




"""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    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


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

										# encoder-decoder outputs
										snake_case__							:       int				      =       torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]   ).to(lowercase_   )
										snake_case__							:       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(lowercase_   )
										snake_case__							:       int				      =       model(
										    input_ids=lowercase_   ,attention_mask=lowercase_   ,encoder_outputs=lowercase_   ,decoder_input_ids=lowercase_   )
										snake_case__							:       Dict				      =       output[0]
										snake_case__							:       List[Any]				      =       torch.Size((1, 1_4, 2_5_0_0_1_2)   )
										self.assertEqual(output_predited_logis.shape   ,lowercase_   )
										snake_case__							:       Dict				      =       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
										snake_case__							:       Tuple				      =       model.prophetnet.encoder(lowercase_   )[0]
										snake_case__							:       Union[str, Any]				      =       torch.tensor(
										    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]   ).to(lowercase_   )
										snake_case__							:       Any				      =       torch.Size((1, 4, 1_0_2_4)   )
										self.assertEqual(encoder_outputs.shape   ,lowercase_   )
										self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]   ,lowercase_   ,atol=1E-4   )   )

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

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

										snake_case__							:       List[str]				      =       torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]   ).to(lowercase_   )
										snake_case__							:       Union[str, 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(lowercase_   )
										snake_case__							:       List[Any]				      =       model(
										    input_ids=lowercase_   ,attention_mask=lowercase_   ,encoder_outputs=lowercase_   ,decoder_input_ids=lowercase_   )
										snake_case__							:       Tuple				      =       output[0]
										snake_case__							:       Any				      =       torch.Size((1, 1_4, 2_5_0_0_1_2)   )
										self.assertEqual(output_predited_logis.shape   ,lowercase_   )
										# compare the actual values for a slice.
										snake_case__							:       List[str]				      =       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    _lowerCamelCase       ( self :  Optional[Any]   ):
										snake_case__							:       Any				      =       XLMProphetNetForConditionalGeneration.from_pretrained(
										    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''   )
										model.to(lowercase_   )
										snake_case__							:       Any				      =       5_1_2

										snake_case__							:       List[Any]				      =       XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''   )

										snake_case__							:       Union[str, 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__							:       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.'
										)
										snake_case__							:       Union[str, 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。'
										)

										snake_case__							:       Union[str, Any]				      =       tokenizer(
										    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]   ,padding=lowercase_   ,max_length=2_5_5   ,return_tensors='''pt'''   ).input_ids
										snake_case__							:       Optional[int]				      =       input_ids.to(lowercase_   )

										snake_case__							:       Optional[Any]				      =       model.generate(
										    lowercase_   ,num_beams=1_0   ,length_penalty=1.0   ,no_repeat_ngram_size=3   ,early_stopping=lowercase_   )
										snake_case__							:       Dict				      =       [tokenizer.decode(lowercase_   ,skip_special_tokens=lowercase_   ) for g in summary_ids]
										snake_case__							:       Optional[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__							:       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]   ,lowercase_   ,)

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





'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_ = 1_00    ,			):
 UpperCAmelCase			:      Any		   =					x_start
 UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
 UpperCAmelCase			:      str		   =					0.0

 for _ in range(UpperCAmelCase_      ):
  # Approximates small segments of curve as linear and solve
  # for trapezoidal area
  UpperCAmelCase			:      List[Any]		   =					(x_end - x_start) / steps + xa
  UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
  area += abs(fxa + fxa      ) * (xa - xa) / 2

  # Increment step
  UpperCAmelCase			:      List[str]		   =					xa
  UpperCAmelCase			:      List[Any]		   =					fxa
 return area


if __name__ == "__main__":




 def  UpperCamelCase(	UpperCAmelCase_      ):
  return x**3

 print("f(x) = x^3")
 print("The area between the curve, x = -10, x = 10 and the x axis is:")
 lowercase__            =     10
 while i <= 100000:
  lowercase__            =     trapezoidal_area(f, -5, 5, i)
  print(f'''with {i} steps: {area}''')
  i *= 10




