

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							):

  @slow
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
      model.to(_lowerCamelCase)

      # encoder-decoder outputs
      SCREAMING_SNAKE_CASE_						=    torch.tensor([[17, 96208, 103471, 2]]).to(_lowerCamelCase)
      SCREAMING_SNAKE_CASE_						=    torch.tensor(
          [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]).to(_lowerCamelCase)
      SCREAMING_SNAKE_CASE_						=    model(
          input_ids=_lowerCamelCase    ,		attention_mask=_lowerCamelCase    ,		encoder_outputs=_lowerCamelCase    ,		decoder_input_ids=_lowerCamelCase)
      SCREAMING_SNAKE_CASE_						=    output[0]
      SCREAMING_SNAKE_CASE_						=    torch.Size((1, 14, 250012))
      self.assertEqual(output_predited_logis.shape    ,		_lowerCamelCase)
      SCREAMING_SNAKE_CASE_						=    torch.tensor(
          [[[-6.3_9_8_6, -8.2_3_9_1, 12.5189], [-6.3_2_8_9, -8.0_8_6_4, 12.6211], [-6.2_4_1_8, -8.0_4_4_5, 12.7968]]]).to(_lowerCamelCase)
      self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,		_lowerCamelCase    ,		atol=1E-4))

      # encoder outputs
      SCREAMING_SNAKE_CASE_						=    model.prophetnet.encoder(_lowerCamelCase)[0]
      SCREAMING_SNAKE_CASE_						=    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(_lowerCamelCase)
      SCREAMING_SNAKE_CASE_						=    torch.Size((1, 4, 1024))
      self.assertEqual(encoder_outputs.shape    ,		_lowerCamelCase)
      self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]    ,		_lowerCamelCase    ,		atol=1E-4))

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

  @slow
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg')
      model.to(_lowerCamelCase)

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

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


  @slow
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg')
      model.to(_lowerCamelCase)
      SCREAMING_SNAKE_CASE_						=    512

      SCREAMING_SNAKE_CASE_						=    XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg')

      SCREAMING_SNAKE_CASE_						=    (
          '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.'
      )
      SCREAMING_SNAKE_CASE_						=    (
          '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.'
      )
      SCREAMING_SNAKE_CASE_						=    (
          '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。'
      )

      SCREAMING_SNAKE_CASE_						=    tokenizer(
          [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]    ,		padding=_lowerCamelCase    ,		max_length=255    ,		return_tensors='pt').input_ids
      SCREAMING_SNAKE_CASE_						=    input_ids.to(_lowerCamelCase)

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

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










"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list

class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			'<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '			)
      lowercase				       =			dict(zip(_lowerCamelCase				,						range(len(_lowerCamelCase			)			)			)			)

      lowercase				       =			{
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      lowercase				       =			{
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 1_6_0_0_0,
          'return_attention_mask': False,
          'do_normalize': True,
      }

      lowercase				       =			tempfile.mkdtemp()
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)
      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(json.dumps(_lowerCamelCase			) + '\n'			)

      with open(self.feature_extraction_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(json.dumps(_lowerCamelCase			) + '\n'			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      lowercase				       =			self.add_kwargs_tokens_map.copy()
      kwargs.update(_lowerCamelCase			)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_feature_extractor()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      processor.save_pretrained(self.tmpdirname			)
      lowercase				       =			WavaVecaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.feature_extractor.to_json_string()				,						feature_extractor.to_json_string()			)
      self.assertIsInstance(processor.feature_extractor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			WavaVecaProcessor(tokenizer=self.get_tokenizer()				,						feature_extractor=self.get_feature_extractor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_feature_extractor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			WavaVecaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.feature_extractor.to_json_string()				,						feature_extractor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.feature_extractor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			floats_list((3, 1_0_0_0)			)

      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			'This is a test string'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      self.assertListEqual(
          processor.model_input_names				,						feature_extractor.model_input_names				,						msg='`processor` and `feature_extractor` model input names do not match'				,						)


