






"""simple docstring"""


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 __a   :



  def __init__(self  :		Optional[int]				,			lowercase_  :		List[str]				,			lowercase_  :		str=13				,			lowercase_  :		Dict=7				,			lowercase_  :		Union[str, Any]=True				,			lowercase_  :		Union[str, Any]=True				,			lowercase_  :		str=True				,			lowercase_  :		Optional[int]=True				,			lowercase_  :		int=99				,			lowercase_  :		Union[str, Any]=32				,			lowercase_  :		Optional[Any]=5				,			lowercase_  :		List[Any]=4				,			lowercase_  :		List[Any]=37				,			lowercase_  :		List[str]="gelu"				,			lowercase_  :		Optional[Any]=0.1				,			lowercase_  :		Tuple=0.1				,			lowercase_  :		Union[str, Any]=512				,			lowercase_  :		Dict=16				,			lowercase_  :		Optional[int]=2				,			lowercase_  :		Optional[int]=0.02				,			lowercase_  :		Any=3				,			lowercase_  :		Any=4				,			lowercase_  :		int=None				,			lowercase_  :		Union[str, Any]=1_000				,			)		->							List[str]:
   __A	:       List[Any]       =	parent
   __A	:       Optional[int]       =	batch_size
   __A	:       Optional[Any]       =	seq_length
   __A	:       Union[str, Any]       =	is_training
   __A	:       Tuple       =	use_input_mask
   __A	:       str       =	use_token_type_ids
   __A	:       Tuple       =	use_labels
   __A	:       Any       =	vocab_size
   __A	:       Union[str, Any]       =	hidden_size
   __A	:       Tuple       =	num_hidden_layers
   __A	:       Union[str, Any]       =	num_attention_heads
   __A	:       Any       =	intermediate_size
   __A	:       List[str]       =	hidden_act
   __A	:       Tuple       =	hidden_dropout_prob
   __A	:       str       =	attention_probs_dropout_prob
   __A	:       str       =	max_position_embeddings
   __A	:       Dict       =	type_vocab_size
   __A	:       int       =	type_sequence_label_size
   __A	:       List[str]       =	initializer_range
   __A	:       str       =	num_labels
   __A	:       Union[str, Any]       =	num_choices
   __A	:       List[str]       =	scope
   __A	:       Optional[Any]       =	range_bbox



  def 						A_				(self  :		List[Any]   )		->							Optional[Any]:
   __A	:       Optional[Any]       =	ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

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

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

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

   __A	:       Union[str, Any]       =	None
   __A	:       Union[str, Any]       =	None
   __A	:       List[str]       =	None
   if self.use_labels:
    __A	:       Dict       =	ids_tensor([self.batch_size]				,			self.type_sequence_label_size   )
    __A	:       str       =	ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels   )
    __A	:       Union[str, Any]       =	ids_tensor([self.batch_size]				,			self.num_choices   )

   __A	:       Any       =	self.get_config()

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



  def 						A_				(self  :		List[Any]   )		->							List[str]:
   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 						A_				(self  :		List[str]				,			lowercase_  :		Optional[int]				,			lowercase_  :		Optional[Any]				,			lowercase_  :		Tuple				,			lowercase_  :		int				,			lowercase_  :		List[str]				,			lowercase_  :		str				,			lowercase_  :		int				,			lowercase_  :		str   )		->							List[str]:
   __A	:       Optional[Any]       =	LayoutLMModel(config=lowercase_   )
   model.to(lowercase_   )
   model.eval()
   __A	:       Dict       =	model(lowercase_				,			lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_   )
   __A	:       Optional[Any]       =	model(lowercase_				,			lowercase_				,			token_type_ids=lowercase_   )
   __A	:       List[Any]       =	model(lowercase_				,			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  :		List[Any]				,			lowercase_  :		Tuple				,			lowercase_  :		int				,			lowercase_  :		Optional[int]				,			lowercase_  :		Union[str, Any]				,			lowercase_  :		List[Any]				,			lowercase_  :		int				,			lowercase_  :		List[str]				,			lowercase_  :		Optional[int]   )		->							int:
   __A	:       List[Any]       =	LayoutLMForMaskedLM(config=lowercase_   )
   model.to(lowercase_   )
   model.eval()
   __A	:       List[Any]       =	model(lowercase_				,			lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_   )
   self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)   )



