"""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     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



			'''simple docstring'''



			@slow
			def       _A							(  self  ):


						'''simple docstring'''

						__lowerCamelCase								=				XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'  )
						model.to(_SCREAMING_SNAKE_CASE  )

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

						# encoder outputs
						__lowerCamelCase								=				model.prophetnet.encoder(_SCREAMING_SNAKE_CASE  )[0]
						__lowerCamelCase								=				torch.tensor(
						    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]  ).to(_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				torch.Size((1, 4, 1024)  )
						self.assertEqual(encoder_outputs.shape   ,							_SCREAMING_SNAKE_CASE  )
						self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]   ,							_SCREAMING_SNAKE_CASE   ,							atol=1E-4  )  )

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







			@slow
			def       _A							(  self  ):


						'''simple docstring'''

						__lowerCamelCase								=				XLMProphetNetForConditionalGeneration.from_pretrained(
						    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'  )
						model.to(_SCREAMING_SNAKE_CASE  )

						__lowerCamelCase								=				torch.tensor([[17, 96208, 103471, 2]]  ).to(_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				torch.tensor(
						    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]  ).to(_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				model(
						    input_ids=_SCREAMING_SNAKE_CASE   ,							attention_mask=_SCREAMING_SNAKE_CASE   ,							encoder_outputs=_SCREAMING_SNAKE_CASE   ,							decoder_input_ids=_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				output[0]
						__lowerCamelCase								=				torch.Size((1, 14, 250012)  )
						self.assertEqual(output_predited_logis.shape   ,							_SCREAMING_SNAKE_CASE  )
						# compare the actual values for a slice.
						__lowerCamelCase								=				torch.tensor(
						    [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]  ).to(_SCREAMING_SNAKE_CASE  )

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







			@slow
			def       _A							(  self  ):


						'''simple docstring'''

						__lowerCamelCase								=				XLMProphetNetForConditionalGeneration.from_pretrained(
						    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'  )
						model.to(_SCREAMING_SNAKE_CASE  )
						__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=_SCREAMING_SNAKE_CASE   ,							max_length=255   ,							return_tensors='pt'  ).input_ids
						__lowerCamelCase								=				input_ids.to(_SCREAMING_SNAKE_CASE  )

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

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







"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_SCREAMING_SNAKE_CASE  :		List[str]        =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :		Any        =      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      {
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE  :		int        =      {
    '''microsoft/mpnet-base''': 5_1_2,
}

_SCREAMING_SNAKE_CASE  :		Tuple        =      {
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Optional[int]						=						VOCAB_FILES_NAMES
   __UpperCAmelCase  :       Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase  :       Dict						=						PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase  :       Dict						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase  :       Union[str, Any]						=						MPNetTokenizer
   __UpperCAmelCase  :       List[Any]						=						["input_ids", "attention_mask"]







   def __init__(  self   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE="<s>"   ,							_SCREAMING_SNAKE_CASE="</s>"   ,							_SCREAMING_SNAKE_CASE="</s>"   ,							_SCREAMING_SNAKE_CASE="<s>"   ,							_SCREAMING_SNAKE_CASE="[UNK]"   ,							_SCREAMING_SNAKE_CASE="<pad>"   ,							_SCREAMING_SNAKE_CASE="<mask>"   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=None   ,							**_SCREAMING_SNAKE_CASE   ,							):


      '''simple docstring'''

      super().__init__(
          _SCREAMING_SNAKE_CASE   ,							tokenizer_file=_SCREAMING_SNAKE_CASE   ,							do_lower_case=_SCREAMING_SNAKE_CASE   ,							bos_token=_SCREAMING_SNAKE_CASE   ,							eos_token=_SCREAMING_SNAKE_CASE   ,							sep_token=_SCREAMING_SNAKE_CASE   ,							cls_token=_SCREAMING_SNAKE_CASE   ,							unk_token=_SCREAMING_SNAKE_CASE   ,							pad_token=_SCREAMING_SNAKE_CASE   ,							mask_token=_SCREAMING_SNAKE_CASE   ,							tokenize_chinese_chars=_SCREAMING_SNAKE_CASE   ,							strip_accents=_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE   ,							)

      __lowerCamelCase								=				json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
      if (
          pre_tok_state.get('lowercase'   ,							_SCREAMING_SNAKE_CASE  ) != do_lower_case
          or pre_tok_state.get('strip_accents'   ,							_SCREAMING_SNAKE_CASE  ) != strip_accents
      ):
         __lowerCamelCase								=				getattr(_SCREAMING_SNAKE_CASE   ,							pre_tok_state.pop('type'  )  )
         __lowerCamelCase								=				do_lower_case
         __lowerCamelCase								=				strip_accents
         __lowerCamelCase								=				pre_tok_class(**_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				do_lower_case







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      if self._mask_token is None:
         if self.verbose:
            logger.error('Using mask_token, but it is not set yet.'  )
         return None
      return str(self._mask_token  )







   @mask_token.setter
   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				AddedToken(_SCREAMING_SNAKE_CASE   ,							lstrip=_SCREAMING_SNAKE_CASE   ,							rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ) else value
      __lowerCamelCase								=				value







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=None  ):


      '''simple docstring'''

      __lowerCamelCase								=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
         return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None  ):


      '''simple docstring'''

      __lowerCamelCase								=				[self.sep_token_id]
      __lowerCamelCase								=				[self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep  ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None  ):


      '''simple docstring'''

      __lowerCamelCase								=				self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,							name=_SCREAMING_SNAKE_CASE  )
      return tuple(_SCREAMING_SNAKE_CASE  )






