
import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
						    LayoutLMForMaskedLM,
						    LayoutLMForQuestionAnswering,
						    LayoutLMForSequenceClassification,
						    LayoutLMForTokenClassification,
						    LayoutLMModel,
						)
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Any=1_3	,							SCREAMING_SNAKE_CASE_  :      Any=7	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=True	,							SCREAMING_SNAKE_CASE_  :      Dict=True	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=9_9	,							SCREAMING_SNAKE_CASE_  :      str=3_2	,							SCREAMING_SNAKE_CASE_  :      List[Any]=5	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=4	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_7	,							SCREAMING_SNAKE_CASE_  :      Any="gelu"	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      Any=0.1	,							SCREAMING_SNAKE_CASE_  :      Any=5_1_2	,							SCREAMING_SNAKE_CASE_  :      Dict=1_6	,							SCREAMING_SNAKE_CASE_  :      List[str]=2	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.02	,							SCREAMING_SNAKE_CASE_  :      Dict=3	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							SCREAMING_SNAKE_CASE_  :      List[str]=None	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_0_0_0	,							)	->					int:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_input_mask
					lowercase_					  = use_token_type_ids
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = num_attention_heads
					lowercase_					  = intermediate_size
					lowercase_					  = hidden_act
					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = max_position_embeddings
					lowercase_					  = type_vocab_size
					lowercase_					  = type_sequence_label_size
					lowercase_					  = initializer_range
					lowercase_					  = num_labels
					lowercase_					  = num_choices
					lowercase_					  = scope
					lowercase_					  = range_bbox
	def 						_lowercase  (	self  :      Optional[Any]	)	->					List[str]:
					lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size	)

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

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

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

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

					lowercase_					  = self.get_config()

					return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	def 						_lowercase  (	self  :      Optional[Any]	)	->					Dict:
					return 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	,							)
	def 						_lowercase  (	self  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      Any	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Any	,							SCREAMING_SNAKE_CASE_  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]	)	->					Dict:
					lowercase_					  = LayoutLMModel(config=SCREAMING_SNAKE_CASE_	)
					model.to(SCREAMING_SNAKE_CASE_	)
					model.eval()
					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
					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 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Any	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Optional[int]:
					lowercase_					  = LayoutLMForMaskedLM(config=SCREAMING_SNAKE_CASE_	)
					model.to(SCREAMING_SNAKE_CASE_	)
					model.eval()
					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	,							labels=SCREAMING_SNAKE_CASE_	)
					self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)	)
	def 						_lowercase  (	self  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Dict	,							SCREAMING_SNAKE_CASE_  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Optional[int]:
					lowercase_					  = self.num_labels
					lowercase_					  = LayoutLMForSequenceClassification(SCREAMING_SNAKE_CASE_	)
					model.to(SCREAMING_SNAKE_CASE_	)
					model.eval()
					lowercase_					  = model(
					    SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	,							labels=SCREAMING_SNAKE_CASE_	)
					self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)	)
	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      List[Any]	)	->					int:
					lowercase_					  = self.num_labels
					lowercase_					  = LayoutLMForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
					model.to(SCREAMING_SNAKE_CASE_	)
					model.eval()
					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	,							labels=SCREAMING_SNAKE_CASE_	)
					self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.num_labels)	)
	def 						_lowercase  (	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      List[str]	)	->					str:
					lowercase_					  = LayoutLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
					model.to(SCREAMING_SNAKE_CASE_	)
					model.eval()
					lowercase_					  = model(
					    SCREAMING_SNAKE_CASE_	,							bbox=SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	,							start_positions=SCREAMING_SNAKE_CASE_	,							end_positions=SCREAMING_SNAKE_CASE_	,							)
					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 						_lowercase  (	self  :      int	)	->					int:
					lowercase_					  = self.prepare_config_and_inputs()
					(
					    (
					    lowercase_
					)  ,     (
					    lowercase_
					)  ,     (
					    lowercase_
					)  ,     (
					    lowercase_
					)  ,     (
					    lowercase_
					)  ,     (
					    lowercase_
					)  ,     (
					    lowercase_
					)  ,     (
					    lowercase_
					)  ,     
					)					  = config_and_inputs
					lowercase_					  = {
					    '''input_ids''': input_ids,
					    '''bbox''': bbox,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': input_mask,
					}
					return config, inputs_dict

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



	"""simple docstring"""




	a      :List[str]   					=     (
	    (
	        LayoutLMModel,
	        LayoutLMForMaskedLM,
	        LayoutLMForSequenceClassification,
	        LayoutLMForTokenClassification,
	        LayoutLMForQuestionAnswering,
	    )
	    if is_torch_available()
	    else None
	)
	a      :List[str]   					=     (
	    {
	        'document-question-answering': LayoutLMForQuestionAnswering,
	        'feature-extraction': LayoutLMModel,
	        'fill-mask': LayoutLMForMaskedLM,
	        'text-classification': LayoutLMForSequenceClassification,
	        'token-classification': LayoutLMForTokenClassification,
	        'zero-shot': LayoutLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a      :List[str]   					=     True
	def 						_lowercase  (	self  :      Union[str, Any]	)	->					str:
					lowercase_					  = LayoutLMModelTester(self	)
					lowercase_					  = ConfigTester(self	,							config_class=SCREAMING_SNAKE_CASE_	,							hidden_size=3_7	)
	def 						_lowercase  (	self  :      List[str]	)	->					Union[str, Any]:
					self.config_tester.run_common_tests()
	def 						_lowercase  (	self  :      Any	)	->					int:
					lowercase_					  = self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					Optional[int]:
					lowercase_					  = self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									lowercase_					  = type
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      List[str]	)	->					str:
					lowercase_					  = self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      List[Any]	)	->					List[Any]:
					lowercase_					  = self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Optional[int]	)	->					int:
					lowercase_					  = self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      str	)	->					List[str]:
					lowercase_					  = self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)




