






"""simple docstring"""



import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput

@dataclass
class 			a	(  a_      ):
    UpperCAmelCase_					:	Union[List[PIL.Image.Image], np.ndarray]


class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			CustomOutput(images=np.random.rand(1				,						3				,						4				,						4			)			)

      # check every way of getting the attribute
      assert isinstance(outputs.images				,						np.ndarray			)
      assert outputs.images.shape == (1, 3, 4, 4)
      assert isinstance(outputs['images']				,						np.ndarray			)
      assert outputs["images"].shape == (1, 3, 4, 4)
      assert isinstance(outputs[0]				,						np.ndarray			)
      assert outputs[0].shape == (1, 3, 4, 4)

      # test with a non-tensor attribute
      lowercase				       =			CustomOutput(images=[PIL.Image.new('RGB'				,						(4, 4)			)]			)

      # check every way of getting the attribute
      assert isinstance(outputs.images				,						_lowerCamelCase			)
      assert isinstance(outputs.images[0]				,						PIL.Image.Image			)
      assert isinstance(outputs['images']				,						_lowerCamelCase			)
      assert isinstance(outputs['images'][0]				,						PIL.Image.Image			)
      assert isinstance(outputs[0]				,						_lowerCamelCase			)
      assert isinstance(outputs[0][0]				,						PIL.Image.Image			)







    def      UpperCamelCase_ (     self			):
      # test output reinitialization with a `dict` for compatibility with `accelerate`
      lowercase				       =			CustomOutput({'images': np.random.rand(1				,						3				,						4				,						4			)}			)

      # check every way of getting the attribute
      assert isinstance(outputs.images				,						np.ndarray			)
      assert outputs.images.shape == (1, 3, 4, 4)
      assert isinstance(outputs['images']				,						np.ndarray			)
      assert outputs["images"].shape == (1, 3, 4, 4)
      assert isinstance(outputs[0]				,						np.ndarray			)
      assert outputs[0].shape == (1, 3, 4, 4)

      # test with a non-tensor attribute
      lowercase				       =			CustomOutput({'images': [PIL.Image.new('RGB'				,						(4, 4)			)]}			)

      # check every way of getting the attribute
      assert isinstance(outputs.images				,						_lowerCamelCase			)
      assert isinstance(outputs.images[0]				,						PIL.Image.Image			)
      assert isinstance(outputs['images']				,						_lowerCamelCase			)
      assert isinstance(outputs['images'][0]				,						PIL.Image.Image			)
      assert isinstance(outputs[0]				,						_lowerCamelCase			)
      assert isinstance(outputs[0][0]				,						PIL.Image.Image			)










"""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				,						)


