



"""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        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  int,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Any=13,  lowerCamelCase							:  str=7,  lowerCamelCase							:  List[str]=True,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  List[Any]=99,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  List[str]=2,  lowerCamelCase							:  List[Any]=6,  lowerCamelCase							:  List[Any]=37,  lowerCamelCase							:  str="gelu",  lowerCamelCase							:  Dict=0.1,  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  str=512,  lowerCamelCase							:  List[str]=16,  lowerCamelCase							:  str=2,  lowerCamelCase							:  Optional[Any]=0.02,  lowerCamelCase							:  int=3,  lowerCamelCase							:  Optional[int]=None,  lowerCamelCase							:  Optional[Any]=1000,  )->					Tuple:
								lowerCamelCase__		:		Optional[Any]			=parent
								lowerCamelCase__		:		int			=batch_size
								lowerCamelCase__		:		Optional[int]			=seq_length
								lowerCamelCase__		:		List[Any]			=is_training
								lowerCamelCase__		:		Tuple			=use_input_mask
								lowerCamelCase__		:		Optional[int]			=use_token_type_ids
								lowerCamelCase__		:		List[Any]			=use_labels
								lowerCamelCase__		:		str			=vocab_size
								lowerCamelCase__		:		List[str]			=hidden_size
								lowerCamelCase__		:		List[Any]			=num_hidden_layers
								lowerCamelCase__		:		Tuple			=num_attention_heads
								lowerCamelCase__		:		List[str]			=intermediate_size
								lowerCamelCase__		:		List[Any]			=hidden_act
								lowerCamelCase__		:		Dict			=hidden_dropout_prob
								lowerCamelCase__		:		List[Any]			=attention_probs_dropout_prob
								lowerCamelCase__		:		Dict			=max_position_embeddings
								lowerCamelCase__		:		List[str]			=type_vocab_size
								lowerCamelCase__		:		int			=type_sequence_label_size
								lowerCamelCase__		:		Optional[Any]			=initializer_range
								lowerCamelCase__		:		Dict			=num_labels
								lowerCamelCase__		:		Union[str, Any]			=scope
								lowerCamelCase__		:		Union[str, Any]			=range_bbox



					def 		snake_case       (       self							:  Any     )->					Dict:
								lowerCamelCase__		:		int			=ids_tensor([self.batch_size, self.seq_length],  self.vocab_size     )

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

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

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

								lowerCamelCase__		:		str			=None
								lowerCamelCase__		:		int			=None
								if self.use_labels:
											lowerCamelCase__		:		Tuple			=ids_tensor([self.batch_size],  self.type_sequence_label_size     )
											lowerCamelCase__		:		Tuple			=ids_tensor([self.batch_size, self.seq_length],  self.num_labels     )

								lowerCamelCase__		:		Any			=self.get_config()

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



					def 		snake_case       (       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 		snake_case       (       self							:  Any,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  int,  )->					List[str]:
								lowerCamelCase__		:		Optional[Any]			=LiltModel(config=lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		List[str]			=model(lowerCamelCase,  bbox=lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=model(lowerCamelCase,  bbox=lowerCamelCase,  token_type_ids=lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=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 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Any,  )->					Optional[int]:
								lowerCamelCase__		:		Tuple			=self.num_labels
								lowerCamelCase__		:		List[str]			=LiltForTokenClassification(config=lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		Union[str, 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 		snake_case       (       self							:  str,  lowerCamelCase							:  int,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict,  lowerCamelCase							:  str,  lowerCamelCase							:  Any,  )->					Tuple:
								lowerCamelCase__		:		Tuple			=LiltForQuestionAnswering(config=lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		str			=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 		snake_case       (       self							:  int     )->					str:
								lowerCamelCase__		:		Optional[Any]			=self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   
								)		:		int			=config_and_inputs
								lowerCamelCase__		:		str			={
								    '''input_ids''': input_ids,
								    '''bbox''': bbox,
								    '''token_type_ids''': token_type_ids,
								    '''attention_mask''': input_mask,
								}
								return config, inputs_dict





@require_torch
class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   lowerCAmelCase_		,   lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





