
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 __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		str=       KandinskyVaaPriorEmbaEmbPipeline
    _a	:		List[str]=       ["prompt", "image"]
    _a	:		str=       ["prompt", "image"]
    _a	:		Optional[int]=       [
        "num_images_per_prompt",
        "strength",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    _a	:		Dict=       False
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return 32
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return 32
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.time_input_dim
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.time_input_dim * 4
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return 100
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
           return tokenizer
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           torch.manual_seed(0   )
           lowercase							:  str 			=   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=1000	,)
           return CLIPTextModelWithProjection(snake_case   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           torch.manual_seed(0   )

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

           lowercase							:  Tuple 			=   PriorTransformer(**snake_case   )
           # 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
           lowercase							:  Any 			=   nn.Parameter(torch.ones(model.clip_std.shape   )   )
           return model
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           torch.manual_seed(0   )
           lowercase							:  Optional[Any] 			=   CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size	,image_size=224	,projection_dim=self.text_embedder_hidden_size	,intermediate_size=37	,num_attention_heads=4	,num_channels=3	,num_hidden_layers=5	,patch_size=14	,)

           lowercase							:  List[str] 			=   CLIPVisionModelWithProjection(snake_case   )
           return model
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   CLIPImageProcessor(
               crop_size=224	,do_center_crop=snake_case	,do_normalize=snake_case	,do_resize=snake_case	,image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]	,image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]	,resample=3	,size=224	,)

           return image_processor
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.dummy_prior
           lowercase							:  Optional[Any] 			=   self.dummy_image_encoder
           lowercase							:  List[str] 			=   self.dummy_text_encoder
           lowercase							:  Any 			=   self.dummy_tokenizer
           lowercase							:  Tuple 			=   self.dummy_image_processor

           lowercase							:  int 			=   UnCLIPScheduler(
               variance_type="""fixed_small_log"""	,prediction_type="""sample"""	,num_train_timesteps=1000	,clip_sample=snake_case	,clip_sample_range=10.0	,)

           lowercase							:  Union[str, Any] 			=   {
               """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	,snake_case	,snake_case=0   ):



           '''simple docstring'''







           if str(snake_case   ).startswith("""mps"""   ):
                  lowercase							:  int 			=   torch.manual_seed(snake_case   )
           else:
                  lowercase							:  Optional[Any] 			=   torch.Generator(device=snake_case   ).manual_seed(snake_case   )

           lowercase							:  Dict 			=   floats_tensor((1, 3, 64, 64)	,rng=random.Random(snake_case   )   ).to(snake_case   )
           lowercase							:  List[str] 			=   image.cpu().permute(0	,2	,3	,1   )[0]
           lowercase							:  Any 			=   Image.fromarray(np.uinta(snake_case   )   ).convert("""RGB"""   ).resize((256, 256)   )

           lowercase							:  str 			=   {
               """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   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   """cpu"""

           lowercase							:  Optional[int] 			=   self.get_dummy_components()

           lowercase							:  Union[str, Any] 			=   self.pipeline_class(**snake_case   )
           lowercase							:  Any 			=   pipe.to(snake_case   )

           pipe.set_progress_bar_config(disable=snake_case   )

           lowercase							:  Dict 			=   pipe(**self.get_dummy_inputs(snake_case   )   )
           lowercase							:  Tuple 			=   output.image_embeds

           lowercase							:  Optional[Any] 			=   pipe(
               **self.get_dummy_inputs(snake_case   )	,return_dict=snake_case	,)[0]

           lowercase							:  Tuple 			=   image[0, -10:]
           lowercase							:  Tuple 			=   image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           lowercase							:  Union[str, Any] 			=   np.array(
               [
                   0.1_071_284,
                   1.3_330_271,
                   0.61_260_223,
                   -0.6_691_065,
                   -0.3_846_852,
                   -1.0_303_661,
                   0.22_716_111,
                   0.03_348_901,
                   0.30_040_675,
                   -0.24_805_029,
               ]   )

           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   ):



           '''simple docstring'''







           lowercase							:  Any 			=   torch_device == """cpu"""
           lowercase							:  int 			=   True
           lowercase							:  List[Any] 			=   False

           self._test_inference_batch_single_identical(
               test_max_difference=snake_case	,relax_max_difference=snake_case	,test_mean_pixel_difference=snake_case	,)
    @skip_mps
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   torch_device == """cpu"""
           lowercase							:  Optional[int] 			=   False

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


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 __snake_case	(   unittest.TestCase  ):
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""   )
           model.to(snake_case   )

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

           # encoder outputs
           lowercase							:  Tuple 			=   model.prophetnet.encoder(snake_case   )[0]
           lowercase							:  Any 			=   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(snake_case   )
           lowercase							:  Optional[Any] 			=   torch.Size((1, 4, 1024)   )
           self.assertEqual(encoder_outputs.shape	,snake_case   )
           self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	,snake_case	,atol=1e-4   )   )

           # decoder outputs
           lowercase							:  Tuple 			=   model.prophetnet.decoder(
               snake_case	,encoder_hidden_states=snake_case	,)
           lowercase							:  Optional[int] 			=   decoder_outputs[1].view(1	,model.config.ngram	,14	,-1   )
           lowercase							:  Optional[int] 			=   model.lm_head(snake_case   )
           lowercase							:  List[Any] 			=   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'''







           lowercase							:  Optional[Any] 			=   XLMProphetNetForConditionalGeneration.from_pretrained(
               """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )
           model.to(snake_case   )

           lowercase							:  Optional[int] 			=   torch.tensor([[17, 96208, 103471, 2]]   ).to(snake_case   )
           lowercase							:  Dict 			=   torch.tensor(
               [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]   ).to(snake_case   )
           lowercase							:  Any 			=   model(
               input_ids=snake_case	,attention_mask=snake_case	,encoder_outputs=snake_case	,decoder_input_ids=snake_case   )
           lowercase							:  Optional[int] 			=   output[0]
           lowercase							:  Dict 			=   torch.Size((1, 14, 250012)   )
           self.assertEqual(output_predited_logis.shape	,snake_case   )
           # compare the actual values for a slice.
           lowercase							:  int 			=   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(snake_case   )

           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	,snake_case	,atol=1e-4   )   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   XLMProphetNetForConditionalGeneration.from_pretrained(
               """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )
           model.to(snake_case   )
           lowercase							:  Optional[int] 			=   512

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

           lowercase							:  int 			=   (
               """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							:  List[str] 			=   (
               """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							:  str 			=   (
               """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							:  Tuple 			=   tokenizer(
               [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]	,padding=snake_case	,max_length=255	,return_tensors="""pt"""   ).input_ids
           lowercase							:  Union[str, Any] 			=   input_ids.to(snake_case   )

           lowercase							:  Dict 			=   model.generate(
               snake_case	,num_beams=10	,length_penalty=1.0	,no_repeat_ngram_size=3	,early_stopping=snake_case   )
           lowercase							:  Any 			=   [tokenizer.decode(snake_case	,skip_special_tokens=snake_case   ) for g in summary_ids]
           lowercase							:  Union[str, Any] 			=   """Microsoft to end Windows 7 free support after January 14, 2020"""
           lowercase							:  Tuple 			=   """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
           lowercase							:  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]	,snake_case	,)

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