

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     ):
		"""simple docstring"""







		@slow
		def        UpperCamelCase						(      self    )					->  Optional[int]:
						'''simple docstring'''






						A__            =     XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"    )
						model.to(lowercase    )

						# encoder-decoder outputs
						A__            =     torch.tensor([[17, 96208, 103471, 2]]    ).to(lowercase    )
						A__            =     torch.tensor(
						    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]    ).to(lowercase    )
						A__            =     model(
						    input_ids=lowercase , attention_mask=lowercase , encoder_outputs=lowercase , decoder_input_ids=lowercase    )
						A__            =     output[0]
						A__            =     torch.Size((1, 14, 250012)    )
						self.assertEqual(output_predited_logis.shape , lowercase    )
						A__            =     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__            =     model.prophetnet.encoder(lowercase    )[0]
						A__            =     torch.tensor(
						    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]    ).to(lowercase    )
						A__            =     torch.Size((1, 4, 1024)    )
						self.assertEqual(encoder_outputs.shape , lowercase    )
						self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3] , lowercase , atol=1e-4    )    )

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


		@slow
		def        UpperCamelCase						(      self    )					->  Optional[Any]:
						'''simple docstring'''






						A__            =     XLMProphetNetForConditionalGeneration.from_pretrained(
						    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
						model.to(lowercase    )

						A__            =     torch.tensor([[17, 96208, 103471, 2]]    ).to(lowercase    )
						A__            =     torch.tensor(
						    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]    ).to(lowercase    )
						A__            =     model(
						    input_ids=lowercase , attention_mask=lowercase , encoder_outputs=lowercase , decoder_input_ids=lowercase    )
						A__            =     output[0]
						A__            =     torch.Size((1, 14, 250012)    )
						self.assertEqual(output_predited_logis.shape , lowercase    )
						# compare the actual values for a slice.
						A__            =     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        UpperCamelCase						(      self    )					->  Union[str, Any]:
						'''simple docstring'''






						A__            =     XLMProphetNetForConditionalGeneration.from_pretrained(
						    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
						model.to(lowercase    )
						A__            =     512

						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=lowercase , max_length=255 , return_tensors="pt"    ).input_ids
						A__            =     input_ids.to(lowercase    )

						A__            =     model.generate(
						    lowercase , num_beams=10 , length_penalty=1.0 , no_repeat_ngram_size=3 , early_stopping=lowercase    )
						A__            =     [tokenizer.decode(lowercase , skip_special_tokens=lowercase    ) 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] , lowercase , )

						A__            =     model.generate(
						    lowercase , num_beams=1 , length_penalty=1.0 , no_repeat_ngram_size=3 , early_stopping=lowercase    )
						A__            =     [
						    tokenizer.convert_ids_to_tokens(lowercase , skip_special_tokens=lowercase    ) 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] , lowercase , )




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    (DDIMScheduler,)
			__lowerCamelCase	    =    (('eta', 0.0), ('num_inference_steps', 50))


			def        UpperCamelCase						(      self , **lowercase    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "clip_sample": True,
							}

							config.update(**lowercase    )
							return config


			def        UpperCamelCase						(      self , **lowercase    )					->  List[Any]:
							'''simple docstring'''






							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config(**lowercase    )
							A__            =     scheduler_class(**lowercase    )

							A__      ,			A__            =     10, 0.0

							A__            =     self.dummy_model()
							A__            =     self.dummy_sample_deter

							scheduler.set_timesteps(lowercase    )

							for t in scheduler.timesteps:
											A__            =     model(lowercase , lowercase    )
											A__            =     scheduler.step(lowercase , lowercase , lowercase , lowercase    ).prev_sample

							return sample


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							for timesteps in [100, 500, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase    )


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=lowercase    )

							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config(steps_offset=1    )
							A__            =     scheduler_class(**lowercase    )
							scheduler.set_timesteps(5    )
							assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1]    )    )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=lowercase , beta_end=lowercase    )


			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowercase    )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowercase    )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=lowercase    )


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							self.check_over_configs(thresholding=lowercase    )
							for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(
															    thresholding=lowercase , prediction_type=lowercase , sample_max_value=lowercase , )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for t in [1, 10, 49]:
											self.check_over_forward(time_step=lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500]    ):
											self.check_over_forward(time_step=lowercase , num_inference_steps=lowercase    )


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0]    ):
											self.check_over_forward(time_step=lowercase , eta=lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config()
							A__            =     scheduler_class(**lowercase    )

							assert torch.sum(torch.abs(scheduler._get_variance(0 , 0    ) - 0.0    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420 , 400    ) - 0.1_4771    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980 , 960    ) - 0.3_2460    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0 , 0    ) - 0.0    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487 , 486    ) - 0.0_0979    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999 , 998    ) - 0.02    )    ) < 1e-5


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     self.full_loop()

							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 172.0067    ) < 1e-2
							assert abs(result_mean.item() - 0.22_3967    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							A__            =     self.full_loop(prediction_type="v_prediction"    )

							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 52.5302    ) < 1e-2
							assert abs(result_mean.item() - 0.0684    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							A__            =     self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01    )
							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 149.8295    ) < 1e-2
							assert abs(result_mean.item() - 0.1951    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01    )
							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 149.0784    ) < 1e-2
							assert abs(result_mean.item() - 0.1941    ) < 1e-3