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



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict,  lowerCamelCase							:  int,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  int     )->					Union[str, Any]:
								return True



					def 		snake_case       (       self							:  List[Any]     )->					Dict:
								lowerCamelCase__		:		Optional[Any]			=LiltModelTester(self     )
								lowerCamelCase__		:		List[Any]			=ConfigTester(self,  config_class=lowerCamelCase,  hidden_size=37     )



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



					def 		snake_case       (       self							:  Optional[Any]     )->					Any:
								lowerCamelCase__		:		int			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase     )



					def 		snake_case       (       self							:  str     )->					List[str]:
								lowerCamelCase__		:		str			=self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
											lowerCamelCase__		:		Any			=type
											self.model_tester.create_and_check_model(*lowerCamelCase     )



					def 		snake_case       (       self							:  Any     )->					List[str]:
								lowerCamelCase__		:		str			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase     )



					def 		snake_case       (       self							:  List[str]     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase     )




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





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


					'''simple docstring'''





					def 		snake_case       (       self							:  Tuple     )->					List[Any]:
								lowerCamelCase__		:		str			=LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''     ).to(lowerCamelCase     )

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

								# forward pass
								with torch.no_grad():
											lowerCamelCase__		:		List[Any]			=model(input_ids=lowerCamelCase,  bbox=lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=torch.Size([1, 2, 768]     )
								lowerCamelCase__		:		int			=torch.tensor(
								    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]],  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      snake_case__   (		):
			"""simple docstring"""
			return [list(range(1000 - i							,		-1000 - i							,		-1	)	) for i in range(1000	)]


_lowercase : str      						=						generate_large_matrix()
_lowercase : str      						=						(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)






def      snake_case__   (		__lowerCamelCase				:    list[list[int]]	):
			"""simple docstring"""
			assert all(row == sorted(__lowerCamelCase							,		reverse=__lowerCamelCase	) for row in grid	)
			assert all(list(__lowerCamelCase	) == sorted(__lowerCamelCase							,		reverse=__lowerCamelCase	) for col in zip(*__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    list[int]	):
			"""simple docstring"""
			lowerCamelCase__		:		int			=0
			lowerCamelCase__		:		Optional[int]			=len(__lowerCamelCase	) - 1

			# Edge cases such as no values or all numbers are negative.
			if not array or array[0] < 0:
						return 0

			while right + 1 > left:
						lowerCamelCase__		:		List[str]			=(left + right) // 2
						lowerCamelCase__		:		Union[str, Any]			=array[mid]

						# Num must be negative and the index must be greater than or equal to 0.
						if num < 0 and array[mid - 1] >= 0:
									return mid

						if num >= 0:
									lowerCamelCase__		:		Union[str, Any]			=mid + 1
						else:
									lowerCamelCase__		:		Dict			=mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
			return len(__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    list[list[int]]	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=0
			lowerCamelCase__		:		str			=len(grid[0]	)

			for i in range(len(__lowerCamelCase	)	):
						lowerCamelCase__		:		Any			=find_negative_index(grid[i][:bound]	)
						total += bound
			return (len(__lowerCamelCase	) * len(grid[0]	)) - total






def      snake_case__   (		__lowerCamelCase				:    list[list[int]]	):
			"""simple docstring"""
			return len([number for row in grid for number in row if number < 0]	)






def      snake_case__   (		__lowerCamelCase				:    list[list[int]]	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=0
			for row in grid:
						for i, number in enumerate(__lowerCamelCase	):
									if number < 0:
												total += len(__lowerCamelCase	) - i
												break
			return total






def      snake_case__   (		):
			"""simple docstring"""
			from timeit import timeit

			print('''Running benchmarks'''	)
			lowerCamelCase__		:		List[str]			=(
			    '''from __main__ import count_negatives_binary_search, '''
			    '''count_negatives_brute_force, count_negatives_brute_force_with_break, grid'''
			)
			for func in (
			    "count_negatives_binary_search",  # took 0.7727 seconds
			    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
			    "count_negatives_brute_force",  # took 12.8160 seconds
			):
						lowerCamelCase__		:		Union[str, Any]			=timeit(f'''{func}(grid=grid)'''							,		setup=__lowerCamelCase							,		number=500	)
						print(f'''{func}() took {time:0.4f} seconds'''	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()




