





from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.layoutlm.modeling_tf_layoutlm import (
					    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFLayoutLMForMaskedLM,
					    TFLayoutLMForQuestionAnswering,
					    TFLayoutLMForSequenceClassification,
					    TFLayoutLMForTokenClassification,
					    TFLayoutLMModel,
					)
class 			UpperCamelCase_		:




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=13				,						lowerCAmelCase_=7				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=99				,						lowerCAmelCase_=32				,						lowerCAmelCase_=2				,						lowerCAmelCase_=4				,						lowerCAmelCase_=37				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=512				,						lowerCAmelCase_=16				,						lowerCAmelCase_=2				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=3				,						lowerCAmelCase_=4				,						lowerCAmelCase_=None				,						lowerCAmelCase_=1000				,						)		->							Any:
										_snake_case									=				parent
										_snake_case									=				batch_size
										_snake_case									=				seq_length
										_snake_case									=				is_training
										_snake_case									=				use_input_mask
										_snake_case									=				use_token_type_ids
										_snake_case									=				use_labels
										_snake_case									=				vocab_size
										_snake_case									=				hidden_size
										_snake_case									=				num_hidden_layers
										_snake_case									=				num_attention_heads
										_snake_case									=				intermediate_size
										_snake_case									=				hidden_act
										_snake_case									=				hidden_dropout_prob
										_snake_case									=				attention_probs_dropout_prob
										_snake_case									=				max_position_embeddings
										_snake_case									=				type_vocab_size
										_snake_case									=				type_sequence_label_size
										_snake_case									=				initializer_range
										_snake_case									=				num_labels
										_snake_case									=				num_choices
										_snake_case									=				scope
										_snake_case									=				range_bbox




					def 	lowerCAmelCase			(		self  )		->							str:
										_snake_case									=				ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size  )

										# convert bbox to numpy since TF does not support item assignment
										_snake_case									=				ids_tensor([self.batch_size, self.seq_length, 4]				,						self.range_bbox  ).numpy()
										# 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]:
																									_snake_case									=				bbox[i, j, 3]
																									_snake_case									=				bbox[i, j, 1]
																									_snake_case									=				t
																				if bbox[i, j, 2] < bbox[i, j, 0]:
																									_snake_case									=				bbox[i, j, 2]
																									_snake_case									=				bbox[i, j, 0]
																									_snake_case									=				t
										_snake_case									=				tf.convert_to_tensor(lowerCAmelCase_  )

										_snake_case									=				None
										if self.use_input_mask:
															_snake_case									=				random_attention_mask([self.batch_size, self.seq_length]  )

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

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

										_snake_case									=				LayoutLMConfig(
										    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				,						)

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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Union[str, Any]:
										_snake_case									=				TFLayoutLMModel(config=lowerCAmelCase_  )

										_snake_case									=				model(lowerCAmelCase_				,						lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )
										_snake_case									=				model(lowerCAmelCase_				,						lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )
										_snake_case									=				model(lowerCAmelCase_				,						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_				,						lowerCAmelCase_  )		->							int:
										_snake_case									=				TFLayoutLMForMaskedLM(config=lowerCAmelCase_  )

										_snake_case									=				model(lowerCAmelCase_				,						lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_				,						labels=lowerCAmelCase_  )
										self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Any:
										_snake_case									=				self.num_labels
										_snake_case									=				TFLayoutLMForSequenceClassification(config=lowerCAmelCase_  )

										_snake_case									=				model(lowerCAmelCase_				,						lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )
										self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				self.num_labels
										_snake_case									=				TFLayoutLMForTokenClassification(config=lowerCAmelCase_  )

										_snake_case									=				model(lowerCAmelCase_				,						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_				,						lowerCAmelCase_  )		->							Tuple:
										_snake_case									=				TFLayoutLMForQuestionAnswering(config=lowerCAmelCase_  )

										_snake_case									=				model(lowerCAmelCase_				,						lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=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  )		->							Any:
										_snake_case									=				self.prepare_config_and_inputs()
										(
										    (
										    _snake_case
										)							,     (
										    _snake_case
										)							,     (
										    _snake_case
										)							,     (
										    _snake_case
										)							,     (
										    _snake_case
										)							,     (
										    _snake_case
										)							,     (
										    _snake_case
										)							,     (
										    _snake_case
										)							,     
										)									=				config_and_inputs
										_snake_case									=				{
										    'input_ids': input_ids,
										    'bbox': bbox,
										    'token_type_ids': token_type_ids,
										    'attention_mask': input_mask,
										}
										return config, inputs_dict





