
'''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  lowerCAmelCase__   (       unittest.TestCase       ):



  '''simple docstring'''






  @slow
  def 		_lowerCamelCase	(self  : Dict    )   ->       Union[str, Any]:
        snake_case_						       =							XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"    )
        model.to(lowerCAmelCase_    )

        # encoder-decoder outputs
        snake_case_						       =							torch.tensor([[17, 9_62_08, 10_34_71, 2]]    ).to(lowerCAmelCase_    )
        snake_case_						       =							torch.tensor(
            [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]    ).to(lowerCAmelCase_    )
        snake_case_						       =							model(
            input_ids=lowerCAmelCase_	, attention_mask=lowerCAmelCase_	, encoder_outputs=lowerCAmelCase_	, decoder_input_ids=lowerCAmelCase_    )
        snake_case_						       =							output[0]
        snake_case_						       =							torch.Size((1, 14, 25_00_12)    )
        self.assertEqual(output_predited_logis.shape	, lowerCAmelCase_    )
        snake_case_						       =							torch.tensor(
            [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]    ).to(lowerCAmelCase_    )
        self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	, lowerCAmelCase_	, atol=1E-4    )    )

        # encoder outputs
        snake_case_						       =							model.prophetnet.encoder(lowerCAmelCase_    )[0]
        snake_case_						       =							torch.tensor(
            [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]    ).to(lowerCAmelCase_    )
        snake_case_						       =							torch.Size((1, 4, 10_24)    )
        self.assertEqual(encoder_outputs.shape	, lowerCAmelCase_    )
        self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	, lowerCAmelCase_	, atol=1E-4    )    )

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

  @slow
  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       str:
        snake_case_						       =							XLMProphetNetForConditionalGeneration.from_pretrained(
            "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
        model.to(lowerCAmelCase_    )

        snake_case_						       =							torch.tensor([[17, 9_62_08, 10_34_71, 2]]    ).to(lowerCAmelCase_    )
        snake_case_						       =							torch.tensor(
            [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]    ).to(lowerCAmelCase_    )
        snake_case_						       =							model(
            input_ids=lowerCAmelCase_	, attention_mask=lowerCAmelCase_	, encoder_outputs=lowerCAmelCase_	, decoder_input_ids=lowerCAmelCase_    )
        snake_case_						       =							output[0]
        snake_case_						       =							torch.Size((1, 14, 25_00_12)    )
        self.assertEqual(output_predited_logis.shape	, lowerCAmelCase_    )
        # compare the actual values for a slice.
        snake_case_						       =							torch.tensor(
            [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]    ).to(lowerCAmelCase_    )

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


  @slow
  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Union[str, Any]:
        snake_case_						       =							XLMProphetNetForConditionalGeneration.from_pretrained(
            "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
        model.to(lowerCAmelCase_    )
        snake_case_						       =							5_12

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

        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."
        )
        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."
        )
        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。"
        )

        snake_case_						       =							tokenizer(
            [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]	, padding=lowerCAmelCase_	, max_length=2_55	, return_tensors="pt"    ).input_ids
        snake_case_						       =							input_ids.to(lowerCAmelCase_    )

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

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A           =   {
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A           =   ["DetaImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A           =   [
         "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DetaForObjectDetection",
         "DetaModel",
         "DetaPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_deta import DetaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deta import (
              DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetaForObjectDetection,
              DetaModel,
              DetaPreTrainedModel,
          )

else:
     import sys

     A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

