



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    ):







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

											# encoder-decoder outputs
											A__						:				Union[str, Any]         =			torch.tensor([[17, 9_6208, 10_3471, 2]]).to(lowercase_)
											A__						:				Any         =			torch.tensor(
											    [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]).to(lowercase_)
											A__						:				Dict         =			model(
											    input_ids=lowercase_      ,		attention_mask=lowercase_      ,		encoder_outputs=lowercase_      ,		decoder_input_ids=lowercase_)
											A__						:				Tuple         =			output[0]
											A__						:				Dict         =			torch.Size((1, 14, 25_0012))
											self.assertEqual(output_predited_logis.shape      ,		lowercase_)
											A__						:				Union[str, Any]         =			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(lowercase_)
											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]      ,		lowercase_      ,		atol=1e-4))

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







					@slow
					def 			_lowercase      (							self   :  Union[str, Any]):
											A__						:				int         =			XLMProphetNetForConditionalGeneration.from_pretrained(
											    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
											model.to(lowercase_)

											A__						:				str         =			torch.tensor([[17, 9_6208, 10_3471, 2]]).to(lowercase_)
											A__						:				List[Any]         =			torch.tensor(
											    [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]).to(lowercase_)
											A__						:				Optional[Any]         =			model(
											    input_ids=lowercase_      ,		attention_mask=lowercase_      ,		encoder_outputs=lowercase_      ,		decoder_input_ids=lowercase_)
											A__						:				List[Any]         =			output[0]
											A__						:				List[str]         =			torch.Size((1, 14, 25_0012))
											self.assertEqual(output_predited_logis.shape      ,		lowercase_)
											# compare the actual values for a slice.
											A__						:				Tuple         =			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(lowercase_)

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







					@slow
					def 			_lowercase      (							self   :  Tuple):
											A__						:				Union[str, Any]         =			XLMProphetNetForConditionalGeneration.from_pretrained(
											    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
											model.to(lowercase_)
											A__						:				Dict         =			512

											A__						:				List[str]         =			XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")

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

											A__						:				Optional[int]         =			model.generate(
											    lowercase_      ,		num_beams=10      ,		length_penalty=1.0      ,		no_repeat_ngram_size=3      ,		early_stopping=lowercase_)
											A__						:				Optional[Any]         =			[tokenizer.decode(lowercase_      ,		skip_special_tokens=lowercase_) for g in summary_ids]
											A__						:				Optional[Any]         =			"Microsoft to end Windows 7 free support after January 14, 2020"
											A__						:				Union[str, Any]         =			"Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
											A__						:				int         =			"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__						:				Union[str, Any]         =			model.generate(
											    lowercase_      ,		num_beams=1      ,		length_penalty=1.0      ,		no_repeat_ngram_size=3      ,		early_stopping=lowercase_)
											A__						:				List[str]         =			[
											    tokenizer.convert_ids_to_tokens(lowercase_      ,		skip_special_tokens=lowercase_) for g in summary_ids_beama
											]
											A__						:				Optional[int]         =			"▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" ")
											A__						:				List[Any]         =			"▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
											    " ")
											A__						:				Optional[int]         =			"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_      ,		)


'''simple docstring'''

from __future__ import annotations







class lowerCamelCase   :

				def __init__(       self,						lowercase_ )    ->							None:
					snake_case							=					value
					snake_case							=					None
					snake_case							=					None







class lowerCamelCase   :
				snake_case_							     = 42

				def __init__(       self )    ->							None:
					snake_case							=					0

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							None:
					if node is None:
						return

					if path_sum == self.target:
						self.paths += 1

					if node.left:
						self.depth_first_search(node.left,						path_sum + node.left.value )
					if node.right:
						self.depth_first_search(node.right,						path_sum + node.right.value )






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = None )    ->							int:
					if node is None:
						return 0
					if target is not None:
						snake_case							=					target

					self.depth_first_search(lowercase_,						node.value )
					self.path_sum(node.left )
					self.path_sum(node.right )

					return self.paths


if __name__ == "__main__":
						import doctest

						doctest.testmod()