@require_tf
class 			UpperCamelCase_		(  _lowerCamelCase							,  _lowerCamelCase							,  unittest.TestCase					):
					lowerCAmelCase_							=	(
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					lowerCAmelCase_							=	(
					    {
					        '''feature-extraction''': TFLayoutLMModel,
					        '''fill-mask''': TFLayoutLMForMaskedLM,
					        '''text-classification''': TFLayoutLMForSequenceClassification,
					        '''token-classification''': TFLayoutLMForTokenClassification,
					        '''zero-shot''': TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase_							=	False
					lowerCAmelCase_							=	True
					lowerCAmelCase_							=	10




					def 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case									=				TFLayoutLMModelTester(self  )
										_snake_case									=				ConfigTester(self				,						config_class=lowerCAmelCase_				,						hidden_size=37  )




					def 	lowerCAmelCase			(		self  )		->							int:
										self.config_tester.run_common_tests()




					def 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_  )




					@slow
					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_snake_case									=				TFLayoutLMModel.from_pretrained(lowerCAmelCase_  )
															self.assertIsNotNone(lowerCAmelCase_  )






					@unittest.skip('Onnx compliancy broke with TF 2.10'  )
					def 	lowerCAmelCase			(		self  )		->							Dict:
										pass





def 						lowerCamelCase__     (     )			-> Tuple:



					'''simple docstring'''




					_snake_case									=				tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]						)  # noqa: E231
					_snake_case									=				tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						)  # noqa: E231
					_snake_case									=				tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]						)  # noqa: E231
					_snake_case									=				tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						)  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					_snake_case									=				tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						)  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class 			UpperCamelCase_		(  unittest.TestCase					):




					@slow
					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										_snake_case									=				TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'  )

										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				prepare_layoutlm_batch_inputs()

										# forward pass
										_snake_case									=				model(input_ids=lowerCAmelCase_				,						bbox=lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )

										# test the sequence output on [0, :3, :3]
										_snake_case									=				tf.convert_to_tensor(
										    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]				,						)

										self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]				,						lowerCAmelCase_				,						atol=1E-3  )  )

										# test the pooled output on [1, :3]
										_snake_case									=				tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]  )

										self.assertTrue(np.allclose(outputs.pooler_output[1, :3]				,						lowerCAmelCase_				,						atol=1E-3  )  )




					@slow
					def 	lowerCAmelCase			(		self  )		->							List[str]:
										# initialize model with randomly initialized sequence classification head
										_snake_case									=				TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,						num_labels=2  )

										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				prepare_layoutlm_batch_inputs()

										# forward pass
										_snake_case									=				model(
										    input_ids=lowerCAmelCase_				,						bbox=lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_				,						labels=tf.convert_to_tensor([1, 1]  )				,						)

										# test whether we get a loss as a scalar
										_snake_case									=				outputs.loss
										_snake_case									=				(2,)
										self.assertEqual(loss.shape				,						lowerCAmelCase_  )

										# test the shape of the logits
										_snake_case									=				outputs.logits
										_snake_case									=				(2, 2)
										self.assertEqual(logits.shape				,						lowerCAmelCase_  )




					@slow
					def 	lowerCAmelCase			(		self  )		->							Any:
										# initialize model with randomly initialized token classification head
										_snake_case									=				TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,						num_labels=13  )

										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				prepare_layoutlm_batch_inputs()

										# forward pass
										_snake_case									=				model(
										    input_ids=lowerCAmelCase_				,						bbox=lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_				,						labels=lowerCAmelCase_  )

										# test the shape of the logits
										_snake_case									=				outputs.logits
										_snake_case									=				tf.convert_to_tensor((2, 25, 13)  )
										self.assertEqual(logits.shape				,						lowerCAmelCase_  )






					@slow
					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										# initialize model with randomly initialized token classification head
										_snake_case									=				TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'  )

										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				prepare_layoutlm_batch_inputs()

										# forward pass
										_snake_case									=				model(input_ids=lowerCAmelCase_				,						bbox=lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )

										# test the shape of the logits
										_snake_case									=				tf.convert_to_tensor((2, 25)  )
										self.assertEqual(outputs.start_logits.shape				,						lowerCAmelCase_  )
										self.assertEqual(outputs.end_logits.shape				,						lowerCAmelCase_  )










UpperCAmelCase_      							=     range(2, 20 + 1)
UpperCAmelCase_      							=     [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_      							=     {}





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       str						)			-> List[str]:



					'''simple docstring'''




					_snake_case									=				sum(a_i[j] for j in range(UpperCamelCase__       ,    len(UpperCamelCase__						)						)						)
					_snake_case									=				sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase__						)       ,    UpperCamelCase__						)						)						)

