


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

            # encoder-decoder outputs
            a_     :  Optional[int]				      =     torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(lowerCamelCase_     )
            a_     :  List[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(lowerCamelCase_     )
            a_     :  Any				      =     model(
                input_ids=lowerCamelCase_   ,				attention_mask=lowerCamelCase_   ,				encoder_outputs=lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_     )
            a_     :  List[str]				      =     output[0]
            a_     :  Any				      =     torch.Size((1, 1_4, 2_5_0_0_1_2)     )
            self.assertEqual(output_predited_logis.shape   ,				lowerCamelCase_     )
            a_     :  int				      =     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
            a_     :  Dict				      =     model.prophetnet.encoder(lowerCamelCase_     )[0]
            a_     :  Optional[int]				      =     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_     )
            a_     :  Tuple				      =     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
            a_     :  Optional[Any]				      =     model.prophetnet.decoder(
                lowerCamelCase_   ,				encoder_hidden_states=lowerCamelCase_   ,				)
            a_     :  Union[str, Any]				      =     decoder_outputs[1].view(1   ,				model.config.ngram   ,				1_4   ,				-1     )
            a_     :  int				      =     model.lm_head(lowerCamelCase_     )
            a_     :  Optional[Any]				      =     predicting_streams_logits[:, 0]
            self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]   ,				lowerCamelCase_   ,				atol=1E-4     )     )

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  List[Any]				      =     XLMProphetNetForConditionalGeneration.from_pretrained(
                """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )
            model.to(lowerCamelCase_     )

            a_     :  List[Any]				      =     torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(lowerCamelCase_     )
            a_     :  Union[str, 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(lowerCamelCase_     )
            a_     :  List[Any]				      =     model(
                input_ids=lowerCamelCase_   ,				attention_mask=lowerCamelCase_   ,				encoder_outputs=lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_     )
            a_     :  str				      =     output[0]
            a_     :  str				      =     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.
            a_     :  int				      =     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   :  Tuple     ):
            a_     :  str				      =     XLMProphetNetForConditionalGeneration.from_pretrained(
                """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )
            model.to(lowerCamelCase_     )
            a_     :  Tuple				      =     5_1_2

            a_     :  Optional[Any]				      =     XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )

            a_     :  Dict				      =     (
                """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."""
            )
            a_     :  List[Any]				      =     (
                """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."""
            )
            a_     :  Optional[int]				      =     (
                """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。"""
            )

            a_     :  Union[str, Any]				      =     tokenizer(
                [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]   ,				padding=lowerCamelCase_   ,				max_length=2_5_5   ,				return_tensors="""pt"""     ).input_ids
            a_     :  str				      =     input_ids.to(lowerCamelCase_     )

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

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






from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					int:
     return True






  def 							lowercase_     (						self						)				->					int:
     a   :	Optional[int]						=    CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'						)
     a   :	Any						=    'Hugging Face大法好，谁用谁知道。'
     a   :	Tuple						=    'Hugging Face大法好,谁用谁知道。<unk>'
     a   :	Optional[int]						=    '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

     a   :	Optional[int]						=    tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[Any]						=    tokens + [tokenizer.unk_token]

     a   :	str						=    [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase						)  ,					__UpperCAmelCase						)

     a   :	List[Any]						=    tokenizer.decode(__UpperCAmelCase						)
     self.assertEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)





