




import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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 (
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer

class 	__lowerCAmelCase			:
							def __init__(		self		:Tuple	,   __magic_name__		:List[Any]	,   __magic_name__		:Any=13	,   __magic_name__		:Any=7	,   __magic_name__		:Optional[Any]=True	,   __magic_name__		:Optional[int]=True	,   __magic_name__		:Optional[Any]=True	,   __magic_name__		:str=True	,   __magic_name__		:List[str]=99	,   __magic_name__		:int=32	,   __magic_name__		:Tuple=5	,   __magic_name__		:List[Any]=4	,   __magic_name__		:Optional[Any]=37	,   __magic_name__		:str="gelu"	,   __magic_name__		:List[Any]=0.1	,   __magic_name__		:int=0.1	,   __magic_name__		:int=512	,   __magic_name__		:Any=16	,   __magic_name__		:List[Any]=2	,   __magic_name__		:Any=0.02	,   __magic_name__		:Any=3	,   __magic_name__		:str=None	,   __magic_name__		:List[str]=20	,   __magic_name__		:Union[str, Any]=30	,   __magic_name__		:int=2	,   __magic_name__		:Optional[Any]=2	,   __magic_name__		:int=10	,   ):







								'''simple docstring'''







								a		       =							parent
								a		       =							batch_size
								a		       =							seq_length
								a		       =							is_training
								a		       =							use_input_mask
								a		       =							use_token_type_ids
								a		       =							use_labels
								a		       =							vocab_size
								a		       =							hidden_size
								a		       =							num_hidden_layers
								a		       =							num_attention_heads
								a		       =							intermediate_size
								a		       =							hidden_act
								a		       =							hidden_dropout_prob
								a		       =							attention_probs_dropout_prob
								a		       =							max_position_embeddings
								a		       =							type_vocab_size
								a		       =							type_sequence_label_size
								a		       =							initializer_range
								a		       =							num_labels
								a		       =							scope
								a		       =							max_xpath_tag_unit_embeddings
								a		       =							max_xpath_subs_unit_embeddings
								a		       =							tag_pad_id
								a		       =							subs_pad_id
								a		       =							max_depth
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







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

								a		       =							ids_tensor(
								    [self.batch_size, self.seq_length, self.max_depth]	,   self.max_xpath_tag_unit_embeddings       )

								a		       =							ids_tensor(
								    [self.batch_size, self.seq_length, self.max_depth]	,   self.max_xpath_subs_unit_embeddings       )

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

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

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

								a		       =							self.get_config()

								return (
								    config,
								    input_ids,
								    xpath_tags_seq,
								    xpath_subs_seq,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								)
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								return MarkupLMConfig(
								    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	,   max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings	,   max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings	,   tag_pad_id=self.tag_pad_id	,   subs_pad_id=self.subs_pad_id	,   max_depth=self.max_depth	,   )
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:str	,   __magic_name__		:Optional[Any]	,   __magic_name__		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:List[Any]	,   __magic_name__		:Tuple	,   __magic_name__		:List[Any]	,   __magic_name__		:str	,   ):







								'''simple docstring'''







								a		       =							MarkupLMModel(config=__magic_name__       )
								model.to(__magic_name__       )
								model.eval()
								print("""Configs:"""	,   model.config.tag_pad_id	,   model.config.subs_pad_id       )
								a		       =							model(__magic_name__	,   attention_mask=__magic_name__	,   token_type_ids=__magic_name__       )
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__       )
								a		       =							model(__magic_name__       )
								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		:List[str]	,   __magic_name__		:str	,   __magic_name__		:Tuple	,   __magic_name__		:Optional[Any]	,   __magic_name__		:List[Any]	,   __magic_name__		:Dict	,   __magic_name__		:str	,   __magic_name__		:Tuple	,   __magic_name__		:Optional[Any]	,   ):







								'''simple docstring'''







								a		       =							self.num_labels
								a		       =							MarkupLMForSequenceClassification(__magic_name__       )
								model.to(__magic_name__       )
								model.eval()
								a		       =							model(
								    __magic_name__	,   xpath_tags_seq=__magic_name__	,   xpath_subs_seq=__magic_name__	,   attention_mask=__magic_name__	,   token_type_ids=__magic_name__	,   labels=__magic_name__	,   )
								self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)       )
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:List[Any]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:int	,   __magic_name__		:int	,   __magic_name__		:Dict	,   __magic_name__		:int	,   __magic_name__		:str	,   __magic_name__		:Any	,   ):







