


'''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      ):





  """simple docstring"""




  @slow
  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"					)
   model.to(UpperCamelCase					)

   # encoder-decoder outputs
   lowerCamelCase_								=  torch.tensor([[17, 9_6208, 10_3471, 2]]					).to(UpperCamelCase					)
   lowerCamelCase_								=  torch.tensor(
       [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]					).to(UpperCamelCase					)
   lowerCamelCase_								=  model(
       input_ids=UpperCamelCase      ,  attention_mask=UpperCamelCase      ,  encoder_outputs=UpperCamelCase      ,  decoder_input_ids=UpperCamelCase					)
   lowerCamelCase_								=  output[0]
   lowerCamelCase_								=  torch.Size((1, 14, 25_0012)					)
   self.assertEqual(output_predited_logis.shape      ,  UpperCamelCase					)
   lowerCamelCase_								=  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_								=  model.prophetnet.encoder(UpperCamelCase					)[0]
   lowerCamelCase_								=  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_								=  torch.Size((1, 4, 1024)					)
   self.assertEqual(encoder_outputs.shape      ,  UpperCamelCase					)
   self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]      ,  UpperCamelCase      ,  atol=1e-4					)					)

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






  @slow
  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  XLMProphetNetForConditionalGeneration.from_pretrained(
       "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"					)
   model.to(UpperCamelCase					)

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







   """simple docstring"""
   lowerCamelCase_								=  XLMProphetNetForConditionalGeneration.from_pretrained(
       "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"					)
   model.to(UpperCamelCase					)
   lowerCamelCase_								=  512

   lowerCamelCase_								=  XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"					)

   lowerCamelCase_								=  (
       "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_								=  (
       "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_								=  (
       "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_								=  tokenizer(
       [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]      ,  padding=UpperCamelCase      ,  max_length=255      ,  return_tensors="pt"					).input_ids
   lowerCamelCase_								=  input_ids.to(UpperCamelCase					)

   lowerCamelCase_								=  model.generate(
       UpperCamelCase      ,  num_beams=10      ,  length_penalty=1.0      ,  no_repeat_ngram_size=3      ,  early_stopping=UpperCamelCase					)
   lowerCamelCase_								=  [tokenizer.decode(UpperCamelCase      ,  skip_special_tokens=UpperCamelCase					) for g in summary_ids]
   lowerCamelCase_								=  "Microsoft to end Windows 7 free support after January 14, 2020"
   lowerCamelCase_								=  "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
   lowerCamelCase_								=  "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_								=  model.generate(
       UpperCamelCase      ,  num_beams=1      ,  length_penalty=1.0      ,  no_repeat_ngram_size=3      ,  early_stopping=UpperCamelCase					)
   lowerCamelCase_								=  [
       tokenizer.convert_ids_to_tokens(UpperCamelCase      ,  skip_special_tokens=UpperCamelCase					) for g in summary_ids_beama
   ]
   lowerCamelCase_								=  "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "					)
   lowerCamelCase_								=  "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
       " "					)
   lowerCamelCase_								=  "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 numpy as np





class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase=5      ,  UpperCamelCase=5					):







   """simple docstring"""
   lowerCamelCase_								=  depth
   lowerCamelCase_								=  0
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   lowerCamelCase_								=  min_leaf_size
   lowerCamelCase_								=  None






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if labels.ndim != 1:
    print("Error: Input labels must be one dimensional"					)

   return np.mean((labels - prediction) ** 2					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if x.ndim != 1:
    print("Error: Input data set must be one dimensional"					)
    return
   if len(UpperCamelCase					) != len(UpperCamelCase					):
    print("Error: X and y have different lengths"					)
    return
   if y.ndim != 1:
    print("Error: Data set labels must be one dimensional"					)
    return

   if len(UpperCamelCase					) < 2 * self.min_leaf_size:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   if self.depth == 1:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   lowerCamelCase_								=  0
   lowerCamelCase_								=  self.mean_squared_error(UpperCamelCase      ,  np.mean(UpperCamelCase					)					) * 2
   for i in range(len(UpperCamelCase					)					):
    if len(x[:i]					) < self.min_leaf_size:
     continue
    elif len(x[i:]					) < self.min_leaf_size:
     continue
    else:
     lowerCamelCase_								=  self.mean_squared_error(x[:i]      ,  np.mean(y[:i]					)					)
     lowerCamelCase_								=  self.mean_squared_error(x[i:]      ,  np.mean(y[i:]					)					)
     lowerCamelCase_								=  error_left + error_right
     if error < min_error:
      lowerCamelCase_								=  i
      lowerCamelCase_								=  error

   if best_split != 0:
    lowerCamelCase_								=  x[:best_split]
    lowerCamelCase_								=  y[:best_split]
    lowerCamelCase_								=  x[best_split:]
    lowerCamelCase_								=  y[best_split:]

    lowerCamelCase_								=  x[best_split]
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    self.left.train(UpperCamelCase      ,  UpperCamelCase					)
    self.right.train(UpperCamelCase      ,  UpperCamelCase					)
   else:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)

   return







  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   if self.prediction is not None:
    return self.prediction
   elif self.left or self.right is not None:
    if x >= self.decision_boundary:
     return self.right.predict(UpperCamelCase					)
    else:
     return self.left.predict(UpperCamelCase					)
   else:
    print("Error: Decision tree not yet trained"					)
    return None





class 					snake_case   :





  """simple docstring"""




  @staticmethod
  def   snake_case		(   UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  float(0					)
   for label in labels:
    squared_error_sum += (label - prediction) ** 2

   return float(squared_error_sum / labels.size					)





def __snake_case (				):
 lowerCamelCase_								=  np.arange(-1.0 ,    1.0 ,    0.005		)
 lowerCamelCase_								=  np.sin(UpperCAmelCase_		)

 lowerCamelCase_								=  DecisionTree(depth=10 ,    min_leaf_size=10		)
 tree.train(UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  (np.random.rand(10		) * 2) - 1
 lowerCamelCase_								=  np.array([tree.predict(UpperCAmelCase_		) for x in test_cases]		)
 lowerCamelCase_								=  np.mean((predictions - test_cases) ** 2		)

 print("Test values: " + str(UpperCAmelCase_		)		)
 print("Predictions: " + str(UpperCAmelCase_		)		)
 print("Average error: " + str(UpperCAmelCase_		)		)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name="""mean_squarred_error""", verbose=True)



