


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 lowerCAmelCase_						( unittest.TestCase       ):

 '''simple docstring'''

 @slow
 def 						UpperCamelCase__     (	self					):
       snake_case_       =      XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''					)
       model.to(__SCREAMING_SNAKE_CASE					)

       # encoder-decoder outputs
       snake_case_       =      torch.tensor([[17, 9_62_08, 10_34_71, 2]]					).to(__SCREAMING_SNAKE_CASE					)
       snake_case_       =      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(__SCREAMING_SNAKE_CASE					)
       snake_case_       =      model(
           input_ids=__SCREAMING_SNAKE_CASE		,      attention_mask=__SCREAMING_SNAKE_CASE		,      encoder_outputs=__SCREAMING_SNAKE_CASE		,      decoder_input_ids=__SCREAMING_SNAKE_CASE					)
       snake_case_       =      output[0]
       snake_case_       =      torch.Size((1, 14, 25_00_12)					)
       self.assertEqual(output_predited_logis.shape		,      __SCREAMING_SNAKE_CASE					)
       snake_case_       =      torch.tensor(
           [[[-6.3_986, -8.2_391, 12.51_89], [-6.3_289, -8.0_864, 12.62_11], [-6.2_418, -8.0_445, 12.79_68]]]					).to(__SCREAMING_SNAKE_CASE					)
       self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]		,      __SCREAMING_SNAKE_CASE		,      atol=1E-4					)					)

       # encoder outputs
       snake_case_       =      model.prophetnet.encoder(__SCREAMING_SNAKE_CASE					)[0]
       snake_case_       =      torch.tensor(
           [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]					).to(__SCREAMING_SNAKE_CASE					)
       snake_case_       =      torch.Size((1, 4, 10_24)					)
       self.assertEqual(encoder_outputs.shape		,      __SCREAMING_SNAKE_CASE					)
       self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]		,      __SCREAMING_SNAKE_CASE		,      atol=1E-4					)					)

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



 @slow
 def 						UpperCamelCase__     (	self					):
       snake_case_       =      XLMProphetNetForConditionalGeneration.from_pretrained(
           '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''					)
       model.to(__SCREAMING_SNAKE_CASE					)

       snake_case_       =      torch.tensor([[17, 9_62_08, 10_34_71, 2]]					).to(__SCREAMING_SNAKE_CASE					)
       snake_case_       =      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(__SCREAMING_SNAKE_CASE					)
       snake_case_       =      model(
           input_ids=__SCREAMING_SNAKE_CASE		,      attention_mask=__SCREAMING_SNAKE_CASE		,      encoder_outputs=__SCREAMING_SNAKE_CASE		,      decoder_input_ids=__SCREAMING_SNAKE_CASE					)
       snake_case_       =      output[0]
       snake_case_       =      torch.Size((1, 14, 25_00_12)					)
       self.assertEqual(output_predited_logis.shape		,      __SCREAMING_SNAKE_CASE					)
       # compare the actual values for a slice.
       snake_case_       =      torch.tensor(
           [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]					).to(__SCREAMING_SNAKE_CASE					)

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





 @slow
 def 						UpperCamelCase__     (	self					):
       snake_case_       =      XLMProphetNetForConditionalGeneration.from_pretrained(
           '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''					)
       model.to(__SCREAMING_SNAKE_CASE					)
       snake_case_       =      5_12

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

       snake_case_       =      (
           '''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.'''
       )
       snake_case_       =      (
           '''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.'''
       )
       snake_case_       =      (
           '''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。'''
       )

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

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

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


"""simple docstring"""




import doctest

import projectq
from projectq.ops import H, Measure
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					quantum_engine.allocate_qubit()
					H | qubit
					Measure | qubit
					return int(lowercase__      )


if __name__ == "__main__":
	doctest.testmod()

	# initialises a new quantum backend
	__A     :    Dict    =		projectq.MainEngine()

	# Generate a list of 10 random numbers
	__A     :    Any    =		[get_random_number(quantum_engine) for _ in range(10)]

	# Flushes the quantum engine from memory
	quantum_engine.flush()

	print('Random numbers', random_numbers_list)





