

'''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				):

				@slow
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased' )
					model.to(lowercase_ )

					# encoder-decoder outputs
					snake_case							=					torch.tensor([[17, 96208, 103471, 2]] ).to(lowercase_ )
					snake_case							=					torch.tensor(
					    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]] ).to(lowercase_ )
					snake_case							=					model(
					    input_ids=lowercase_,						attention_mask=lowercase_,						encoder_outputs=lowercase_,						decoder_input_ids=lowercase_ )
					snake_case							=					output[0]
					snake_case							=					torch.Size((1, 14, 250012) )
					self.assertEqual(output_predited_logis.shape,						lowercase_ )
					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(lowercase_ )
					self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3],						lowercase_,						atol=1E-4 ) )

					# encoder outputs
					snake_case							=					model.prophetnet.encoder(lowercase_ )[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(lowercase_ )
					snake_case							=					torch.Size((1, 4, 1024) )
					self.assertEqual(encoder_outputs.shape,						lowercase_ )
					self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3],						lowercase_,						atol=1E-4 ) )

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

				@slow
				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					XLMProphetNetForConditionalGeneration.from_pretrained(
					    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg' )
					model.to(lowercase_ )

					snake_case							=					torch.tensor([[17, 96208, 103471, 2]] ).to(lowercase_ )
					snake_case							=					torch.tensor(
					    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]] ).to(lowercase_ )
					snake_case							=					model(
					    input_ids=lowercase_,						attention_mask=lowercase_,						encoder_outputs=lowercase_,						decoder_input_ids=lowercase_ )
					snake_case							=					output[0]
					snake_case							=					torch.Size((1, 14, 250012) )
					self.assertEqual(output_predited_logis.shape,						lowercase_ )
					# 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(lowercase_ )

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






				@slow
				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					XLMProphetNetForConditionalGeneration.from_pretrained(
					    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg' )
					model.to(lowercase_ )
					snake_case							=					512

					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=lowercase_,						max_length=255,						return_tensors='pt' ).input_ids
					snake_case							=					input_ids.to(lowercase_ )

					snake_case							=					model.generate(
					    lowercase_,						num_beams=10,						length_penalty=1.0,						no_repeat_ngram_size=3,						early_stopping=lowercase_ )
					snake_case							=					[tokenizer.decode(lowercase_,						skip_special_tokens=lowercase_ ) 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],						lowercase_,						)

					snake_case							=					model.generate(
					    lowercase_,						num_beams=1,						length_penalty=1.0,						no_repeat_ngram_size=3,						early_stopping=lowercase_ )
					snake_case							=					[
					    tokenizer.convert_ids_to_tokens(lowercase_,						skip_special_tokens=lowercase_ ) 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],						lowercase_,						)











import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		__UpperCAmelCase(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     KandinskyVaaPriorEmbaEmbPipeline
     __UpperCAmelCase						:			str							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Dict							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Optional[Any]							=     [
         """num_images_per_prompt""",
         """strength""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     __UpperCAmelCase						:			Dict							=     False

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      int:






         """simple docstring"""
         return 32

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      Any:






         """simple docstring"""
         return 32

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      int:






         """simple docstring"""
         return self.time_input_dim

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[Any]:






         """simple docstring"""
         return self.time_input_dim * 4

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Tuple:






         """simple docstring"""
         return 1_00

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
         return tokenizer

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      Optional[int]:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							CLIPTextConfig(
             bos_token_id=0    ,					eos_token_id=2    ,					hidden_size=self.text_embedder_hidden_size    ,					projection_dim=self.text_embedder_hidden_size    ,					intermediate_size=37    ,					layer_norm_eps=1E-05    ,					num_attention_heads=4    ,					num_hidden_layers=5    ,					pad_token_id=1    ,					vocab_size=10_00    ,					)
         return CLIPTextModelWithProjection(A     )

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      List[Any]:






         """simple docstring"""
         torch.manual_seed(0     )

         a__  			=							{
             """num_attention_heads""": 2,
             """attention_head_dim""": 12,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }

         a__  			=							PriorTransformer(**A     )
         # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
         a__  			=							nn.Parameter(torch.ones(model.clip_std.shape     )     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size    ,					image_size=2_24    ,					projection_dim=self.text_embedder_hidden_size    ,					intermediate_size=37    ,					num_attention_heads=4    ,					num_channels=3    ,					num_hidden_layers=5    ,					patch_size=14    ,					)

         a__  			=							CLIPVisionModelWithProjection(A     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPImageProcessor(
             crop_size=2_24    ,					do_center_crop=A    ,					do_normalize=A    ,					do_resize=A    ,					image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,					image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,					resample=3    ,					size=2_24    ,					)

         return image_processor

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.dummy_prior
         a__  			=							self.dummy_image_encoder
         a__  			=							self.dummy_text_encoder
         a__  			=							self.dummy_tokenizer
         a__  			=							self.dummy_image_processor

         a__  			=							UnCLIPScheduler(
             variance_type="""fixed_small_log"""    ,					prediction_type="""sample"""    ,					num_train_timesteps=10_00    ,					clip_sample=A    ,					clip_sample_range=1_0.0    ,					)

         a__  			=							{
             """prior""": prior,
             """image_encoder""": image_encoder,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """scheduler""": scheduler,
             """image_processor""": image_processor,
         }

         return components

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tuple    ,					A			:							Optional[Any]=0     )      ->      Any:






         """simple docstring"""
         if str(A     ).startswith("""mps"""     ):
             a__  			=							torch.manual_seed(A     )
         else:
             a__  			=							torch.Generator(device=A     ).manual_seed(A     )

         a__  			=							floats_tensor((1, 3, 64, 64)    ,					rng=random.Random(A     )     ).to(A     )
         a__  			=							image.cpu().permute(0    ,					2    ,					3    ,					1     )[0]
         a__  			=							Image.fromarray(np.uinta(A     )     ).convert("""RGB"""     ).resize((2_56, 2_56)     )

         a__  			=							{
             """prompt""": """horse""",
             """image""": init_image,
             """strength""": 0.5,
             """generator""": generator,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							"""cpu"""

         a__  			=							self.get_dummy_components()

         a__  			=							self.pipeline_class(**A     )
         a__  			=							pipe.to(A     )

         pipe.set_progress_bar_config(disable=A     )

         a__  			=							pipe(**self.get_dummy_inputs(A     )     )
         a__  			=							output.image_embeds

         a__  			=							pipe(
             **self.get_dummy_inputs(A     )    ,					return_dict=A    ,					)[0]

         a__  			=							image[0, -10:]
         a__  			=							image_from_tuple[0, -10:]

         assert image.shape == (1, 32)

         a__  			=							np.array(
             [
                 0.1_0_7_1_2_8_4,
                 1.3_3_3_0_2_7_1,
                 0.6_1_2_6_0_2_2_3,
                 -0.6_6_9_1_0_6_5,
                 -0.3_8_4_6_8_5_2,
                 -1.0_3_0_3_6_6_1,
                 0.2_2_7_1_6_1_1_1,
                 0.0_3_3_4_8_9_0_1,
                 0.3_0_0_4_0_6_7_5,
                 -0.2_4_8_0_5_0_2_9,
             ]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2

     @skip_mps
     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							True
         a__  			=							False

         self._test_inference_batch_single_identical(
             test_max_difference=A    ,					relax_max_difference=A    ,					test_mean_pixel_difference=A    ,					)






     @skip_mps
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      Union[str, Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							False

         self._test_attention_slicing_forward_pass(
             test_max_difference=A    ,					test_mean_pixel_difference=A    ,					)






