


"""simple docstring"""





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 		snake_case_(							unittest.TestCase      ):






     @slow
     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						Tuple							= XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''					)
           model.to(UpperCamelCase_					)

           # encoder-decoder outputs
           lowerCAmelCase       :						Dict							= torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]					).to(UpperCamelCase_					)
           lowerCAmelCase       :						List[Any]							= torch.tensor(
               [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]					).to(UpperCamelCase_					)
           lowerCAmelCase       :						Union[str, Any]							= model(
               input_ids=UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					encoder_outputs=UpperCamelCase_       ,					decoder_input_ids=UpperCamelCase_					)
           lowerCAmelCase       :						int							= output[0]
           lowerCAmelCase       :						Optional[Any]							= torch.Size((1, 1_4, 2_5_0_0_1_2)					)
           self.assertEqual(output_predited_logis.shape       ,					UpperCamelCase_					)
           lowerCAmelCase       :						Dict							= 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(UpperCamelCase_					)
           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]       ,					UpperCamelCase_       ,					atol=1E-4					)					)

           # encoder outputs
           lowerCAmelCase       :						str							= model.prophetnet.encoder(UpperCamelCase_					)[0]
           lowerCAmelCase       :						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(UpperCamelCase_					)
           lowerCAmelCase       :						int							= torch.Size((1, 4, 1_0_2_4)					)
           self.assertEqual(encoder_outputs.shape       ,					UpperCamelCase_					)
           self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]       ,					UpperCamelCase_       ,					atol=1E-4					)					)

           # decoder outputs
           lowerCAmelCase       :						Union[str, Any]							= model.prophetnet.decoder(
               UpperCamelCase_       ,					encoder_hidden_states=UpperCamelCase_       ,					)
           lowerCAmelCase       :						Dict							= decoder_outputs[1].view(1       ,					model.config.ngram       ,					1_4       ,					-1					)
           lowerCAmelCase       :						Optional[Any]							= model.lm_head(UpperCamelCase_					)
           lowerCAmelCase       :						str							= predicting_streams_logits[:, 0]
           self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]       ,					UpperCamelCase_       ,					atol=1E-4					)					)






     @slow
     def   lowerCamelCase__  (						self	:       List[str]					):
           lowerCAmelCase       :						List[str]							= XLMProphetNetForConditionalGeneration.from_pretrained(
               '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''					)
           model.to(UpperCamelCase_					)

           lowerCAmelCase       :						List[str]							= torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]					).to(UpperCamelCase_					)
           lowerCAmelCase       :						Tuple							= torch.tensor(
               [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]					).to(UpperCamelCase_					)
           lowerCAmelCase       :						Any							= model(
               input_ids=UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					encoder_outputs=UpperCamelCase_       ,					decoder_input_ids=UpperCamelCase_					)
           lowerCAmelCase       :						str							= output[0]
           lowerCAmelCase       :						List[str]							= torch.Size((1, 1_4, 2_5_0_0_1_2)					)
           self.assertEqual(output_predited_logis.shape       ,					UpperCamelCase_					)
           # compare the actual values for a slice.
           lowerCAmelCase       :						Tuple							= 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(UpperCamelCase_					)

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






     @slow
     def   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						Dict							= XLMProphetNetForConditionalGeneration.from_pretrained(
               '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''					)
           model.to(UpperCamelCase_					)
           lowerCAmelCase       :						int							= 5_1_2

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

           lowerCAmelCase       :						Dict							= (
               '''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.'''
           )
           lowerCAmelCase       :						List[str]							= (
               '''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.'''
           )
           lowerCAmelCase       :						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。'''
           )

           lowerCAmelCase       :						Tuple							= tokenizer(
               [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]       ,					padding=UpperCamelCase_       ,					max_length=2_5_5       ,					return_tensors='''pt'''					).input_ids
           lowerCAmelCase       :						Optional[Any]							= input_ids.to(UpperCamelCase_					)

           lowerCAmelCase       :						Optional[Any]							= model.generate(
               UpperCamelCase_       ,					num_beams=1_0       ,					length_penalty=1.0       ,					no_repeat_ngram_size=3       ,					early_stopping=UpperCamelCase_					)
           lowerCAmelCase       :						List[Any]							= [tokenizer.decode(UpperCamelCase_       ,					skip_special_tokens=UpperCamelCase_					) for g in summary_ids]
           lowerCAmelCase       :						List[str]							= '''Microsoft to end Windows 7 free support after January 14, 2020'''
           lowerCAmelCase       :						Any							= '''Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'''
           lowerCAmelCase       :						Optional[Any]							= '''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]       ,					UpperCamelCase_       ,					)

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






"""simple docstring"""





import math as m


def   _snake_case    (    _snake_case					:      List[Any]			, _snake_case					:      Tuple			, _snake_case					:      Tuple=0.001      ):
      return (f(a + h      ) - f(a - h      )) / (2 * h)


def   _snake_case    (    _snake_case					:      int			, _snake_case					:      List[Any]=0			, _snake_case					:      Any=100			, _snake_case					:      Any=0.0001			, _snake_case					:      Optional[Any]=1E-6			, _snake_case					:      Any=False      ):
      lowerCAmelCase       :						Optional[Any]							= xa  # set the initial guess
      lowerCAmelCase       :						str							= [a]
      lowerCAmelCase       :						List[str]							= abs(f(_snake_case      )      )
      lowerCAmelCase       :						Dict							= lambda _snake_case      : calc_derivative(_snake_case			, _snake_case			, h=_snake_case      )  # noqa: E731  Derivative of f(x)
      for _ in range(_snake_case      ):
            if fa(_snake_case      ) == 0:
                  raise ValueError('''No converging solution found'''      )
            lowerCAmelCase       :						Tuple							= a - f(_snake_case      ) / fa(_snake_case      )  # Calculate the next estimate
            if logsteps:
                  steps.append(_snake_case      )
            if error < maxerror:
                  break
      else:
            raise ValueError('''Iteration limit reached, no converging solution found'''      )
      if logsteps:
            # If logstep is true, then log intermediate steps
            return a, error, steps
      return a, error


if __name__ == "__main__":
 from matplotlib import pyplot as plt

 snake_case__		:  Any     					=					lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
 snake_case__					,  snake_case__					,  snake_case__		:  List[str]     					=					newton_raphson(
     f, xa=10, maxiter=1_000, step=1e-6, logsteps=True
 )
 plt.plot([abs(f(x)) for x in steps])
 plt.xlabel('''step''')
 plt.ylabel('''error''')
 plt.show()
 print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")


