



"""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 				A_      :



						"""simple docstring"""






						def __init__( self			:Optional[Any]       ,  lowercase_			:Tuple       ,  lowercase_			:Optional[Any]=13       ,  lowercase_			:List[str]=7       ,  lowercase_			:Optional[int]=True       ,  lowercase_			:Union[str, Any]=True       ,  lowercase_			:int=True       ,  lowercase_			:Any=True       ,  lowercase_			:Optional[Any]=99       ,  lowercase_			:int=24       ,  lowercase_			:Dict=2       ,  lowercase_			:Optional[Any]=6       ,  lowercase_			:List[Any]=37       ,  lowercase_			:List[Any]="gelu"       ,  lowercase_			:Optional[Any]=0.1       ,  lowercase_			:Tuple=0.1       ,  lowercase_			:Optional[Any]=5_12       ,  lowercase_			:Any=16       ,  lowercase_			:List[Any]=2       ,  lowercase_			:List[str]=0.02       ,  lowercase_			:Union[str, Any]=3       ,  lowercase_			:int=None       ,  lowercase_			:Any=10_00       ,  )    ->      Any:
												UpperCAmelCase					=			parent
												UpperCAmelCase					=			batch_size
												UpperCAmelCase					=			seq_length
												UpperCAmelCase					=			is_training
												UpperCAmelCase					=			use_input_mask
												UpperCAmelCase					=			use_token_type_ids
												UpperCAmelCase					=			use_labels
												UpperCAmelCase					=			vocab_size
												UpperCAmelCase					=			hidden_size
												UpperCAmelCase					=			num_hidden_layers
												UpperCAmelCase					=			num_attention_heads
												UpperCAmelCase					=			intermediate_size
												UpperCAmelCase					=			hidden_act
												UpperCAmelCase					=			hidden_dropout_prob
												UpperCAmelCase					=			attention_probs_dropout_prob
												UpperCAmelCase					=			max_position_embeddings
												UpperCAmelCase					=			type_vocab_size
												UpperCAmelCase					=			type_sequence_label_size
												UpperCAmelCase					=			initializer_range
												UpperCAmelCase					=			num_labels
												UpperCAmelCase					=			scope
												UpperCAmelCase					=			range_bbox






						def      UpperCAmelCase__					( self			:Any )    ->      Dict:
												UpperCAmelCase					=			ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size )

												UpperCAmelCase					=			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]:
																														UpperCAmelCase					=			bbox[i, j, 3]
																														UpperCAmelCase					=			bbox[i, j, 1]
																														UpperCAmelCase					=			t
																								if bbox[i, j, 2] < bbox[i, j, 0]:
																														UpperCAmelCase					=			bbox[i, j, 2]
																														UpperCAmelCase					=			bbox[i, j, 0]
																														UpperCAmelCase					=			t

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

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

												UpperCAmelCase					=			None
												UpperCAmelCase					=			None
												if self.use_labels:
																		UpperCAmelCase					=			ids_tensor([self.batch_size]       ,  self.type_sequence_label_size )
																		UpperCAmelCase					=			ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels )

												UpperCAmelCase					=			self.get_config()

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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Dict:
												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      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:List[Any]       ,  lowercase_			:List[Any]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:Optional[int]       ,  lowercase_			:Dict       ,  lowercase_			:int       ,  lowercase_			:int       ,  )    ->      int:
												UpperCAmelCase					=			LiltModel(config=lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_       ,  bbox=lowercase_       ,  attention_mask=lowercase_       ,  token_type_ids=lowercase_ )
												UpperCAmelCase					=			model(lowercase_       ,  bbox=lowercase_       ,  token_type_ids=lowercase_ )
												UpperCAmelCase					=			model(lowercase_       ,  bbox=lowercase_ )

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






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







@require_torch
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



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






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Optional[Any]       ,  lowercase_			:Dict       ,  lowercase_			:List[Any]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:Optional[int] )    ->      List[str]:
												return True






						def      UpperCAmelCase__					( self			:int )    ->      Tuple:
												UpperCAmelCase					=			LiltModelTester(self )
												UpperCAmelCase					=			ConfigTester(self       ,  config_class=lowercase_       ,  hidden_size=37 )






						def      UpperCAmelCase__					( self			:List[Any] )    ->      Dict:
												self.config_tester.run_common_tests()






						def      UpperCAmelCase__					( self			:List[Any] )    ->      Dict:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_ )






						def      UpperCAmelCase__					( self			:Tuple )    ->      List[str]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																		UpperCAmelCase					=			type
																		self.model_tester.create_and_check_model(*lowercase_ )






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






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*lowercase_ )






						@slow
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Optional[Any]:
												for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase					=			LiltModel.from_pretrained(lowercase_ )
																		self.assertIsNotNone(lowercase_ )







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



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:List[str] )    ->      Any:
												UpperCAmelCase					=			LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base' ).to(lowercase_ )

												UpperCAmelCase					=			torch.tensor([[1, 2]]       ,  device=lowercase_ )
												UpperCAmelCase					=			torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]       ,  device=lowercase_ )

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase					=			model(input_ids=lowercase_       ,  bbox=lowercase_ )

												UpperCAmelCase					=			torch.Size([1, 2, 7_68] )
												UpperCAmelCase					=			torch.tensor(
												    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]       ,  device=lowercase_       ,  )

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










"""simple docstring"""


def 			_lowerCAmelCase				(       lowercase_						):

						if not isinstance(lowercase_		,     lowercase_						):
												UpperCAmelCase					=			F"""Input value of [number={number}] must be an integer"""
												raise TypeError(lowercase_						)

						if number < 1:
												UpperCAmelCase					=			F"""Input value of [number={number}] must be > 0"""
												raise ValueError(lowercase_						)

						UpperCAmelCase					=			1

						for i in range(1		,     lowercase_						):
												current_number *= 4 * i - 2
												current_number //= i + 1

						return current_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()





