






"""simple docstring"""


import unittest

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


if is_torch_available():
   import torch

   from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer






@require_torch
class __a   (   unittest.TestCase				):



  @slow
  def 						A_				(self  :		Dict   )		->							Optional[Any]:
   __A	:       Optional[int]       =	XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'   )
   model.to(lowercase_   )

   # encoder-decoder outputs
   __A	:       str       =	torch.tensor([[17, 96_208, 103_471, 2]]   ).to(lowercase_   )
   __A	:       Optional[int]       =	torch.tensor(
       [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]   ).to(lowercase_   )
   __A	:       Optional[int]       =	model(
       input_ids=lowercase_				,			attention_mask=lowercase_				,			encoder_outputs=lowercase_				,			decoder_input_ids=lowercase_   )
   __A	:       Optional[int]       =	output[0]
   __A	:       str       =	torch.Size((1, 14, 250_012)   )
   self.assertEqual(output_predited_logis.shape				,			lowercase_   )
   __A	:       Union[str, Any]       =	torch.tensor(
       [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]   ).to(lowercase_   )
   self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]				,			lowercase_				,			atol=1E-4   )   )

   # encoder outputs
   __A	:       Optional[Any]       =	model.prophetnet.encoder(lowercase_   )[0]
   __A	:       List[str]       =	torch.tensor(
       [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]   ).to(lowercase_   )
   __A	:       str       =	torch.Size((1, 4, 1_024)   )
   self.assertEqual(encoder_outputs.shape				,			lowercase_   )
   self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]				,			lowercase_				,			atol=1E-4   )   )

   # decoder outputs
   __A	:       Any       =	model.prophetnet.decoder(
       lowercase_				,			encoder_hidden_states=lowercase_				,			)
   __A	:       List[str]       =	decoder_outputs[1].view(1				,			model.config.ngram				,			14				,			-1   )
   __A	:       Optional[int]       =	model.lm_head(lowercase_   )
   __A	:       Dict       =	predicting_streams_logits[:, 0]
   self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]				,			lowercase_				,			atol=1E-4   )   )



  @slow
  def 						A_				(self  :		Optional[int]   )		->							Optional[Any]:
   __A	:       Dict       =	XLMProphetNetForConditionalGeneration.from_pretrained(
       'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )
   model.to(lowercase_   )

   __A	:       str       =	torch.tensor([[17, 96_208, 103_471, 2]]   ).to(lowercase_   )
   __A	:       Optional[int]       =	torch.tensor(
       [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]   ).to(lowercase_   )
   __A	:       Optional[int]       =	model(
       input_ids=lowercase_				,			attention_mask=lowercase_				,			encoder_outputs=lowercase_				,			decoder_input_ids=lowercase_   )
   __A	:       str       =	output[0]
   __A	:       Union[str, Any]       =	torch.Size((1, 14, 250_012)   )
   self.assertEqual(output_predited_logis.shape				,			lowercase_   )
   # compare the actual values for a slice.
   __A	:       Dict       =	torch.tensor(
       [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]   ).to(lowercase_   )

   self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]				,			lowercase_				,			atol=1E-4   )   )



  @slow
  def 						A_				(self  :		Union[str, Any]   )		->							Optional[Any]:
   __A	:       List[str]       =	XLMProphetNetForConditionalGeneration.from_pretrained(
       'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )
   model.to(lowercase_   )
   __A	:       str       =	512

   __A	:       str       =	XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )

   __A	:       Optional[int]       =	(
       'Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'
       ' January 14, 2020, according to the official portal of the organization. From that day, users of this'
       ' system will not be able to receive security updates, which could make their computers vulnerable to'
       ' cyber attacks.'
   )
   __A	:       Optional[Any]       =	(
       'орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'
       ' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'
       ' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'
       ' к кибератакам.'
   )
   __A	:       Optional[int]       =	(
       '根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'
   )

   __A	:       Dict       =	tokenizer(
       [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]				,			padding=lowercase_				,			max_length=255				,			return_tensors='pt'   ).input_ids
   __A	:       Optional[int]       =	input_ids.to(lowercase_   )

   __A	:       str       =	model.generate(
       lowercase_				,			num_beams=10				,			length_penalty=1.0				,			no_repeat_ngram_size=3				,			early_stopping=lowercase_   )
   __A	:       Union[str, Any]       =	[tokenizer.decode(lowercase_				,			skip_special_tokens=lowercase_   ) for g in summary_ids]
   __A	:       str       =	'Microsoft to end Windows 7 free support after January 14, 2020'
   __A	:       Optional[int]       =	'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
   __A	:       List[Any]       =	'微软打算终止对Windows 7操作系统的免费支持'
   self.assertListEqual(
       [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]				,			lowercase_				,			)

   __A	:       str       =	model.generate(
       lowercase_				,			num_beams=1				,			length_penalty=1.0				,			no_repeat_ngram_size=3				,			early_stopping=lowercase_   )
   __A	:       Optional[Any]       =	[
       tokenizer.convert_ids_to_tokens(lowercase_				,			skip_special_tokens=lowercase_   ) for g in summary_ids_beama
   ]
   __A	:       List[Any]       =	'▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '   )
   __A	:       int       =	'▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
       ' '   )
   __A	:       List[str]       =	'微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'.split(' '   )
   self.assertListEqual(
       [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]				,			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"""))





