

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    ):







  """simple docstring"""




  @slow
  def 	_lowercase (     self      :	Tuple						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
        model.to(UpperCAmelCase__						)

        # encoder-decoder outputs
        SCREAMING_SNAKE_CASE	: Dict					       =				torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]						).to(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				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(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				model(
            input_ids=UpperCAmelCase__			,			attention_mask=UpperCAmelCase__			,			encoder_outputs=UpperCAmelCase__			,			decoder_input_ids=UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				output[0]
        SCREAMING_SNAKE_CASE	: Dict					       =				torch.Size((1, 1_4, 2_5_0_0_1_2)						)
        self.assertEqual(output_predited_logis.shape			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				torch.tensor(
            [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]						).to(UpperCAmelCase__						)
        self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]			,			UpperCAmelCase__			,			atol=1e-4						)						)

        # encoder outputs
        SCREAMING_SNAKE_CASE	: str					       =				model.prophetnet.encoder(UpperCAmelCase__						)[0]
        SCREAMING_SNAKE_CASE	: List[Any]					       =				torch.tensor(
            [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]						).to(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				torch.Size((1, 4, 1_0_2_4)						)
        self.assertEqual(encoder_outputs.shape			,			UpperCAmelCase__						)
        self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]			,			UpperCAmelCase__			,			atol=1e-4						)						)

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


  @slow
  def 	_lowercase (     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				XLMProphetNetForConditionalGeneration.from_pretrained(
            """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
        model.to(UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]						).to(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				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(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model(
            input_ids=UpperCAmelCase__			,			attention_mask=UpperCAmelCase__			,			encoder_outputs=UpperCAmelCase__			,			decoder_input_ids=UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				output[0]
        SCREAMING_SNAKE_CASE	: List[Any]					       =				torch.Size((1, 1_4, 2_5_0_0_1_2)						)
        self.assertEqual(output_predited_logis.shape			,			UpperCAmelCase__						)
        # compare the actual values for a slice.
        SCREAMING_SNAKE_CASE	: Tuple					       =				torch.tensor(
            [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]						).to(UpperCAmelCase__						)

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




  @slow
  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				XLMProphetNetForConditionalGeneration.from_pretrained(
            """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
        model.to(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				5_1_2

        SCREAMING_SNAKE_CASE	: Tuple					       =				XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)

        SCREAMING_SNAKE_CASE	: List[Any]					       =				(
            """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	: Optional[int]					       =				(
            """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	: List[str]					       =				(
            """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	: List[Any]					       =				tokenizer(
            [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]			,			padding=UpperCAmelCase__			,			max_length=2_5_5			,			return_tensors="""pt"""						).input_ids
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				input_ids.to(UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: List[str]					       =				model.generate(
            UpperCAmelCase__			,			num_beams=1_0			,			length_penalty=1.0			,			no_repeat_ngram_size=3			,			early_stopping=UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				[tokenizer.decode(UpperCAmelCase__			,			skip_special_tokens=UpperCAmelCase__						) for g in summary_ids]
        SCREAMING_SNAKE_CASE	: Dict					       =				"""Microsoft to end Windows 7 free support after January 14, 2020"""
        SCREAMING_SNAKE_CASE	: str					       =				"""Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				"""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__			,			)

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				model.generate(
            UpperCAmelCase__			,			num_beams=1			,			length_penalty=1.0			,			no_repeat_ngram_size=3			,			early_stopping=UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				[
            tokenizer.convert_ids_to_tokens(UpperCAmelCase__			,			skip_special_tokens=UpperCAmelCase__						) for g in summary_ids_beama
        ]
        SCREAMING_SNAKE_CASE	: Any					       =				"""▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """						)
        SCREAMING_SNAKE_CASE	: str					       =				"""▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
            """ """						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				"""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__			,			)







import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

UpperCAmelCase__	:       Dict			      =      [(1,)]






def    __lowercase (       )       ->  Tuple:
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"






def    __lowercase (       )       ->  Optional[Any]:
      SCREAMING_SNAKE_CASE	: Dict					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"







@require_torch
class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  @slow
  @require_torch_gpu
  def 	_lowercase (     self      :	Any						)   ->Optional[Any]:
        """simple docstring"""







        check_slow_torch_cuda()


  @require_torch_gpu
  @slow
  def 	_lowercase (     self      :	Optional[Any]						)   ->Any:
        """simple docstring"""







        check_slow_torch_cuda()




  @parameterized.expand(UpperCAmelCase__						)
  @slow
  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Dict=None						)   ->Optional[Any]:
        """simple docstring"""







        check_slow()






@slow
@require_torch_gpu
def    __lowercase (       )       ->  Optional[int]:
      check_slow_torch_cuda()






@require_torch_gpu
@slow
def    __lowercase (       )       ->  int:
      check_slow_torch_cuda()






@slow
@pytest.mark.parametrize("""param"""  ,      [1]     )
def    __lowercase (       _A     )       ->  Optional[int]:
      check_slow()







@pytest.mark.parametrize("""param"""  ,      [1]     )
@slow
def    __lowercase (       _A     )       ->  str:
      check_slow()




