
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 __snake_case		:
    def __init__(  self		,snake_case		,snake_case=13		,snake_case=7		,snake_case=True		,snake_case=True		,snake_case=True		,snake_case=True		,snake_case=99		,snake_case=24		,snake_case=2		,snake_case=6		,snake_case=37		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=16		,snake_case=2		,snake_case=0.02		,snake_case=3		,snake_case=None		,snake_case=1000		,):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				parent
           lowercase							:					Tuple	     =				batch_size
           lowercase							:					Union[str, Any]	     =				seq_length
           lowercase							:					int	     =				is_training
           lowercase							:					Union[str, Any]	     =				use_input_mask
           lowercase							:					Any	     =				use_token_type_ids
           lowercase							:					List[str]	     =				use_labels
           lowercase							:					Tuple	     =				vocab_size
           lowercase							:					Dict	     =				hidden_size
           lowercase							:					Any	     =				num_hidden_layers
           lowercase							:					Optional[int]	     =				num_attention_heads
           lowercase							:					Optional[Any]	     =				intermediate_size
           lowercase							:					List[str]	     =				hidden_act
           lowercase							:					Any	     =				hidden_dropout_prob
           lowercase							:					str	     =				attention_probs_dropout_prob
           lowercase							:					Optional[int]	     =				max_position_embeddings
           lowercase							:					Union[str, Any]	     =				type_vocab_size
           lowercase							:					int	     =				type_sequence_label_size
           lowercase							:					int	     =				initializer_range
           lowercase							:					int	     =				num_labels
           lowercase							:					int	     =				scope
           lowercase							:					Dict	     =				range_bbox
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size				)

           lowercase							:					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]:
                                lowercase							:					List[Any]	     =				bbox[i, j, 3]
                                lowercase							:					int	     =				bbox[i, j, 1]
                                lowercase							:					int	     =				t
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                lowercase							:					List[str]	     =				bbox[i, j, 2]
                                lowercase							:					Tuple	     =				bbox[i, j, 0]
                                lowercase							:					List[str]	     =				t

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

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

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

           lowercase							:					Union[str, Any]	     =				self.get_config()

           return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				LiltModel(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					List[str]	     =				model(snake_case		,bbox=snake_case		,attention_mask=snake_case		,token_type_ids=snake_case				)
           lowercase							:					List[str]	     =				model(snake_case		,bbox=snake_case		,token_type_ids=snake_case				)
           lowercase							:					Optional[int]	     =				model(snake_case		,bbox=snake_case				)

           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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:					int	     =				self.num_labels
           lowercase							:					Optional[Any]	     =				LiltForTokenClassification(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					Optional[int]	     =				model(
               snake_case		,bbox=snake_case		,attention_mask=snake_case		,token_type_ids=snake_case		,labels=snake_case				)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				LiltForQuestionAnswering(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					int	     =				model(
               snake_case		,bbox=snake_case		,attention_mask=snake_case		,token_type_ids=snake_case		,start_positions=snake_case		,end_positions=snake_case		,)
           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				):



           '''simple docstring'''







           lowercase							:					str	     =				self.prepare_config_and_inputs()
           (
               (
               lowercase
           )     ,      (
               lowercase
           )     ,      (
               lowercase
           )     ,      (
               lowercase
           )     ,      (
               lowercase
           )     ,      (
               lowercase
           )     ,      (
               lowercase
           )     ,      
           )							:					int	     =				config_and_inputs
           lowercase							:					Any	     =				{
               """input_ids""": input_ids,
               """bbox""": bbox,
               """token_type_ids""": token_type_ids,
               """attention_mask""": input_mask,
           }
           return config, inputs_dict






@require_torch
class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     unittest.TestCase					):
    _a		:						List[Any]=    (
        (
            LiltModel,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    _a		:						Dict=    (
        {
            "feature-extraction": LiltModel,
            "question-answering": LiltForQuestionAnswering,
            "text-classification": LiltForSequenceClassification,
            "token-classification": LiltForTokenClassification,
            "zero-shot": LiltForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _a		:						str=    False
    _a		:						Optional[Any]=    False
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           return True
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				LiltModelTester(self				)
           lowercase							:					int	     =				ConfigTester(self		,config_class=snake_case		,hidden_size=37				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  lowercase							:					Union[str, Any]	     =				type
                  self.model_tester.create_and_check_model(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*snake_case				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase							:					List[Any]	     =				LiltModel.from_pretrained(snake_case				)
                  self.assertIsNotNone(snake_case				)






@require_torch
@slow
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""				).to(snake_case				)

           lowercase							:					List[Any]	     =				torch.tensor([[1, 2]]		,device=snake_case				)
           lowercase							:					Optional[Any]	     =				torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]		,device=snake_case				)

           # forward pass
           with torch.no_grad():
                  lowercase							:					Optional[Any]	     =				model(input_ids=snake_case		,bbox=snake_case				)

           lowercase							:					Tuple	     =				torch.Size([1, 2, 768]				)
           lowercase							:					Tuple	     =				torch.tensor(
               [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]		,device=snake_case		,)

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


from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat







def 						_snake_case(  )			->   tuple[list[int], int]:
       lowercase							:					List[Any]	     =				[randint(-1_000 ,     1_000    ) for i in range(10    )]
       lowercase							:					Tuple	     =				randint(-5_000 ,     5_000    )
       return (arr, r)


lowercase  : List[Any]						   =							make_dataset()







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, ...]:
       for triplet in permutations(SCREAMING_SNAKE_CASE__ ,     3    ):
              if sum(SCREAMING_SNAKE_CASE__    ) == target:
                     return tuple(sorted(SCREAMING_SNAKE_CASE__    )    )
       return (0, 0, 0)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, int, int]:
       arr.sort()
       lowercase							:					Optional[int]	     =				len(SCREAMING_SNAKE_CASE__    )
       for i in range(n - 1    ):
              lowercase     ,      lowercase							:					Optional[Any]	     =				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)







def 						_snake_case(  )			->   tuple[float, float]:
       lowercase							:					Dict	     =				"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
       lowercase							:					Tuple	     =				"""
triplet_sum1(*dataset)
"""
       lowercase							:					int	     =				"""
triplet_sum2(*dataset)
"""
       lowercase							:					str	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       lowercase							:					Dict	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       return (min(SCREAMING_SNAKE_CASE__    ), min(SCREAMING_SNAKE_CASE__    ))


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  lowercase  : Union[str, Any]						   =							solution_times()
  print(F'''The time for naive implementation is {times[0]}.''')
  print(F'''The time for optimized implementation is {times[1]}.''')
