

"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


__A	:       Optional[int]        =						get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')





@require_torch
class      __UpperCamelCase					(  unittest.TestCase		):


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   tempfile.mkdtemp()

							A     =   SpeechTaTokenizer(__SCREAMING_SNAKE_CASE)
							tokenizer.save_pretrained(self.tmpdirname)

							A     =   {
							    "feature_size": 1,
							    "padding_value": 0.0,
							    "sampling_rate": 1_6_0_0_0,
							    "do_normalize": False,
							    "num_mel_bins": 8_0,
							    "hop_length": 1_6,
							    "win_length": 6_4,
							    "win_function": "hann_window",
							    "fmin": 8_0,
							    "fmax": 7_6_0_0,
							    "mel_floor": 1E-10,
							    "reduction_factor": 2,
							    "return_attention_mask": True,
							}

							A     =   os.path.join(self.tmpdirname	,	__SCREAMING_SNAKE_CASE)
							with open(self.feature_extraction_file	,	"w"	,	encoding="utf-8") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + "\n")


		def    SCREAMING_SNAKE_CASE__  (self					:						int	,	**__SCREAMING_SNAKE_CASE					:						int):
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[int]):
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							shutil.rmtree(self.tmpdirname)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_tokenizer()
							A     =   self.get_feature_extractor()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							processor.save_pretrained(self.tmpdirname)
							A     =   SpeechTaProcessor.from_pretrained(self.tmpdirname)

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   SpeechTaProcessor(tokenizer=self.get_tokenizer()	,	feature_extractor=self.get_feature_extractor())
							processor.save_pretrained(self.tmpdirname)

							A     =   self.get_tokenizer(bos_token="(BOS)"	,	eos_token="(EOS)")
							A     =   self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE	,	padding_value=1.0)

							A     =   SpeechTaProcessor.from_pretrained(
							    self.tmpdirname	,	bos_token="(BOS)"	,	eos_token="(EOS)"	,	do_normalize=__SCREAMING_SNAKE_CASE	,	padding_value=1.0)

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   floats_list((3, 1_0_0_0))

							A     =   feature_extractor(audio=__SCREAMING_SNAKE_CASE	,	return_tensors="np")
							A     =   processor(audio=__SCREAMING_SNAKE_CASE	,	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					:						Tuple):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   floats_list((3, 1_0_0_0))

							A     =   feature_extractor(audio_target=__SCREAMING_SNAKE_CASE	,	return_tensors="np")
							A     =   processor(audio_target=__SCREAMING_SNAKE_CASE	,	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					:						List[str]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   "This is a test string"

							A     =   processor(text=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer(__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   "This is a test string"

							A     =   processor(text_target=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer(__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

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

							A     =   processor.batch_decode(__SCREAMING_SNAKE_CASE)
							A     =   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

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













"""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 			a	(  unittest.TestCase      ):







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

      # encoder-decoder outputs
      lowercase				       =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]			).to(_lowerCamelCase			)
      lowercase				       =			torch.tensor(
          [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]			).to(_lowerCamelCase			)
      lowercase				       =			model(
          input_ids=_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						encoder_outputs=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase			)
      lowercase				       =			output[0]
      lowercase				       =			torch.Size((1, 1_4, 2_5_0_0_1_2)			)
      self.assertEqual(output_predited_logis.shape				,						_lowerCamelCase			)
      lowercase				       =			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(_lowerCamelCase			)
      self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)

      # encoder outputs
      lowercase				       =			model.prophetnet.encoder(_lowerCamelCase			)[0]
      lowercase				       =			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			)
      lowercase				       =			torch.Size((1, 4, 1_0_2_4)			)
      self.assertEqual(encoder_outputs.shape				,						_lowerCamelCase			)
      self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)

      # decoder outputs
      lowercase				       =			model.prophetnet.decoder(
          _lowerCamelCase				,						encoder_hidden_states=_lowerCamelCase				,						)
      lowercase				       =			decoder_outputs[1].view(1				,						model.config.ngram				,						1_4				,						-1			)
      lowercase				       =			model.lm_head(_lowerCamelCase			)
      lowercase				       =			predicting_streams_logits[:, 0]
      self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)







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

      lowercase				       =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]			).to(_lowerCamelCase			)
      lowercase				       =			torch.tensor(
          [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]			).to(_lowerCamelCase			)
      lowercase				       =			model(
          input_ids=_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						encoder_outputs=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase			)
      lowercase				       =			output[0]
      lowercase				       =			torch.Size((1, 1_4, 2_5_0_0_1_2)			)
      self.assertEqual(output_predited_logis.shape				,						_lowerCamelCase			)
      # compare the actual values for a slice.
      lowercase				       =			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      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
      model.to(_lowerCamelCase			)
      lowercase				       =			5_1_2

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

      lowercase				       =			(
          '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.'
      )
      lowercase				       =			(
          '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.'
      )
      lowercase				       =			(
          '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。'
      )

      lowercase				       =			tokenizer(
          [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]				,						padding=_lowerCamelCase				,						max_length=2_5_5				,						return_tensors='pt'			).input_ids
      lowercase				       =			input_ids.to(_lowerCamelCase			)

      lowercase				       =			model.generate(
          _lowerCamelCase				,						num_beams=1_0				,						length_penalty=1.0				,						no_repeat_ngram_size=3				,						early_stopping=_lowerCamelCase			)
      lowercase				       =			[tokenizer.decode(_lowerCamelCase				,						skip_special_tokens=_lowerCamelCase			) for g in summary_ids]
      lowercase				       =			'Microsoft to end Windows 7 free support after January 14, 2020'
      lowercase				       =			'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
      lowercase				       =			'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				,						)

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


