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       a__	(       self							):
            _A=						XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'							)
            model.to(lowerCAmelCase__							)

            # encoder-decoder outputs
            _A=						torch.tensor([[17, 96208, 103471, 2]]							).to(lowerCAmelCase__							)
            _A=						torch.tensor(
                [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]							).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, 250012)							)
            self.assertEqual(output_predited_logis.shape					,   lowerCAmelCase__							)
            _A=						torch.tensor(
                [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]							).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(
                [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]							).to(lowerCAmelCase__							)
            _A=						torch.Size((1, 4, 1024)							)
            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       a__	(       self							):
            _A=						XLMProphetNetForConditionalGeneration.from_pretrained(
                'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'							)
            model.to(lowerCAmelCase__							)

            _A=						torch.tensor([[17, 96208, 103471, 2]]							).to(lowerCAmelCase__							)
            _A=						torch.tensor(
                [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]							).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, 250012)							)
            self.assertEqual(output_predited_logis.shape					,   lowerCAmelCase__							)
            # 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(lowerCAmelCase__							)

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





     @slow
     def       a__	(       self							):
            _A=						XLMProphetNetForConditionalGeneration.from_pretrained(
                'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'							)
            model.to(lowerCAmelCase__							)
            _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=lowerCAmelCase__					,   max_length=255					,   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__					,   )
from __future__ import annotations

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						{1}
       _A=						[1]
       for _ in range(2							,       depth + 1 ):
              _A=						[*previous_coefficients, 0]
              _A=						[0, *previous_coefficients]
              _A=						[]
              for x, y in zip(lowerCAmelCase_							,       lowerCAmelCase_ ):
                     coefficients.add(x + y )
                     previous_coefficients.append(x + y )
       return coefficients

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						set()
       for number in unique_coefficients:
              _A=						2
              _A=						number
              while divisor**2 <= copy_number:
                     _A=						0
                     while copy_number % divisor == 0:
                            copy_number //= divisor
                            multiplicity += 1
                     if multiplicity >= 2:
                            non_squarefrees.add(lowerCAmelCase_ )
                            break
                     divisor += 1

       return unique_coefficients.difference(lowerCAmelCase_ )

def 				UpperCamelCase					(     lowerCAmelCase_ = 51 )		->    int:




       '''simple docstring'''






       _A=						get_pascal_triangle_unique_coefficients(lowerCAmelCase_ )
       _A=						get_squarefrees(lowerCAmelCase_ )
       return sum(lowerCAmelCase_ )


if __name__ == "__main__":
      print(F"{solution() = }")