					_snake_case							,     _snake_case									=				0, 0
					_snake_case									=				n - i

					_snake_case									=				memo.get(UpperCamelCase__						)

					if sub_memo is not None:
										_snake_case									=				sub_memo.get(UpperCamelCase__						)

										if jumps is not None and len(UpperCamelCase__						) > 0:
															# find and make the largest jump without going over
															_snake_case									=				-1
															for _k in range(len(UpperCamelCase__						) - 1       ,    -1       ,    -1						):
																				if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																									_snake_case									=				_k
																									break

															if max_jump >= 0:
																				_snake_case							,     _snake_case							,     _snake_case									=				jumps[max_jump]
																				# since the difference between jumps is cached, add c
																				_snake_case									=				diff + c
																				for j in range(min(UpperCamelCase__       ,    len(UpperCamelCase__						)						)						):
																									_snake_case							,     _snake_case									=				divmod(UpperCamelCase__       ,    10						)
																				if new_c > 0:
																									add(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)

										else:
															_snake_case									=				[]
					else:
										_snake_case									=				{c: []}
										_snake_case									=				sub_memo

					if dn >= max_dn or c + diff >= base[k]:
										return diff, dn

					if k > ks[0]:
										while True:
															# keep doing smaller jumps
															_snake_case							,     _snake_case									=				next_term(UpperCamelCase__       ,    k - 1       ,    i + dn       ,    UpperCamelCase__						)
															diff += _diff
															dn += terms_jumped

															if dn >= max_dn or c + diff >= base[k]:
																				break
					else:
										# would be too small a jump, just compute sequential terms instead
										_snake_case							,     _snake_case									=				compute(UpperCamelCase__       ,    UpperCamelCase__       ,    i + dn       ,    UpperCamelCase__						)
										diff += _diff
										dn += terms_jumped

					_snake_case									=				sub_memo[c]

					# keep jumps sorted by # of terms skipped
					_snake_case									=				0
					while j < len(UpperCamelCase__						):
										if jumps[j][1] > dn:
															break
										j += 1

					# cache the jump for this value digitsum(b) and c
					sub_memo[c].insert(UpperCamelCase__       ,    (diff, dn, k)						)
					return (diff, dn)





def 						lowerCamelCase__     (     UpperCamelCase__			:       str       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       Union[str, Any]						)			-> Optional[Any]:



					'''simple docstring'''




					if i >= n:
										return 0, i
					if k > len(UpperCamelCase__						):
										a_i.extend([0 for _ in range(k - len(UpperCamelCase__						)						)]						)

					# note: a_i -> b * 10^k + c
					# ds_b -> digitsum(b)
					# ds_c -> digitsum(c)
					_snake_case									=				i
					_snake_case							,     _snake_case							,     _snake_case									=				0, 0, 0
					for j in range(len(UpperCamelCase__						)						):
										if j >= k:
															ds_b += a_i[j]
										else:
															ds_c += a_i[j]

					while i < n:
										i += 1
										_snake_case									=				ds_c + ds_b
										diff += addend
										_snake_case									=				0
										for j in range(UpperCamelCase__						):
															_snake_case									=				a_i[j] + addend
															_snake_case							,     _snake_case									=				divmod(UpperCamelCase__       ,    10						)

															ds_c += a_i[j]

										if addend > 0:
															break

					if addend > 0:
										add(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)
					return diff, i - start_i





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       Optional[Any]						)			-> Any:



					'''simple docstring'''




					for j in range(UpperCamelCase__       ,    len(UpperCamelCase__						)						):
										_snake_case									=				digits[j] + addend
										if s >= 10:
															_snake_case							,     _snake_case									=				divmod(UpperCamelCase__       ,    10						)
															_snake_case									=				addend // 10 + quotient
										else:
															_snake_case									=				s
															_snake_case									=				addend // 10

										if addend == 0:
															break

					while addend > 0:
										_snake_case							,     _snake_case									=				divmod(UpperCamelCase__       ,    10						)
										digits.append(UpperCamelCase__						)





def 						lowerCamelCase__     (     UpperCamelCase__			:       int = 10**15						)			-> int:



					'''simple docstring'''




					_snake_case									=				[1]
					_snake_case									=				1
					_snake_case									=				0
					while True:
										_snake_case							,     _snake_case									=				next_term(UpperCamelCase__       ,    20       ,    i + dn       ,    UpperCamelCase__						)
										dn += terms_jumped
										if dn == n - i:
															break

					_snake_case									=				0
					for j in range(len(UpperCamelCase__						)						):
										a_n += digits[j] * 10**j
					return a_n


if __name__ == "__main__":
					print(F"{solution() = }")



