'''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  :





       '''simple docstring'''



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

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[Any]						= ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size			)

        lowercase__    :   List[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__    :   Dict						= bbox[i, j, 3]
           lowercase__    :   Dict						= bbox[i, j, 1]
           lowercase__    :   Any						= t
          if bbox[i, j, 2] < bbox[i, j, 0]:
           lowercase__    :   int						= bbox[i, j, 2]
           lowercase__    :   Optional[Any]						= bbox[i, j, 0]
           lowercase__    :   Union[str, Any]						= t

        lowercase__    :   int						= None
        if self.use_input_mask:
         lowercase__    :   Dict						= ids_tensor([self.batch_size, self.seq_length]     ,       vocab_size=2			)

        lowercase__    :   int						= None
        if self.use_token_type_ids:
         lowercase__    :   int						= ids_tensor([self.batch_size, self.seq_length]     ,       self.type_vocab_size			)

        lowercase__    :   int						= None
        lowercase__    :   Optional[int]						= None
        if self.use_labels:
         lowercase__    :   Union[str, Any]						= ids_tensor([self.batch_size]     ,       self.type_sequence_label_size			)
         lowercase__    :   Tuple						= 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 _lowerCAmelCase(						self			)  ->  Tuple:
        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  Optional[Any]:
        lowercase__    :   List[str]						= LiltModel(config=__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   int						= model(__lowerCAmelCase     ,       bbox=__lowerCAmelCase     ,       attention_mask=__lowerCAmelCase     ,       token_type_ids=__lowerCAmelCase			)
        lowercase__    :   int						= model(__lowerCAmelCase     ,       bbox=__lowerCAmelCase     ,       token_type_ids=__lowerCAmelCase			)
        lowercase__    :   Tuple						= model(__lowerCAmelCase     ,       bbox=__lowerCAmelCase			)

        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  Dict:
        lowercase__    :   int						= self.num_labels
        lowercase__    :   str						= LiltForTokenClassification(config=__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   Optional[Any]						= model(
            __lowerCAmelCase     ,       bbox=__lowerCAmelCase     ,       attention_mask=__lowerCAmelCase     ,       token_type_ids=__lowerCAmelCase     ,       labels=__lowerCAmelCase			)
        self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.num_labels)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  str:
        lowercase__    :   int						= LiltForQuestionAnswering(config=__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   Any						= model(
            __lowerCAmelCase     ,       bbox=__lowerCAmelCase     ,       attention_mask=__lowerCAmelCase     ,       token_type_ids=__lowerCAmelCase     ,       start_positions=__lowerCAmelCase     ,       end_positions=__lowerCAmelCase     ,       )
        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 _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   List[str]						= self.prepare_config_and_inputs()
        (
            (
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							
        )    :   int						= config_and_inputs
        lowercase__    :   Dict						= {
            '''input_ids''': input_ids,
            '''bbox''': bbox,
            '''token_type_ids''': token_type_ids,
            '''attention_mask''': input_mask,
        }
        return config, inputs_dict



@require_torch
class       UpperCAmelCase  (						a__   ,						a__   ,						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   (
           (
               LiltModel,
               LiltForSequenceClassification,
               LiltForTokenClassification,
               LiltForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE 		=   (
           {
               "feature-extraction": LiltModel,
               "question-answering": LiltForQuestionAnswering,
               "text-classification": LiltForSequenceClassification,
               "token-classification": LiltForTokenClassification,
               "zero-shot": LiltForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        return True

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Tuple						= LiltModelTester(self			)
        lowercase__    :   Union[str, Any]						= ConfigTester(self     ,       config_class=__lowerCAmelCase     ,       hidden_size=37			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        self.config_tester.run_common_tests()

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   List[Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   int						= self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
         lowercase__    :   List[Any]						= type
         self.model_tester.create_and_check_model(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   Optional[Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase			)


       @slow
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__    :   int						= LiltModel.from_pretrained(__lowerCAmelCase			)
         self.assertIsNotNone(__lowerCAmelCase			)



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





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   Optional[int]						= LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''			).to(__lowerCAmelCase			)

        lowercase__    :   Tuple						= torch.tensor([[1, 2]]     ,       device=__lowerCAmelCase			)
        lowercase__    :   Any						= torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]     ,       device=__lowerCAmelCase			)

        # forward pass
        with torch.no_grad():
         lowercase__    :   List[str]						= model(input_ids=__lowerCAmelCase     ,       bbox=__lowerCAmelCase			)

        lowercase__    :   str						= torch.Size([1, 2, 768]			)
        lowercase__    :   List[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=__lowerCAmelCase     ,       )

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


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

