






"""simple docstring"""



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 			a	(  __UpperCAmelCase,							unittest.TestCase      ):
    UpperCAmelCase_					:	Tuple      	=KandinskyVaaPriorEmbaEmbPipeline
    UpperCAmelCase_					:	int      	=["prompt", "image"]
    UpperCAmelCase_					:	Any      	=["prompt", "image"]
    UpperCAmelCase_					:	Optional[Any]      	=[
        "num_images_per_prompt",
        "strength",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    UpperCAmelCase_					:	List[Any]      	=False







    @property
    def      UpperCamelCase_ (     self			):
      return 3_2







    @property
    def      UpperCamelCase_ (     self			):
      return 3_2







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







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







    @property
    def      UpperCamelCase_ (     self			):
      return 1_0_0







    @property
    def      UpperCamelCase_ (     self			):
      lowercase				       =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
      return tokenizer







    @property
    def      UpperCamelCase_ (     self			):
      torch.manual_seed(0			)
      lowercase				       =			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=3_7				,						layer_norm_eps=1e-05				,						num_attention_heads=4				,						num_hidden_layers=5				,						pad_token_id=1				,						vocab_size=1_0_0_0				,						)
      return CLIPTextModelWithProjection(_lowerCamelCase			)







    @property
    def      UpperCamelCase_ (     self			):
      torch.manual_seed(0			)

      lowercase				       =			{
          'num_attention_heads': 2,
          'attention_head_dim': 1_2,
          'embedding_dim': self.text_embedder_hidden_size,
          'num_layers': 1,
      }

      lowercase				       =			PriorTransformer(**_lowerCamelCase			)
      # 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				       =			nn.Parameter(torch.ones(model.clip_std.shape			)			)
      return model







    @property
    def      UpperCamelCase_ (     self			):
      torch.manual_seed(0			)
      lowercase				       =			CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size				,						image_size=2_2_4				,						projection_dim=self.text_embedder_hidden_size				,						intermediate_size=3_7				,						num_attention_heads=4				,						num_channels=3				,						num_hidden_layers=5				,						patch_size=1_4				,						)

      lowercase				       =			CLIPVisionModelWithProjection(_lowerCamelCase			)
      return model







    @property
    def      UpperCamelCase_ (     self			):
      lowercase				       =			CLIPImageProcessor(
          crop_size=2_2_4				,						do_center_crop=_lowerCamelCase				,						do_normalize=_lowerCamelCase				,						do_resize=_lowerCamelCase				,						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_2_4				,						)

      return image_processor







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.dummy_prior
      lowercase				       =			self.dummy_image_encoder
      lowercase				       =			self.dummy_text_encoder
      lowercase				       =			self.dummy_tokenizer
      lowercase				       =			self.dummy_image_processor

      lowercase				       =			UnCLIPScheduler(
          variance_type='fixed_small_log'				,						prediction_type='sample'				,						num_train_timesteps=1_0_0_0				,						clip_sample=_lowerCamelCase				,						clip_sample_range=1_0.0				,						)

      lowercase				       =			{
          'prior': prior,
          'image_encoder': image_encoder,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'scheduler': scheduler,
          'image_processor': image_processor,
      }

      return components







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase=0			):

      if str(_lowerCamelCase			).startswith('mps'			):
        lowercase				       =			torch.manual_seed(_lowerCamelCase			)
      else:
        lowercase				       =			torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)

      lowercase				       =			floats_tensor((1, 3, 6_4, 6_4)				,						rng=random.Random(_lowerCamelCase			)			).to(_lowerCamelCase			)
      lowercase				       =			image.cpu().permute(0				,						2				,						3				,						1			)[0]
      lowercase				       =			Image.fromarray(np.uinta(_lowerCamelCase			)			).convert('RGB'			).resize((2_5_6, 2_5_6)			)

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			'cpu'

      lowercase				       =			self.get_dummy_components()

      lowercase				       =			self.pipeline_class(**_lowerCamelCase			)
      lowercase				       =			pipe.to(_lowerCamelCase			)

      pipe.set_progress_bar_config(disable=_lowerCamelCase			)

      lowercase				       =			pipe(**self.get_dummy_inputs(_lowerCamelCase			)			)
      lowercase				       =			output.image_embeds

      lowercase				       =			pipe(
          **self.get_dummy_inputs(_lowerCamelCase			)				,						return_dict=_lowerCamelCase				,						)[0]

      lowercase				       =			image[0, -1_0:]
      lowercase				       =			image_from_tuple[0, -1_0:]

      assert image.shape == (1, 3_2)

      lowercase				       =			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      UpperCamelCase_ (     self			):
      lowercase				       =			torch_device == 'cpu'
      lowercase				       =			True
      lowercase				       =			False

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







    @skip_mps
    def      UpperCamelCase_ (     self			):
      lowercase				       =			torch_device == 'cpu'
      lowercase				       =			False

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









import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase     :			Optional[int]     						=	{"""UserAgent""": UserAgent().random}
lowercase     :			Union[str, Any]     						=	"""https://www.mywaifulist.moe/random"""


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       )
       with open(lowerCAmelCase     ,							'''wb'''       ) as file:
              file.write(image.content       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    BeautifulSoup(requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       ).text     ,							'''html.parser'''       )
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:title'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:image'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''p'''     ,							id='''description'''       ).get_text()
       _UpperCamelCase							,		_UpperCamelCase														=    os.path.splitext(os.path.basename(lowerCAmelCase       )       )
       _UpperCamelCase														=    title.strip().replace(''' '''     ,							'''_'''       )
       _UpperCamelCase														=    f'''{image_title}{image_extension}'''
       save_image(lowerCAmelCase     ,							lowerCAmelCase       )
       return (title, description, image_title)


if __name__ == "__main__":
       lowercase						,lowercase						,lowercase     :			str     						=	random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')






