


import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_FOR_PRETRAINING_MAPPING,
	    AlbertForMaskedLM,
	    AlbertForMultipleChoice,
	    AlbertForPreTraining,
	    AlbertForQuestionAnswering,
	    AlbertForSequenceClassification,
	    AlbertForTokenClassification,
	    AlbertModel,
	)
	from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class     UpperCamelCase:


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1_3     ,    SCREAMING_SNAKE_CASE	:		Dict=7     ,    SCREAMING_SNAKE_CASE	:		Dict=True     ,    SCREAMING_SNAKE_CASE	:		Any=True     ,    SCREAMING_SNAKE_CASE	:		int=True     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=True     ,    SCREAMING_SNAKE_CASE	:		List[Any]=9_9     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		List[Any]=3_6     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=6     ,    SCREAMING_SNAKE_CASE	:		Tuple=6     ,    SCREAMING_SNAKE_CASE	:		Dict=6     ,    SCREAMING_SNAKE_CASE	:		Any=3_7     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Dict=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=1_6     ,    SCREAMING_SNAKE_CASE	:		int=2     ,    SCREAMING_SNAKE_CASE	:		str=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=3     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=4     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    )			->	Dict:




											'''simple docstring'''

											__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				=		embedding_size
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_hidden_groups
											__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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		ids_tensor([self.batch_size, self.seq_length]     ,    self.vocab_size						)

											__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				=		self.get_config()

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	str:




											'''simple docstring'''

											return AlbertConfig(
											    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     ,    num_hidden_groups=self.num_hidden_groups     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		AlbertModel(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE						)
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE						)
											__snake_case				=		model(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 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		AlbertForPreTraining(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE     ,    sentence_order_label=SCREAMING_SNAKE_CASE     ,    )
											self.parent.assertEqual(result.prediction_logits.shape     ,    (self.batch_size, self.seq_length, self.vocab_size)						)
											self.parent.assertEqual(result.sop_logits.shape     ,    (self.batch_size, config.num_labels)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		AlbertForMaskedLM(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(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 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		AlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(
											    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 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		AlbertForSequenceClassification(SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(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 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		AlbertForTokenClassification(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(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 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.num_choices
											__snake_case				=		AlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		input_ids.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		token_type_ids.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		input_mask.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		model(
											    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_choices)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.prepare_config_and_inputs()
											(
											    (
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					
											)				=		config_and_inputs
											__snake_case				=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_torch
class     UpperCamelCase(  _a     ,							_a     ,							unittest.TestCase   ):
					snake_case_		:				Any										=	(
					    (
					        AlbertModel,
					        AlbertForPreTraining,
					        AlbertForMaskedLM,
					        AlbertForMultipleChoice,
					        AlbertForSequenceClassification,
					        AlbertForTokenClassification,
					        AlbertForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					snake_case_		:				List[Any]										=	(
					    {
					        """feature-extraction""": AlbertModel,
					        """fill-mask""": AlbertForMaskedLM,
					        """question-answering""": AlbertForQuestionAnswering,
					        """text-classification""": AlbertForSequenceClassification,
					        """token-classification""": AlbertForTokenClassification,
					        """zero-shot""": AlbertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					snake_case_		:				Dict										=	True


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=False						)			->	str:




											'''simple docstring'''

											__snake_case				=		super()._prepare_for_class(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    return_labels=SCREAMING_SNAKE_CASE						)

											if return_labels:
																	if model_class in get_values(SCREAMING_SNAKE_CASE						):
																							__snake_case				=		torch.zeros(
																							    (self.model_tester.batch_size, self.model_tester.seq_length)     ,    dtype=torch.long     ,    device=SCREAMING_SNAKE_CASE						)
																							__snake_case				=		torch.zeros(
																							    self.model_tester.batch_size     ,    dtype=torch.long     ,    device=SCREAMING_SNAKE_CASE						)
											return inputs_dict


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		AlbertModelTester(self						)
											__snake_case				=		ConfigTester(self     ,    config_class=SCREAMING_SNAKE_CASE     ,    hidden_size=3_7						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Tuple:




											'''simple docstring'''

											self.config_tester.run_common_tests()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																	__snake_case				=		type
																	self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE						)






					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	str:




											'''simple docstring'''

											for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case				=		AlbertModel.from_pretrained(SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE						)






@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	int:




											'''simple docstring'''

											__snake_case				=		AlbertModel.from_pretrained("albert-base-v2"						)
											__snake_case				=		torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]						)
											__snake_case				=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
											with torch.no_grad():
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE						)[0]
											__snake_case				=		torch.Size((1, 1_1, 7_6_8)						)
											self.assertEqual(output.shape     ,    SCREAMING_SNAKE_CASE						)
											__snake_case				=		torch.tensor(
											    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]						)

											self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,    SCREAMING_SNAKE_CASE     ,    atol=1e-4						)						)









import itertools
import string
from collections.abc import Generator, Iterable





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Generator[tuple[str, ...], None, None]:


						'''simple docstring'''






						__snake_case				=		iter(_lowerCAmelCase )
						while True:
												__snake_case				=		tuple(itertools.islice(_lowerCAmelCase      ,	_lowerCAmelCase ) )
												if not chunk:
																		return
												yield chunk





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		"".join([c.upper() for c in dirty if c in string.ascii_letters] )
						__snake_case				=		""

						if len(_lowerCAmelCase ) < 2:
												return dirty

						for i in range(len(_lowerCAmelCase ) - 1 ):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowerCAmelCase ) & 1:
												clean += "X"

						return clean





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	list[str]:


						'''simple docstring'''






						__snake_case				=		"ABCDEFGHIKLMNOPQRSTUVWXYZ"
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						__snake_case				=		[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowerCAmelCase )

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowerCAmelCase )

						return table





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		generate_table(_lowerCAmelCase )
						__snake_case				=		prepare_input(_lowerCAmelCase )
						__snake_case				=		""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowerCAmelCase      ,	2 ):
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		generate_table(_lowerCAmelCase )
						__snake_case				=		""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowerCAmelCase      ,	2 ):
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