  def 						A_				(self  :		Any				,			lowercase_  :		Union[str, Any]				,			lowercase_  :		str				,			lowercase_  :		Tuple				,			lowercase_  :		int				,			lowercase_  :		Dict				,			lowercase_  :		List[str]				,			lowercase_  :		Any				,			lowercase_  :		Optional[Any]   )		->							Tuple:
   __A	:       str       =	self.num_labels
   __A	:       List[str]       =	LayoutLMForSequenceClassification(lowercase_   )
   model.to(lowercase_   )
   model.eval()
   __A	:       Tuple       =	model(
       lowercase_				,			lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_   )
   self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)   )



  def 						A_				(self  :		Any				,			lowercase_  :		Any				,			lowercase_  :		Optional[Any]				,			lowercase_  :		List[str]				,			lowercase_  :		List[str]				,			lowercase_  :		Tuple				,			lowercase_  :		int				,			lowercase_  :		str				,			lowercase_  :		Dict   )		->							Optional[Any]:
   __A	:       Optional[Any]       =	self.num_labels
   __A	:       Optional[int]       =	LayoutLMForTokenClassification(config=lowercase_   )
   model.to(lowercase_   )
   model.eval()
   __A	:       List[str]       =	model(lowercase_				,			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  :		str				,			lowercase_  :		Optional[int]				,			lowercase_  :		Optional[Any]				,			lowercase_  :		Dict				,			lowercase_  :		Optional[int]				,			lowercase_  :		Tuple				,			lowercase_  :		Dict				,			lowercase_  :		List[Any]				,			lowercase_  :		Optional[int]   )		->							List[Any]:
   __A	:       Union[str, Any]       =	LayoutLMForQuestionAnswering(config=lowercase_   )
   model.to(lowercase_   )
   model.eval()
   __A	:       int       =	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  :		int   )		->							Optional[Any]:
   __A	:       List[str]       =	self.prepare_config_and_inputs()
   (
       (
       __A
   )    ,					(
       __A
   )    ,					(
       __A
   )    ,					(
       __A
   )    ,					(
       __A
   )    ,					(
       __A
   )    ,					(
       __A
   )    ,					(
       __A
   )    ,					
   )	:       Union[str, Any]       =	config_and_inputs
   __A	:       List[str]       =	{
       'input_ids': input_ids,
       'bbox': bbox,
       'token_type_ids': token_type_ids,
       'attention_mask': input_mask,
   }
   return config, inputs_dict






@require_torch
class __a   (   snake_case_ ,snake_case_ ,unittest.TestCase				):
  __lowercase    :    List[str]						=					(
      (
          LayoutLMModel,
          LayoutLMForMaskedLM,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  __lowercase    :    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 {}
  )
  __lowercase    :    str						=					True



  def 						A_				(self  :		Optional[Any]   )		->							List[Any]:
   __A	:       List[str]       =	LayoutLMModelTester(self   )
   __A	:       Union[str, Any]       =	ConfigTester(self				,			config_class=lowercase_				,			hidden_size=37   )



  def 						A_				(self  :		Optional[Any]   )		->							int:
   self.config_tester.run_common_tests()



  def 						A_				(self  :		Optional[int]   )		->							int:
   __A	:       List[str]       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase_   )



  def 						A_				(self  :		List[str]   )		->							Any:
   __A	:       Dict       =	self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
    __A	:       Optional[Any]       =	type
    self.model_tester.create_and_check_model(*lowercase_   )



  def 						A_				(self  :		Optional[Any]   )		->							Any:
   __A	:       Optional[int]       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase_   )



  def 						A_				(self  :		List[Any]   )		->							Union[str, Any]:
   __A	:       int       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*lowercase_   )



  def 						A_				(self  :		Tuple   )		->							Union[str, Any]:
   __A	:       List[str]       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase_   )



  def 						A_				(self  :		Any   )		->							List[Any]:
   __A	:       List[str]       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowercase_   )



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






def 				a (     )  ->		Any:
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 __A	:       str       =	torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]     ,	device=UpperCamelCase_		)  # noqa: E231
 __A	:       Any       =	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=UpperCamelCase_		)  # noqa: E231
 __A	:       List[str]       =	torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]     ,	device=UpperCamelCase_		)  # noqa: E231
 __A	:       Union[str, Any]       =	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=UpperCamelCase_		)  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 __A	:       int       =	torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]     ,	device=UpperCamelCase_		)  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class __a   (   unittest.TestCase				):



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

   __A    ,					__A    ,					__A    ,					__A    ,					__A	:       Tuple       =	prepare_layoutlm_batch_inputs()

   # forward pass
   __A	:       Union[str, Any]       =	model(input_ids=lowercase_				,			bbox=lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_   )