								'''simple docstring'''







								a		       =							self.num_labels
								a		       =							MarkupLMForTokenClassification(config=__magic_name__       )
								model.to(__magic_name__       )
								model.eval()
								a		       =							model(
								    __magic_name__	,   xpath_tags_seq=__magic_name__	,   xpath_subs_seq=__magic_name__	,   attention_mask=__magic_name__	,   token_type_ids=__magic_name__	,   labels=__magic_name__	,   )
								self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.num_labels)       )
							def    lowerCamelCase__							(		self		:str	,   __magic_name__		:str	,   __magic_name__		:int	,   __magic_name__		:int	,   __magic_name__		:str	,   __magic_name__		:Optional[int]	,   __magic_name__		:Dict	,   __magic_name__		:List[str]	,   __magic_name__		:Any	,   ):







								'''simple docstring'''







								a		       =							MarkupLMForQuestionAnswering(config=__magic_name__       )
								model.to(__magic_name__       )
								model.eval()
								a		       =							model(
								    __magic_name__	,   xpath_tags_seq=__magic_name__	,   xpath_subs_seq=__magic_name__	,   attention_mask=__magic_name__	,   token_type_ids=__magic_name__	,   start_positions=__magic_name__	,   end_positions=__magic_name__	,   )
								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		:Optional[Any]       ):







								'''simple docstring'''







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







@require_torch
class 	__lowerCAmelCase			(			__magic_name__   , __magic_name__   , unittest.TestCase				):
							UpperCamelCase__        =						(
							    (
							        MarkupLMModel,
							        MarkupLMForSequenceClassification,
							        MarkupLMForTokenClassification,
							        MarkupLMForQuestionAnswering,
							    )
							    if is_torch_available()
							    else None
							)
							UpperCamelCase__        =						(
							    {
							        '''feature-extraction''': MarkupLMModel,
							        '''question-answering''': MarkupLMForQuestionAnswering,
							        '''text-classification''': MarkupLMForSequenceClassification,
							        '''token-classification''': MarkupLMForTokenClassification,
							        '''zero-shot''': MarkupLMForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							def    lowerCamelCase__							(		self		:int	,   __magic_name__		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:Any	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Dict       ):







								'''simple docstring'''







								return True
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							MarkupLMModelTester(self       )
								a		       =							ConfigTester(self	,   config_class=__magic_name__	,   hidden_size=37       )
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__magic_name__       )
							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__magic_name__       )





							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__magic_name__       )







def 	__A	(			)   -> Optional[Any]:
	a		       =							"""
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    """

	return html_string



@require_torch
class 	__lowerCAmelCase			(			unittest.TestCase				):
							@cached_property
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a		       =							MarkupLMFeatureExtractor()
								a		       =							MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""       )

								return MarkupLMProcessor(__magic_name__	,   __magic_name__       )





							@slow
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								a		       =							MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""       ).to(__magic_name__       )

								a		       =							self.default_processor

								a		       =							processor(prepare_html_string()	,   return_tensors="""pt"""       )
								a		       =							inputs.to(__magic_name__       )

								# forward pass
								with torch.no_grad():
									a		       =							model(**__magic_name__       )

								# verify the last hidden states
								a		       =							torch.Size([1, 14, 768]       )
								self.assertEqual(outputs.last_hidden_state.shape	,   __magic_name__       )

								a		       =							torch.tensor(
								    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]       ).to(__magic_name__       )

								self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,   __magic_name__	,   atol=1E-4       )       )







__UpperCamelCase      : Union[str, Any]					      =   {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}





def 	__A	(			__lowerCamelCase					)   -> list[tuple[str, str]]:
	return [alphabet[char] for char in key.upper()]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							""""""
	a		       =							0
	a		       =							generate_table(__lowerCamelCase					)
	for char in words.upper():
		cipher += get_opponent(table[count]							,    __lowerCamelCase					)
		a		       =							(count + 1) % len(__lowerCamelCase					)
	return cipher





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	return encrypt(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> tuple[int, int]:
	a		       =							0 if char in table[0] else 1
	a		       =							table[row].index(__lowerCamelCase					)
	return row, col





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a       ,				a		       =							get_position(__lowerCamelCase							,    char.upper()					)
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
		import doctest

		doctest.testmod()  # Fist ensure that all our tests are passing...
		__UpperCamelCase      : int					      =   input("Enter key: ").strip()
		__UpperCamelCase      : int					      =   input("Enter text to encrypt: ").strip()
		__UpperCamelCase      : Optional[Any]					      =   encrypt(key, text)

		print(F'Encrypted: {cipher_text}')
		print(F'Decrypted with key: {decrypt(key, cipher_text)}')

