnswering(config=UpperCAmelCase__							)
   model.to(UpperCAmelCase__							)
   model.eval()
   _a  :       str             =	model(
       UpperCAmelCase__							,						bbox=UpperCAmelCase__							,						attention_mask=UpperCAmelCase__							,						token_type_ids=UpperCAmelCase__							,						start_positions=UpperCAmelCase__							,						end_positions=UpperCAmelCase__							,						)
   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 _lowercase	(     self		:						Optional[int]							)					->       Optional[Any]:
   _a  :       List[str]             =	self.prepare_config_and_inputs()
   (
       (
       _a
   )      ,  (
       _a
   )      ,  (
       _a
   )      ,  (
       _a
   )      ,  (
       _a
   )      ,  (
       _a
   )      ,  (
       _a
   )      ,  
   )  :       Optional[Any]             =	config_and_inputs
   _a  :       str             =	{
       """input_ids""": input_ids,
       """bbox""": bbox,
       """token_type_ids""": token_type_ids,
       """attention_mask""": input_mask,
   }
   return config, inputs_dict







@require_torch
class        UpperCamelCase			(	snake_case_		,     snake_case_		,     snake_case_		,     unittest.TestCase      ):
 UpperCamelCase					:       List[Any]			=					(
     (
         LiltModel,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 UpperCamelCase					:       Tuple			=					(
     {
         '''feature-extraction''': LiltModel,
         '''question-answering''': LiltForQuestionAnswering,
         '''text-classification''': LiltForSequenceClassification,
         '''token-classification''': LiltForTokenClassification,
         '''zero-shot''': LiltForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCamelCase					:       Dict			=					False
 UpperCamelCase					:       Union[str, Any]			=					False


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						Union[str, Any]							)					->       Tuple:
   return True


 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Dict             =	LiltModelTester(self							)
   _a  :       Optional[Any]             =	ConfigTester(self							,						config_class=UpperCAmelCase__							,						hidden_size=37							)


 def _lowercase	(     self		:						List[str]							)					->       Tuple:
   self.config_tester.run_common_tests()


 def _lowercase	(     self		:						List[Any]							)					->       Union[str, Any]:
   _a  :       Dict             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase__							)


 def _lowercase	(     self		:						Dict							)					->       Optional[int]:
   _a  :       Optional[int]             =	self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
     _a  :       str             =	type
     self.model_tester.create_and_check_model(*UpperCAmelCase__							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       str:
   _a  :       Optional[Any]             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__							)


 def _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       List[str]             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__							)







 @slow
 def _lowercase	(     self		:						Optional[Any]							)					->       List[Any]:
   for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _a  :       List[Any]             =	LiltModel.from_pretrained(UpperCAmelCase__							)
     self.assertIsNotNone(UpperCAmelCase__							)







@require_torch
@slow
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       List[Any]:
   _a  :       Tuple             =	LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""							).to(UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	torch.tensor([[1, 2]]							,						device=UpperCAmelCase__							)
   _a  :       List[Any]             =	torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]							,						device=UpperCAmelCase__							)

   # forward pass
   with torch.no_grad():
     _a  :       Union[str, Any]             =	model(input_ids=UpperCAmelCase__							,						bbox=UpperCAmelCase__							)

   _a  :       str             =	torch.Size([1, 2, 768]							)
   _a  :       Any             =	torch.tensor(
       [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]							,						device=UpperCAmelCase__							,						)

   self.assertTrue(outputs.last_hidden_state.shape							,						UpperCAmelCase__							)
   self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]							,						UpperCAmelCase__							,						atol=1E-3							)							)









"""simple docstring"""




from __future__ import annotations

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  print(F"""Vertex\tShortest Distance from vertex {src}"""				)
  for i, d in enumerate(UpperCamelCase__				):
    print(F"""{i}\t\t{d}"""				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for j in range(UpperCamelCase__				):
    _a      ,  _a      ,  _a  :       List[str]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])
    if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
      return True
  return False

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	[float("""inf"""				)] * vertex_count
  _a  :       Any             =	0.0

  for _ in range(vertex_count - 1				):
    for j in range(UpperCamelCase__				):
      _a      ,  _a      ,  _a  :       List[Any]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])

      if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
        _a  :       Any             =	distance[u] + w

  _a  :       Union[str, Any]             =	check_negative_cycle(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  if negative_cycle_exists:
    raise Exception("""Negative cycle found"""				)

  return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _snake_case          =   int(input('Enter number of vertices: ').strip())
 _snake_case          =   int(input('Enter number of edges: ').strip())

 _snake_case          =   [{} for _ in range(E)]

 for i in range(E):
  print('Edge ', i + 1)
  _snake_case      , _snake_case      , _snake_case          =   (
      int(x)
      for x in input('Enter source, destination, weight: ').strip().split(' ')
  )
  _snake_case          =   {'src': src, 'dst': dest, 'weight': weight}

 _snake_case          =   int(input('\nEnter shortest path source:').strip())
 _snake_case          =   bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)

