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    lowercase_    (     unittest.TestCase		):



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[int]				=    XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"				)
													model.to(lowercase_				)

													# encoder-decoder outputs
													_snake_case						:     List[str]				=    torch.tensor([[17, 96_208, 103_471, 2]]				).to(lowercase_				)
													_snake_case						:     List[Any]				=    torch.tensor(
													    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]				).to(lowercase_				)
													_snake_case						:     int				=    model(
													    input_ids=lowercase_      ,				attention_mask=lowercase_      ,				encoder_outputs=lowercase_      ,				decoder_input_ids=lowercase_				)
													_snake_case						:     int				=    output[0]
													_snake_case						:     List[str]				=    torch.Size((1, 14, 250_012)				)
													self.assertEqual(output_predited_logis.shape      ,				lowercase_				)
													_snake_case						:     Any				=    torch.tensor(
													    [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]				).to(lowercase_				)
													self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]      ,				lowercase_      ,				atol=1e-4				)				)

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



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    XLMProphetNetForConditionalGeneration.from_pretrained(
													    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"				)
													model.to(lowercase_				)

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

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



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     Tuple				=    XLMProphetNetForConditionalGeneration.from_pretrained(
													    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"				)
													model.to(lowercase_				)
													_snake_case						:     Any				=    512

													_snake_case						:     int				=    XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"				)

													_snake_case						:     Any				=    (
													    "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						:     Optional[int]				=    (
													    "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						:     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。"
													)

													_snake_case						:     Union[str, Any]				=    tokenizer(
													    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]      ,				padding=lowercase_      ,				max_length=255      ,				return_tensors="pt"				).input_ids
													_snake_case						:     int				=    input_ids.to(lowercase_				)

													_snake_case						:     Union[str, Any]				=    model.generate(
													    lowercase_      ,				num_beams=10      ,				length_penalty=1.0      ,				no_repeat_ngram_size=3      ,				early_stopping=lowercase_				)
													_snake_case						:     str				=    [tokenizer.decode(lowercase_      ,				skip_special_tokens=lowercase_				) for g in summary_ids]
													_snake_case						:     Union[str, Any]				=    "Microsoft to end Windows 7 free support after January 14, 2020"
													_snake_case						:     List[str]				=    "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
													_snake_case						:     str				=    "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_      ,				)

													_snake_case						:     Dict				=    model.generate(
													    lowercase_      ,				num_beams=1      ,				length_penalty=1.0      ,				no_repeat_ngram_size=3      ,				early_stopping=lowercase_				)
													_snake_case						:     List[str]				=    [
													    tokenizer.convert_ids_to_tokens(lowercase_      ,				skip_special_tokens=lowercase_				) for g in summary_ids_beama
													]
													_snake_case						:     List[str]				=    "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "				)
													_snake_case						:     Union[str, Any]				=    "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
													    " "				)
													_snake_case						:     Dict				=    "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_      ,				)





import doctest

import projectq
from projectq.ops import H, Measure

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      quantum_engine.allocate_qubit()
     H | qubit
     Measure | qubit
     return int(__UpperCamelCase)


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

   # initialises a new quantum backend
   lowercase__      :			Optional[int]           =					projectq.MainEngine()

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

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

   print("Random numbers", random_numbers_list)

