

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							):
   __UpperCAmelCase	:  List[Any]		=     XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'							)
   model.to(_UpperCAmelCase							)

   # encoder-decoder outputs
   __UpperCAmelCase	:  Optional[Any]		=     torch.tensor([[17, 96_208, 103_471, 2]]							).to(_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[int]		=     torch.tensor(
       [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]							).to(_UpperCAmelCase							)
   __UpperCAmelCase	:  str		=     model(
       input_ids=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  encoder_outputs=_UpperCAmelCase  ,  decoder_input_ids=_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[int]		=     output[0]
   __UpperCAmelCase	:  Any		=     torch.Size((1, 14, 250_012)							)
   self.assertEqual(output_predited_logis.shape  ,  _UpperCAmelCase							)
   __UpperCAmelCase	:  Any		=     torch.tensor(
       [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]							).to(_UpperCAmelCase							)
   self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,  _UpperCAmelCase  ,  atol=1E-4							)							)

   # encoder outputs
   __UpperCAmelCase	:  List[str]		=     model.prophetnet.encoder(_UpperCAmelCase							)[0]
   __UpperCAmelCase	:  Optional[Any]		=     torch.tensor(
       [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]							).to(_UpperCAmelCase							)
   __UpperCAmelCase	:  List[Any]		=     torch.Size((1, 4, 1_024)							)
   self.assertEqual(encoder_outputs.shape  ,  _UpperCAmelCase							)
   self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]  ,  _UpperCAmelCase  ,  atol=1E-4							)							)

   # decoder outputs
   __UpperCAmelCase	:  Union[str, Any]		=     model.prophetnet.decoder(
       _UpperCAmelCase  ,  encoder_hidden_states=_UpperCAmelCase  ,  )
   __UpperCAmelCase	:  Optional[Any]		=     decoder_outputs[1].view(1  ,  model.config.ngram  ,  14  ,  -1							)
   __UpperCAmelCase	:  str		=     model.lm_head(_UpperCAmelCase							)
   __UpperCAmelCase	:  Any		=     predicting_streams_logits[:, 0]
   self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]  ,  _UpperCAmelCase  ,  atol=1E-4							)							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  str		=     XLMProphetNetForConditionalGeneration.from_pretrained(
       'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'							)
   model.to(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     torch.tensor([[17, 96_208, 103_471, 2]]							).to(_UpperCAmelCase							)
   __UpperCAmelCase	:  Tuple		=     torch.tensor(
       [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]							).to(_UpperCAmelCase							)
   __UpperCAmelCase	:  Any		=     model(
       input_ids=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  encoder_outputs=_UpperCAmelCase  ,  decoder_input_ids=_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[int]		=     output[0]
   __UpperCAmelCase	:  Dict		=     torch.Size((1, 14, 250_012)							)
   self.assertEqual(output_predited_logis.shape  ,  _UpperCAmelCase							)
   # compare the actual values for a slice.
   __UpperCAmelCase	:  Tuple		=     torch.tensor(
       [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]							).to(_UpperCAmelCase							)

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




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     XLMProphetNetForConditionalGeneration.from_pretrained(
       'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'							)
   model.to(_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     512

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

   __UpperCAmelCase	:  List[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.'
   )
   __UpperCAmelCase	:  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.'
   )
   __UpperCAmelCase	:  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。'
   )

   __UpperCAmelCase	:  int		=     tokenizer(
       [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]  ,  padding=_UpperCAmelCase  ,  max_length=255  ,  return_tensors='pt'							).input_ids
   __UpperCAmelCase	:  Union[str, Any]		=     input_ids.to(_UpperCAmelCase							)

   __UpperCAmelCase	:  Dict		=     model.generate(
       _UpperCAmelCase  ,  num_beams=10  ,  length_penalty=1.0  ,  no_repeat_ngram_size=3  ,  early_stopping=_UpperCAmelCase							)
   __UpperCAmelCase	:  List[str]		=     [tokenizer.decode(_UpperCAmelCase  ,  skip_special_tokens=_UpperCAmelCase							) for g in summary_ids]
   __UpperCAmelCase	:  List[str]		=     'Microsoft to end Windows 7 free support after January 14, 2020'
   __UpperCAmelCase	:  Dict		=     'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
   __UpperCAmelCase	:  List[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  ,  )

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


from __future__ import annotations

snake_case__	:     str      =    [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = -1
       UpperCAmelCase_				:  int  = -1
       UpperCAmelCase_				:  list[int]  = [-1] * length
       UpperCAmelCase_				:  Node | None  = None
       UpperCAmelCase_				:  Node | None  = None






  def __repr__(					self		):




       '''simple docstring'''

       return F'''Node(min_value={self.minn} max_value={self.maxx})'''







def    _lowerCamelCase       ( lowerCamelCase_   :				list[int]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[str]  = Node(len(lowerCamelCase_  )  )
     UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = min(lowerCamelCase_  ), max(lowerCamelCase_  )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     UpperCAmelCase_				:  str  = (root.minn + root.maxx) // 2

     UpperCAmelCase_				:  list[int]  = []
     UpperCAmelCase_				:  list[int]  = []

     for index, num in enumerate(lowerCamelCase_  ):
          if num <= pivot:
               left_arr.append(lowerCamelCase_  )
          else:
               right_arr.append(lowerCamelCase_  )
          UpperCAmelCase_				:  Tuple  = len(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = build_tree(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = build_tree(lowerCamelCase_  )
     return root


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if index < 0 or node is None:
          return 0
     # Leaf node cases
     if node.minn == node.maxx:
          return index + 1 if node.minn == num else 0
     UpperCAmelCase_				:  Any  = (node.minn + node.maxx) // 2
     if num <= pivot:
          # go the left subtree and map index to the left subtree
          return rank_till_index(node.left      ,	lowerCamelCase_      ,	node.map_left[index] - 1  )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right      ,	lowerCamelCase_      ,	index - node.map_left[index]  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if start > end:
          return 0
     UpperCAmelCase_				:  int  = rank_till_index(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )
     UpperCAmelCase_				:  List[Any]  = rank_till_index(lowerCamelCase_      ,	lowerCamelCase_      ,	start - 1  )
     return rank_till_end - rank_before_start


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if index > (end - start) or start > end or node is None:
          return -1
     # Leaf node case
     if node.minn == node.maxx:
          return node.minn
     # Number of elements in the left subtree in interval [start, end]
     UpperCAmelCase_				:  Tuple  = node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left      ,	lowerCamelCase_      ,	(node.map_left[start - 1] if start else 0)      ,	node.map_left[end] - 1      ,	)
     else:
          return quantile(
              node.right      ,	index - num_elements_in_left_tree      ,	start - (node.map_left[start - 1] if start else 0)      ,	end - node.map_left[end]      ,	)


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if (
         start > end
         or node is None
         or start_num > end_num
         or node.minn > end_num
         or node.maxx < start_num
     ):
          return 0
     if start_num <= node.minn and node.maxx <= end_num:
          return end - start + 1
     UpperCAmelCase_				:  Any  = range_counting(
         node.left      ,	(node.map_left[start - 1] if start else 0)      ,	node.map_left[end] - 1      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	)
     UpperCAmelCase_				:  List[str]  = range_counting(
         node.right      ,	start - (node.map_left[start - 1] if start else 0)      ,	end - node.map_left[end]      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	)
     return left + right


if __name__ == "__main__":
 import doctest

 doctest.testmod()






