

'''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    snake_case     :






				"""simple docstring"""



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



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				ids_tensor([self.batch_size, self.seq_length],     self.vocab_size       )

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

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

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

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

											SCREAMING_SNAKE_CASE_				=				self.get_config()

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



				def   a__					(    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   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				LiltModel(config=_lowercase       )
											model.to(_lowercase       )
											model.eval()
											SCREAMING_SNAKE_CASE_				=				model(_lowercase,     bbox=_lowercase,     attention_mask=_lowercase,     token_type_ids=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				model(_lowercase,     bbox=_lowercase,     token_type_ids=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.num_labels
											SCREAMING_SNAKE_CASE_				=				LiltForTokenClassification(config=_lowercase       )
											model.to(_lowercase       )
											model.eval()
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				LiltForQuestionAnswering(config=_lowercase       )
											model.to(_lowercase       )
											model.eval()
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				self.prepare_config_and_inputs()
											(
											    (
											    SCREAMING_SNAKE_CASE_
											)				,    (
											    SCREAMING_SNAKE_CASE_
											)				,    (
											    SCREAMING_SNAKE_CASE_
											)				,    (
											    SCREAMING_SNAKE_CASE_
											)				,    (
											    SCREAMING_SNAKE_CASE_
											)				,    (
											    SCREAMING_SNAKE_CASE_
											)				,    (
											    SCREAMING_SNAKE_CASE_
											)				,    
											)				=				config_and_inputs
											SCREAMING_SNAKE_CASE_				=				{
											    '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     (      lowercase_,						lowercase_,						lowercase_,						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   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							Any:
											return True



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				LiltModelTester(self       )
											SCREAMING_SNAKE_CASE_				=				ConfigTester(self,     config_class=_lowercase,     hidden_size=37       )



				def   a__					(    self       )     ->							Union[str, Any]:
											self.config_tester.run_common_tests()



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowercase       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		SCREAMING_SNAKE_CASE_				=				type
																		self.model_tester.create_and_check_model(*_lowercase       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_lowercase       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_lowercase       )





				@slow
				def   a__					(    self       )     ->							List[str]:
											for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE_				=				LiltModel.from_pretrained(_lowercase       )
																		self.assertIsNotNone(_lowercase       )






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






				"""simple docstring"""



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'       ).to(_lowercase       )

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

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_				=				model(input_ids=_lowercase,     bbox=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				torch.Size([1, 2, 768]       )
											SCREAMING_SNAKE_CASE_				=				torch.tensor(
											    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]],     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 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					bool = False   )      -> bool:
							if n == 2:
														return True
							if not n % 2 or n < 2:
														return False
							if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
														return False
							if n > 3_3170_4406_4679_8873_8596_1981 and not allow_probable:
														raise ValueError(
														    'Warning: upper bound of deterministic test is exceeded. '
														    'Pass allow_probable=True to allow probabilistic test. '
														    'A return value of True indicates a probable prime.'   )
							# array bounds provided by analysis
							SCREAMING_SNAKE_CASE_				=				[
							    2047,
							    137_3653,
							    2532_6001,
							    32_1503_1751,
							    2_1523_0289_8747,
							    3_4747_4966_0383,
							    341_5500_7172_8321,
							    1,
							    382_5123_0565_4641_3051,
							    1,
							    1,
							    3186_6585_7834_0311_5116_7461,
							    3_3170_4406_4679_8873_8596_1981,
							]

							SCREAMING_SNAKE_CASE_				=				[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
							for idx, _p in enumerate(lowerCAmelCase__		,1   ):
														if n < _p:
																					# then we have our last prime to check
																					SCREAMING_SNAKE_CASE_				=				primes[:idx]
																					break
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				n - 1, 0
							# break up n -1 into a power of 2 (s) and
							# remaining odd component
							# essentially, solve for d * 2 ** s == n - 1
							while d % 2 == 0:
														d //= 2
														s += 1
							for prime in plist:
														SCREAMING_SNAKE_CASE_				=				False
														for r in range(lowerCAmelCase__   ):
																					SCREAMING_SNAKE_CASE_				=				pow(lowerCAmelCase__		,d * 2**r		,lowerCAmelCase__   )
																					# see article for analysis explanation for m
																					if (r == 0 and m == 1) or ((m + 1) % n == 0):
																												SCREAMING_SNAKE_CASE_				=				True
																												# this loop will not determine compositeness
																												break
														if pr:
																					continue
														# if pr is False, then the above loop never evaluated to true,
														# and the n MUST be composite
														return False
							return True




def 							_UpperCamelCase				(   )      -> None:
							assert not miller_rabin(561   )
							assert miller_rabin(563   )
							# 2047

							assert not miller_rabin(83_8201   )
							assert miller_rabin(83_8207   )
							# 1_373_653

							assert not miller_rabin(1731_6001   )
							assert miller_rabin(1731_6017   )
							# 25_326_001

							assert not miller_rabin(30_7838_6641   )
							assert miller_rabin(30_7838_6653   )
							# 3_215_031_751

							assert not miller_rabin(1_7130_4557_4801   )
							assert miller_rabin(1_7130_4557_4819   )
							# 2_152_302_898_747

							assert not miller_rabin(2_7797_9972_8307   )
							assert miller_rabin(2_7797_9972_8327   )
							# 3_474_749_660_383

							assert not miller_rabin(113_8500_2390_9441   )
							assert miller_rabin(113_8500_2390_9527   )
							# 341_550_071_728_321

							assert not miller_rabin(127_5041_0188_4880_4351   )
							assert miller_rabin(127_5041_0188_4880_4391   )
							# 3_825_123_056_546_413_051

							assert not miller_rabin(796_6646_4458_5077_8779_1867   )
							assert miller_rabin(796_6646_4458_5077_8779_1951   )
							# 318_665_857_834_031_151_167_461

							assert not miller_rabin(5528_4067_7446_6478_9766_0333   )
							assert miller_rabin(5528_4067_7446_6478_9766_0359   )
							# 3_317_044_064_679_887_385_961_981
							# upper limit for probabilistic test


if __name__ == "__main__":
							test_miller_rabin()





