






"""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  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

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

     '''simple docstring'''
     __A						      =			XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''  )
     model.to(_lowerCamelCase  )

     # encoder-decoder outputs
     __A						      =			torch.tensor([[17, 9_62_08, 10_34_71, 2]]  ).to(_lowerCamelCase  )
     __A						      =			torch.tensor(
         [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]  ).to(_lowerCamelCase  )
     __A						      =			model(
         input_ids=_lowerCamelCase,	attention_mask=_lowerCamelCase,	encoder_outputs=_lowerCamelCase,	decoder_input_ids=_lowerCamelCase  )
     __A						      =			output[0]
     __A						      =			torch.Size((1, 14, 25_00_12)  )
     self.assertEqual(output_predited_logis.shape,	_lowerCamelCase  )
     __A						      =			torch.tensor(
         [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]  ).to(_lowerCamelCase  )
     self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3],	_lowerCamelCase,	atol=1e-4  )  )

     # encoder outputs
     __A						      =			model.prophetnet.encoder(_lowerCamelCase  )[0]
     __A						      =			torch.tensor(
         [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]  ).to(_lowerCamelCase  )
     __A						      =			torch.Size((1, 4, 10_24)  )
     self.assertEqual(encoder_outputs.shape,	_lowerCamelCase  )
     self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3],	_lowerCamelCase,	atol=1e-4  )  )

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


   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			XLMProphetNetForConditionalGeneration.from_pretrained(
         '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''  )
     model.to(_lowerCamelCase  )

     __A						      =			torch.tensor([[17, 9_62_08, 10_34_71, 2]]  ).to(_lowerCamelCase  )
     __A						      =			torch.tensor(
         [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]  ).to(_lowerCamelCase  )
     __A						      =			model(
         input_ids=_lowerCamelCase,	attention_mask=_lowerCamelCase,	encoder_outputs=_lowerCamelCase,	decoder_input_ids=_lowerCamelCase  )
     __A						      =			output[0]
     __A						      =			torch.Size((1, 14, 25_00_12)  )
     self.assertEqual(output_predited_logis.shape,	_lowerCamelCase  )
     # compare the actual values for a slice.
     __A						      =			torch.tensor(
         [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]  ).to(_lowerCamelCase  )

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






   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			XLMProphetNetForConditionalGeneration.from_pretrained(
         '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''  )
     model.to(_lowerCamelCase  )
     __A						      =			5_12

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

     __A						      =			(
         '''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						      =			(
         '''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						      =			(
         '''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						      =			tokenizer(
         [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE],	padding=_lowerCamelCase,	max_length=2_55,	return_tensors='''pt'''  ).input_ids
     __A						      =			input_ids.to(_lowerCamelCase  )

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

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


def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  if len(__UpperCamelCase			) == 1:
    return True
  try:
    __A						      =			series[1] / series[0]
    for index in range(len(__UpperCamelCase			) - 1			):
      if series[index + 1] / series[index] != common_ratio:
        return False
  except ZeroDivisionError:
    return False
  return True



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  __A						      =			1
  for value in series:
    answer *= value
  return pow(__UpperCamelCase		,	1 / len(__UpperCamelCase			)			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()




