



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 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"						)
						model.to(_UpperCAmelCase						)

						# encoder-decoder outputs
						a   	=  torch.tensor([[17, 96208, 103471, 2]]						).to(_UpperCAmelCase						)
						a   	=  torch.tensor(
						    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]						).to(_UpperCAmelCase						)
						a   	=  model(
						    input_ids=_UpperCAmelCase						,			attention_mask=_UpperCAmelCase						,			encoder_outputs=_UpperCAmelCase						,			decoder_input_ids=_UpperCAmelCase						)
						a   	=  output[0]
						a   	=  torch.Size((1, 14, 250012)						)
						self.assertEqual(output_predited_logis.shape						,			_UpperCAmelCase						)
						a   	=  torch.tensor(
						    [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]						).to(_UpperCAmelCase						)
						self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]						,			_UpperCAmelCase						,			atol=1E-4						)						)

						# encoder outputs
						a   	=  model.prophetnet.encoder(_UpperCAmelCase						)[0]
						a   	=  torch.tensor(
						    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]						).to(_UpperCAmelCase						)
						a   	=  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
						a   	=  model.prophetnet.decoder(
						    _UpperCAmelCase						,			encoder_hidden_states=_UpperCAmelCase						,			)
						a   	=  decoder_outputs[1].view(1						,			model.config.ngram						,			14						,			-1						)
						a   	=  model.lm_head(_UpperCAmelCase						)
						a   	=  predicting_streams_logits[:, 0]
						self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]						,			_UpperCAmelCase						,			atol=1E-4						)						)




	@slow
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  XLMProphetNetForConditionalGeneration.from_pretrained(
						    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"						)
						model.to(_UpperCAmelCase						)

						a   	=  torch.tensor([[17, 96208, 103471, 2]]						).to(_UpperCAmelCase						)
						a   	=  torch.tensor(
						    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]						).to(_UpperCAmelCase						)
						a   	=  model(
						    input_ids=_UpperCAmelCase						,			attention_mask=_UpperCAmelCase						,			encoder_outputs=_UpperCAmelCase						,			decoder_input_ids=_UpperCAmelCase						)
						a   	=  output[0]
						a   	=  torch.Size((1, 14, 250012)						)
						self.assertEqual(output_predited_logis.shape						,			_UpperCAmelCase						)
						# 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(_UpperCAmelCase						)

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




	@slow
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  XLMProphetNetForConditionalGeneration.from_pretrained(
						    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"						)
						model.to(_UpperCAmelCase						)
						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=_UpperCAmelCase						,			max_length=255						,			return_tensors="pt"						).input_ids
						a   	=  input_ids.to(_UpperCAmelCase						)

						a   	=  model.generate(
						    _UpperCAmelCase						,			num_beams=10						,			length_penalty=1.0						,			no_repeat_ngram_size=3						,			early_stopping=_UpperCAmelCase						)
						a   	=  [tokenizer.decode(_UpperCAmelCase						,			skip_special_tokens=_UpperCAmelCase						) 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]						,			_UpperCAmelCase						,			)

						a   	=  model.generate(
						    _UpperCAmelCase						,			num_beams=1						,			length_penalty=1.0						,			no_repeat_ngram_size=3						,			early_stopping=_UpperCAmelCase						)
						a   	=  [
						    tokenizer.convert_ids_to_tokens(_UpperCAmelCase						,			skip_special_tokens=_UpperCAmelCase						) 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]						,			_UpperCAmelCase						,			)



class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Any		=     data
   __UpperCAmelCase	:  BinaryTreeNode | None		=     None
   __UpperCAmelCase	:  BinaryTreeNode | None		=     None





def  _snake_case		(snake_case_					:     BinaryTreeNode | None    ,       snake_case_					:     int  )							->		BinaryTreeNode | None:
  if node is None:
    __UpperCAmelCase	:  List[Any]		=     BinaryTreeNode(snake_case_  )
    return node

  # binary search tree is not empty,
  # so we will insert it into the tree
  # if new_value is less than value of data in node,
  #  add it to left subtree and proceed recursively
  if new_value < node.data:
    __UpperCAmelCase	:  Union[str, Any]		=     insert(node.left_child    ,       snake_case_  )
  else:
    # if new_value is greater than value of data in node,
    #  add it to right subtree and proceed recursively
    __UpperCAmelCase	:  Tuple		=     insert(node.right_child    ,       snake_case_  )
  return node





def  _snake_case		(snake_case_					:     None | BinaryTreeNode  )							->		list[int]:  # if node is None,return
  if node:
    __UpperCAmelCase	:  str		=     inorder(node.left_child  )
    __UpperCAmelCase	:  Optional[int]		=     [*inorder_array, node.data]
    __UpperCAmelCase	:  str		=     inorder_array + inorder(node.right_child  )
  else:
    __UpperCAmelCase	:  Union[str, Any]		=     []
  return inorder_array





def  _snake_case		()							->		BinaryTreeNode | None:
  __UpperCAmelCase	:  Tuple		=     insert(snake_case_    ,       15  )
  insert(snake_case_    ,       10  )
  insert(snake_case_    ,       25  )
  insert(snake_case_    ,       6  )
  insert(snake_case_    ,       14  )
  insert(snake_case_    ,       20  )
  insert(snake_case_    ,       60  )
  return root





def  _snake_case		()							->		None:
  # main function
  __UpperCAmelCase	:  Any		=     make_tree()
  print('Printing values of binary search tree in Inorder Traversal.'  )
  inorder(snake_case_  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()





