
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 SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   List[str]:
         _lowerCAmelCase						=		XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'       )
         model.to(_lowerCAmelCase       )

         # encoder-decoder outputs
         _lowerCAmelCase						=		torch.tensor([[17, 9_6208, 10_3471, 2]]       ).to(_lowerCAmelCase       )
         _lowerCAmelCase						=		torch.tensor(
             [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]       ).to(_lowerCAmelCase       )
         _lowerCAmelCase						=		model(
             input_ids=_lowerCAmelCase  ,      attention_mask=_lowerCAmelCase  ,      encoder_outputs=_lowerCAmelCase  ,      decoder_input_ids=_lowerCAmelCase       )
         _lowerCAmelCase						=		output[0]
         _lowerCAmelCase						=		torch.Size((1, 14, 25_0012)       )
         self.assertEqual(output_predited_logis.shape  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		torch.tensor(
             [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]       ).to(_lowerCAmelCase       )
         self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,      _lowerCAmelCase  ,      atol=1e-4       )       )

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



       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Dict:
         _lowerCAmelCase						=		XLMProphetNetForConditionalGeneration.from_pretrained(
             'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'       )
         model.to(_lowerCAmelCase       )

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

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






       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Tuple:
         _lowerCAmelCase						=		XLMProphetNetForConditionalGeneration.from_pretrained(
             'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'       )
         model.to(_lowerCAmelCase       )
         _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=_lowerCAmelCase  ,      max_length=255  ,      return_tensors='pt'       ).input_ids
         _lowerCAmelCase						=		input_ids.to(_lowerCAmelCase       )

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

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


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		Path(self.tmpdirname       )
         save_json(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['spm_file']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

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

         _lowerCAmelCase						=		SpeechaTextProcessor.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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

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

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(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'  ,      )
