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        SCREAMING_SNAKE_CASE   (				unittest.TestCase ):
  """simple docstring"""







  @slow
  def   a   (  self   :					List[str]					):


        """simple docstring"""



        _lowerCAmelCase		   =   XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'					)
        model.to(__lowerCAmelCase					)

        # encoder-decoder outputs
        _lowerCAmelCase		   =   torch.tensor([[17, 9_6208, 10_3471, 2]]					).to(__lowerCAmelCase					)
        _lowerCAmelCase		   =   torch.tensor(
            [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]					).to(__lowerCAmelCase					)
        _lowerCAmelCase		   =   model(
            input_ids=__lowerCAmelCase					,					attention_mask=__lowerCAmelCase					,					encoder_outputs=__lowerCAmelCase					,					decoder_input_ids=__lowerCAmelCase					)
        _lowerCAmelCase		   =   output[0]
        _lowerCAmelCase		   =   torch.Size((1, 14, 25_0012)					)
        self.assertEqual(output_predited_logis.shape					,					__lowerCAmelCase					)
        _lowerCAmelCase		   =   torch.tensor(
            [[[-6.3_9_8_6, -8.2_3_9_1, 12.5189], [-6.3_2_8_9, -8.0_8_6_4, 12.6211], [-6.2_4_1_8, -8.0_4_4_5, 12.7968]]]					).to(__lowerCAmelCase					)
        self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					,					__lowerCAmelCase					,					atol=1e-4					)					)

        # encoder outputs
        _lowerCAmelCase		   =   model.prophetnet.encoder(__lowerCAmelCase					)[0]
        _lowerCAmelCase		   =   torch.tensor(
            [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]					).to(__lowerCAmelCase					)
        _lowerCAmelCase		   =   torch.Size((1, 4, 1024)					)
        self.assertEqual(encoder_outputs.shape					,					__lowerCAmelCase					)
        self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]					,					__lowerCAmelCase					,					atol=1e-4					)					)

        # decoder outputs
        _lowerCAmelCase		   =   model.prophetnet.decoder(
            __lowerCAmelCase					,					encoder_hidden_states=__lowerCAmelCase					,					)
        _lowerCAmelCase		   =   decoder_outputs[1].view(1					,					model.config.ngram					,					14					,					-1					)
        _lowerCAmelCase		   =   model.lm_head(__lowerCAmelCase					)
        _lowerCAmelCase		   =   predicting_streams_logits[:, 0]
        self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]					,					__lowerCAmelCase					,					atol=1e-4					)					)



  @slow
  def   a   (  self   :					Tuple					):


        """simple docstring"""



        _lowerCAmelCase		   =   XLMProphetNetForConditionalGeneration.from_pretrained(
            'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'					)
        model.to(__lowerCAmelCase					)

        _lowerCAmelCase		   =   torch.tensor([[17, 9_6208, 10_3471, 2]]					).to(__lowerCAmelCase					)
        _lowerCAmelCase		   =   torch.tensor(
            [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]					).to(__lowerCAmelCase					)
        _lowerCAmelCase		   =   model(
            input_ids=__lowerCAmelCase					,					attention_mask=__lowerCAmelCase					,					encoder_outputs=__lowerCAmelCase					,					decoder_input_ids=__lowerCAmelCase					)
        _lowerCAmelCase		   =   output[0]
        _lowerCAmelCase		   =   torch.Size((1, 14, 25_0012)					)
        self.assertEqual(output_predited_logis.shape					,					__lowerCAmelCase					)
        # compare the actual values for a slice.
        _lowerCAmelCase		   =   torch.tensor(
            [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]					).to(__lowerCAmelCase					)

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



  @slow
  def   a   (  self   :					int					):


        """simple docstring"""



        _lowerCAmelCase		   =   XLMProphetNetForConditionalGeneration.from_pretrained(
            'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'					)
        model.to(__lowerCAmelCase					)
        _lowerCAmelCase		   =   512

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

        _lowerCAmelCase		   =   (
            '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.'
        )
        _lowerCAmelCase		   =   (
            '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.'
        )
        _lowerCAmelCase		   =   (
            '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。'
        )

        _lowerCAmelCase		   =   tokenizer(
            [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]					,					padding=__lowerCAmelCase					,					max_length=255					,					return_tensors='pt'					).input_ids
        _lowerCAmelCase		   =   input_ids.to(__lowerCAmelCase					)