	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
	def 						_lowercase  (	self  :      List[str]	)	->					Dict:
					pass




def     a						(							):


				'''simple docstring'''
				# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
				# fmt: off
				lowercase_					  = torch.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]]						,       device=snake_case__ )  # noqa: E231
				lowercase_					  = torch.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],]						,       device=snake_case__ )  # noqa: E231
				lowercase_					  = torch.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]]]						,       device=snake_case__ )  # noqa: E231
				lowercase_					  = torch.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]]						,       device=snake_case__ )  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				lowercase_					  = torch.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]]						,       device=snake_case__ )  # noqa: E231
				# fmt: on

				return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	@slow
	def 						_lowercase  (	self  :      Optional[Any]	)	->					List[str]:
					lowercase_					  = LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''	).to(SCREAMING_SNAKE_CASE_	)

					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = prepare_layoutlm_batch_inputs()

					# forward pass
					lowercase_					  = model(input_ids=SCREAMING_SNAKE_CASE_	,							bbox=SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	)

					# test the sequence output on [0, :3, :3]
					lowercase_					  = torch.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]]	,							device=SCREAMING_SNAKE_CASE_	,							)

					self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,							SCREAMING_SNAKE_CASE_	,							atol=1e-3	)	)

					# test the pooled output on [1, :3]
					lowercase_					  = torch.tensor([-0.65_80, -0.02_14, 0.85_52]	,							device=SCREAMING_SNAKE_CASE_	)

					self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]	,							SCREAMING_SNAKE_CASE_	,							atol=1e-3	)	)
	@slow
	def 						_lowercase  (	self  :      Union[str, Any]	)	->					Optional[int]:
					# initialize model with randomly initialized sequence classification head
					lowercase_					  = LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''	,							num_labels=2	).to(
					    SCREAMING_SNAKE_CASE_	)

					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = prepare_layoutlm_batch_inputs()

					# forward pass
					lowercase_					  = model(
					    input_ids=SCREAMING_SNAKE_CASE_	,							bbox=SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	,							labels=torch.tensor([1, 1]	,							device=SCREAMING_SNAKE_CASE_	)	,							)

					# test whether we get a loss as a scalar
					lowercase_					  = outputs.loss
					lowercase_					  = torch.Size([]	)
					self.assertEqual(loss.shape	,							SCREAMING_SNAKE_CASE_	)

					# test the shape of the logits
					lowercase_					  = outputs.logits
					lowercase_					  = torch.Size((2, 2)	)
					self.assertEqual(logits.shape	,							SCREAMING_SNAKE_CASE_	)
	@slow
	def 						_lowercase  (	self  :      List[str]	)	->					List[Any]:
					# initialize model with randomly initialized token classification head
					lowercase_					  = LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''	,							num_labels=1_3	).to(
					    SCREAMING_SNAKE_CASE_	)

					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = prepare_layoutlm_batch_inputs()

					# forward pass
					lowercase_					  = model(
					    input_ids=SCREAMING_SNAKE_CASE_	,							bbox=SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	,							labels=SCREAMING_SNAKE_CASE_	)

					# test the loss calculation to be around 2.65
					# expected_loss = torch.tensor(2.65, device=torch_device)

					# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
					# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

					# test the shape of the logits
					lowercase_					  = outputs.logits
					lowercase_					  = torch.Size((2, 2_5, 1_3)	)
					self.assertEqual(logits.shape	,							SCREAMING_SNAKE_CASE_	)




	@slow
	def 						_lowercase  (	self  :      Dict	)	->					List[str]:
					# initialize model with randomly initialized token classification head
					lowercase_					  = LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''	).to(SCREAMING_SNAKE_CASE_	)

					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = prepare_layoutlm_batch_inputs()

					# forward pass
					lowercase_					  = model(input_ids=SCREAMING_SNAKE_CASE_	,							bbox=SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							token_type_ids=SCREAMING_SNAKE_CASE_	)

					# test the shape of the logits
					lowercase_					  = torch.Size((2, 2_5)	)
					self.assertEqual(outputs.start_logits.shape	,							SCREAMING_SNAKE_CASE_	)
					self.assertEqual(outputs.end_logits.shape	,							SCREAMING_SNAKE_CASE_	)






class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	)	->					None:
					lowercase_					  = [0, 1]




	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      int	)	->					list:
					if (difference := index - (len(self.sequence	) - 2)) >= 1:
									for _ in range(SCREAMING_SNAKE_CASE_	):
													self.sequence.append(self.sequence[-1] + self.sequence[-2]	)
					return self.sequence[:index]




def     a						(							):


				'''simple docstring'''
				print(
				    '''Fibonacci Series Using Dynamic Programming\n'''						,       '''Enter the index of the Fibonacci number you want to calculate '''						,       '''in the prompt below. (To exit enter exit or Ctrl-C)\n'''						,       sep=''''''						,       )
				lowercase_					  = Fibonacci()

				while True:
								lowercase_					  = input('''>> ''' )
								if prompt in {"exit", "quit"}:
												break

								try:
												lowercase_					  = int(snake_case__ )
								except ValueError:
												print('''Enter a number or \'exit\'''' )
												continue

								print(fibonacci.get(snake_case__ ) )


if __name__ == "__main__":
						main()