   # test the sequence output on [0, :3, :3]
   __A	:       Optional[Any]       =	torch.tensor(
       [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]				,			device=lowercase_				,			)

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

   # test the pooled output on [1, :3]
   __A	:       List[str]       =	torch.tensor([-0.6580, -0.0214, 0.8552]				,			device=lowercase_   )

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



  @slow
  def 						A_				(self  :		Any   )		->							int:
   # initialize model with randomly initialized sequence classification head
   __A	:       str       =	LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,			num_labels=2   ).to(
       lowercase_   )

   __A    ,					__A    ,					__A    ,					__A    ,					__A	:       Optional[Any]       =	prepare_layoutlm_batch_inputs()

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

   # test whether we get a loss as a scalar
   __A	:       Dict       =	outputs.loss
   __A	:       List[str]       =	torch.Size([]   )
   self.assertEqual(loss.shape				,			lowercase_   )

   # test the shape of the logits
   __A	:       int       =	outputs.logits
   __A	:       Any       =	torch.Size((2, 2)   )
   self.assertEqual(logits.shape				,			lowercase_   )



  @slow
  def 						A_				(self  :		int   )		->							Any:
   # initialize model with randomly initialized token classification head
   __A	:       Tuple       =	LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,			num_labels=13   ).to(
       lowercase_   )

   __A    ,					__A    ,					__A    ,					__A    ,					__A	:       List[Any]       =	prepare_layoutlm_batch_inputs()

   # forward pass
   __A	:       int       =	model(
       input_ids=lowercase_				,			bbox=lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_   )

   # 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
   __A	:       Dict       =	outputs.logits
   __A	:       Dict       =	torch.Size((2, 25, 13)   )
   self.assertEqual(logits.shape				,			lowercase_   )



  @slow
  def 						A_				(self  :		Optional[Any]   )		->							Tuple:
   # initialize model with randomly initialized token classification head
   __A	:       Dict       =	LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'   ).to(lowercase_   )

   __A    ,					__A    ,					__A    ,					__A    ,					__A	:       Optional[int]       =	prepare_layoutlm_batch_inputs()

   # forward pass
   __A	:       Dict       =	model(input_ids=lowercase_				,			bbox=lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_   )

   # test the shape of the logits
   __A	:       Union[str, Any]       =	torch.Size((2, 25)   )
   self.assertEqual(outputs.start_logits.shape				,			lowercase_   )
   self.assertEqual(outputs.end_logits.shape				,			lowercase_   )













"""simple docstring"""


from string import ascii_uppercase




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				str     ,	UpperCamelCase_							:				bool = False     ,	UpperCamelCase_							:				str = ascii_uppercase		)  ->		str:
 __A	:       List[str]       =	keyword.upper()
 __A	:       str       =	plaintext.upper()
 __A	:       int       =	set(UpperCamelCase_		)

 # create a list of unique characters in the keyword - their order matters
 # it determines how we will map plaintext characters to the ciphertext
 __A	:       int       =	[]
 for char in keyword:
  if char in alphabet_set and char not in unique_chars:
   unique_chars.append(UpperCamelCase_		)
    # the number of those unique characters will determine the number of rows
 __A	:       Any       =	len(UpperCamelCase_		)

 # create a shifted version of the alphabet
 __A	:       str       =	unique_chars + [
     char for char in alphabet if char not in unique_chars
 ]

 # create a modified alphabet by splitting the shifted alphabet into rows
 __A	:       int       =	[
     shifted_alphabet[k : k + num_unique_chars_in_keyword]
     for k in range(0     ,	2_6     ,	UpperCamelCase_		)
 ]

 # map the alphabet characters to the modified alphabet characters
 # going 'vertically' through the modified alphabet - consider columns first
 __A	:       Any       =	{}
 __A	:       Union[str, Any]       =	0
 for column in range(UpperCamelCase_		):
  for row in modified_alphabet:
   # if current row (the last one) is too short, break out of loop
   if len(UpperCamelCase_		) <= column:
    break

   # map current letter to letter in modified alphabet
   __A	:       str       =	row[column]
   letter_index += 1

 if verbose:
  print(UpperCamelCase_		)
 # create the encrypted text by mapping the plaintext to the modified alphabet
 return "".join(mapping[char] if char in mapping else char for char in plaintext		)


if __name__ == "__main__":
   # example use
   print(mixed_keyword("""college""", """UNIVERSITY"""))