        _lowerCAmelCase		   =   model.generate(
            __lowerCAmelCase					,					num_beams=10					,					length_penalty=1.0					,					no_repeat_ngram_size=3					,					early_stopping=__lowerCAmelCase					)
        _lowerCAmelCase		   =   [tokenizer.decode(__lowerCAmelCase					,					skip_special_tokens=__lowerCAmelCase					) for g in summary_ids]
        _lowerCAmelCase		   =   'Microsoft to end Windows 7 free support after January 14, 2020'
        _lowerCAmelCase		   =   'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
        _lowerCAmelCase		   =   '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]					,					__lowerCAmelCase					,					)

        _lowerCAmelCase		   =   model.generate(
            __lowerCAmelCase					,					num_beams=1					,					length_penalty=1.0					,					no_repeat_ngram_size=3					,					early_stopping=__lowerCAmelCase					)
        _lowerCAmelCase		   =   [
            tokenizer.convert_ids_to_tokens(__lowerCAmelCase					,					skip_special_tokens=__lowerCAmelCase					) for g in summary_ids_beama
        ]
        _lowerCAmelCase		   =   '▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '					)
        _lowerCAmelCase		   =   '▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
            ' '					)
        _lowerCAmelCase		   =   '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]					,					__lowerCAmelCase					,					)






'''simple docstring'''




from __future__ import annotations







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							dict[str, str]    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', '', ''
      _lowerCAmelCase		   =   []

      for character in message_part:
            tmp.append(character_to_number[character]    )

      for each in tmp:
            one += each[0]
            two += each[1]
            three += each[2]

      return one + two + three







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							dict[str, str]    ):
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', ''
      _lowerCAmelCase		   =   []

      for character in message_part:
            this_part += character_to_number[character]

      for digit in this_part:
            tmp += digit
            if len(_lowerCamelCase    ) == len(_lowerCamelCase    ):
                  result.append(_lowerCamelCase    )
                  _lowerCAmelCase		   =   ''

      return result[0], result[1], result[2]







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str    ):
      # Validate message and alphabet, set to upper and remove spaces
      _lowerCAmelCase		   =   alphabet.replace(' '  ,  ''    ).upper()
      _lowerCAmelCase		   =   message.replace(' '  ,  ''    ).upper()

      # Check length and characters
      if len(_lowerCamelCase    ) != 27:
            raise KeyError('Length of alphabet has to be 27.'    )
      for each in message:
            if each not in alphabet:
                  raise ValueError('Each message character has to be included in alphabet!'    )

    # Generate dictionares
      _lowerCAmelCase		   =   (
          '111',
          '112',
          '113',
          '121',
          '122',
          '123',
          '131',
          '132',
          '133',
          '211',
          '212',
          '213',
          '221',
          '222',
          '223',
          '231',
          '232',
          '233',
          '311',
          '312',
          '313',
          '321',
          '322',
          '323',
          '331',
          '332',
          '333',
      )
      _lowerCAmelCase		   =   {}
      _lowerCAmelCase		   =   {}
      for letter, number in zip(_lowerCamelCase  ,  _lowerCamelCase    ):
            _lowerCAmelCase		   =   number
            _lowerCAmelCase		   =   letter

      return message, alphabet, character_to_number, number_to_character







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,  _lowerCamelCase			:							int = 5    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __prepare(
          _lowerCamelCase  ,  _lowerCamelCase    )
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', ''

      for i in range(0  ,  len(_lowerCamelCase    ) + 1  ,  _lowerCamelCase    ):
            encrypted_numeric += __encrypt_part(
                message[i : i + period]  ,  _lowerCamelCase    )

      for i in range(0  ,  len(_lowerCamelCase    )  ,  3    ):
            encrypted += number_to_character[encrypted_numeric[i : i + 3]]

      return encrypted







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,  _lowerCamelCase			:							int = 5    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __prepare(
          _lowerCamelCase  ,  _lowerCamelCase    )
      _lowerCAmelCase		   =   []
      _lowerCAmelCase		   =   ''

      for i in range(0  ,  len(_lowerCamelCase    ) + 1  ,  _lowerCamelCase    ):
            _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __decrypt_part(message[i : i + period]  ,  _lowerCamelCase    )

            for j in range(0  ,  len(_lowerCamelCase    )    ):
                  decrypted_numeric.append(a[j] + b[j] + c[j]    )

      for each in decrypted_numeric:
            decrypted += number_to_character[each]

      return decrypted


if __name__ == "__main__":
 snake_case  =			'''DEFEND THE EAST WALL OF THE CASTLE.'''
 snake_case  =			encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
 snake_case  =			decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
 print(f'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')





