




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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"     )
											model.to(__snake_case     )

											# encoder-decoder outputs
											__lowerCAmelCase							  =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(__snake_case     )
											__lowerCAmelCase							  =			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(__snake_case     )
											__lowerCAmelCase							  =			model(
											    input_ids=__snake_case						,     attention_mask=__snake_case						,     encoder_outputs=__snake_case						,     decoder_input_ids=__snake_case     )
											__lowerCAmelCase							  =			output[0]
											__lowerCAmelCase							  =			torch.Size((1, 1_4, 2_5_0_0_1_2)     )
											self.assertEqual(output_predited_logis.shape						,     __snake_case     )
											__lowerCAmelCase							  =			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(__snake_case     )
											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]						,     __snake_case						,     atol=1E-4     )     )

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

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






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			XLMProphetNetForConditionalGeneration.from_pretrained(
											    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"     )
											model.to(__snake_case     )

											__lowerCAmelCase							  =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(__snake_case     )
											__lowerCAmelCase							  =			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(__snake_case     )
											__lowerCAmelCase							  =			model(
											    input_ids=__snake_case						,     attention_mask=__snake_case						,     encoder_outputs=__snake_case						,     decoder_input_ids=__snake_case     )
											__lowerCAmelCase							  =			output[0]
											__lowerCAmelCase							  =			torch.Size((1, 1_4, 2_5_0_0_1_2)     )
											self.assertEqual(output_predited_logis.shape						,     __snake_case     )
											# compare the actual values for a slice.
											__lowerCAmelCase							  =			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(__snake_case     )

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






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			XLMProphetNetForConditionalGeneration.from_pretrained(
											    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"     )
											model.to(__snake_case     )
											__lowerCAmelCase							  =			5_1_2

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

											__lowerCAmelCase							  =			(
											    '''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.'''
											)
											__lowerCAmelCase							  =			(
											    '''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.'''
											)
											__lowerCAmelCase							  =			(
											    '''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。'''
											)

											__lowerCAmelCase							  =			tokenizer(
											    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]						,     padding=__snake_case						,     max_length=2_5_5						,     return_tensors="pt"     ).input_ids
											__lowerCAmelCase							  =			input_ids.to(__snake_case     )

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

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








import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 			lowerCAmelCase__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Tuple								=			RePaintPipeline
    A_	:  Tuple								=			IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
    A_	:  str								=			PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    A_	:  Optional[Any]								=			IMAGE_INPAINTING_BATCH_PARAMS
    A_	:  Union[str, Any]								=			False





    def        _lowerCamelCase			(						self					: Any						):





       '''simple docstring'''
       torch.manual_seed(0						)
       torch.manual_seed(0						)
       UpperCAmelCase_				:   int							= UNetaDModel(
           block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')							, up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')							, )
       UpperCAmelCase_				:   str							= RePaintScheduler()
       UpperCAmelCase_				:   Optional[Any]							= {'''unet''': unet, '''scheduler''': scheduler}
       return components





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: Optional[Any]							, __snake_case					: int=0						):





       '''simple docstring'''
       if str(__snake_case						).startswith('''mps'''						):
          UpperCAmelCase_				:   Optional[Any]							= torch.manual_seed(__snake_case						)
       else:
          UpperCAmelCase_				:   Optional[Any]							= torch.Generator(device=__snake_case						).manual_seed(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= np.random.RandomState(__snake_case						).standard_normal((1, 3, 32, 32)						)
       UpperCAmelCase_				:   Optional[int]							= torch.from_numpy(__snake_case						).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   Any							= (image > 0).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   List[str]							= {
           '''image''': image,
           '''mask_image''': mask,
           '''generator''': generator,
           '''num_inference_steps''': 5,
           '''eta''': 0.0,
           '''jump_length''': 2,
           '''jump_n_sample''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs





    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:   Optional[int]							= self.get_dummy_components()
       UpperCAmelCase_				:   Dict							= RePaintPipeline(**__snake_case						)
       UpperCAmelCase_				:   int							= sd_pipe.to(__snake_case						)
       sd_pipe.set_progress_bar_config(disable=__snake_case						)

       UpperCAmelCase_				:   List[Any]							= self.get_dummy_inputs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= sd_pipe(**__snake_case						).images
       UpperCAmelCase_				:   Optional[Any]							= image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase_				:   Tuple							= np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3





    @skip_mps
    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       return super().test_save_load_local()





    @unittest.skip('''non-deterministic pipeline'''						)
    def        _lowerCamelCase			(						self					: Optional[Any]						):





       '''simple docstring'''
       return super().test_inference_batch_single_identical()





    @skip_mps
    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       return super().test_dict_tuple_outputs_equivalent()





    @skip_mps
    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       return super().test_save_load_optional_components()





    @skip_mps
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       return super().test_attention_slicing_forward_pass()





@nightly
@require_torch_gpu
class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Tuple						):





       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





    def        _lowerCamelCase			(						self					: List[str]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256.png'''						)
       UpperCAmelCase_				:   int							= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''						)
       UpperCAmelCase_				:   List[Any]							= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256_result.npy'''						)

       UpperCAmelCase_				:   List[str]							= '''google/ddpm-ema-celebahq-256'''
       UpperCAmelCase_				:   Union[str, Any]							= UNetaDModel.from_pretrained(__snake_case						)
       UpperCAmelCase_				:   Dict							= RePaintScheduler.from_pretrained(__snake_case						)

       UpperCAmelCase_				:   Optional[int]							= RePaintPipeline(unet=__snake_case							, scheduler=__snake_case						).to(__snake_case						)
       repaint.set_progress_bar_config(disable=__snake_case						)
       repaint.enable_attention_slicing()

       UpperCAmelCase_				:   Optional[int]							= torch.manual_seed(0						)
       UpperCAmelCase_				:   List[str]							= repaint(
           __snake_case							, __snake_case							, num_inference_steps=250							, eta=0.0							, jump_length=10							, jump_n_sample=10							, generator=__snake_case							, output_type='''np'''							, )
       UpperCAmelCase_				:   Tuple							= output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image						).mean() < 1E-2