


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class       __lowerCAmelCase       (	a	,					unittest.TestCase   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    UnCLIPPipeline
  _SCREAMING_SNAKE_CASE						      =    TEXT_TO_IMAGE_PARAMS - {
      'negative_prompt',
      'height',
      'width',
      'negative_prompt_embeds',
      'guidance_scale',
      'prompt_embeds',
      'cross_attention_kwargs',
  }
  _SCREAMING_SNAKE_CASE						      =    TEXT_TO_IMAGE_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE						      =    [
      'generator',
      'return_dict',
      'prior_num_inference_steps',
      'decoder_num_inference_steps',
      'super_res_num_inference_steps',
  ]
  _SCREAMING_SNAKE_CASE						      =    False



  @property
  def    lowerCAmelCase__					(       self    :    Any					)				->	str:




     """simple docstring"""
     return 3_2



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




     """simple docstring"""
     return 3_2



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




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



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




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



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




     """simple docstring"""
     return 1_0_0



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




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



  @property
  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	Tuple:




     """simple docstring"""
     torch.manual_seed(0					)
     snake_case_         =   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    lowerCAmelCase__					(       self    :    str					)				->	Dict:




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

     snake_case_         =   {
         "num_attention_heads": 2,
         "attention_head_dim": 1_2,
         "embedding_dim": self.text_embedder_hidden_size,
         "num_layers": 1,
     }

     snake_case_         =   PriorTransformer(**_lowerCAmelCase					)
     return model



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




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

     snake_case_         =   {
         "clip_embeddings_dim": self.text_embedder_hidden_size,
         "time_embed_dim": self.time_embed_dim,
         "cross_attention_dim": self.cross_attention_dim,
     }

     snake_case_         =   UnCLIPTextProjModel(**_lowerCAmelCase					)
     return model



  @property
  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Any:




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

     snake_case_         =   {
         "sample_size": 3_2,
         # RGB in channels
         "in_channels": 3,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 6,
         "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
         "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
         "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "layers_per_block": 1,
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": "identity",
     }

     snake_case_         =   UNetaDConditionModel(**_lowerCAmelCase					)
     return model



  @property
  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	List[str]:




     """simple docstring"""
     return {
         "sample_size": 6_4,
         "layers_per_block": 1,
         "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
         "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "in_channels": 6,
         "out_channels": 3,
     }



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




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

     snake_case_         =   UNetaDModel(**self.dummy_super_res_kwargs					)
     return model



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




     """simple docstring"""
     torch.manual_seed(1					)

     snake_case_         =   UNetaDModel(**self.dummy_super_res_kwargs					)
     return model



  def    lowerCAmelCase__					(       self    :    Dict					)				->	List[Any]:




     """simple docstring"""
     snake_case_         =   self.dummy_prior
     snake_case_         =   self.dummy_decoder
     snake_case_         =   self.dummy_text_proj
     snake_case_         =   self.dummy_text_encoder
     snake_case_         =   self.dummy_tokenizer
     snake_case_         =   self.dummy_super_res_first
     snake_case_         =   self.dummy_super_res_last

     snake_case_         =   UnCLIPScheduler(
         variance_type="fixed_small_log"      ,			prediction_type="sample"      ,			num_train_timesteps=1_0_0_0      ,			clip_sample_range=5.0      ,			)

     snake_case_         =   UnCLIPScheduler(
         variance_type="learned_range"      ,			prediction_type="epsilon"      ,			num_train_timesteps=1_0_0_0      ,			)

     snake_case_         =   UnCLIPScheduler(
         variance_type="fixed_small_log"      ,			prediction_type="epsilon"      ,			num_train_timesteps=1_0_0_0      ,			)

     snake_case_         =   {
         "prior": prior,
         "decoder": decoder,
         "text_proj": text_proj,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "super_res_first": super_res_first,
         "super_res_last": super_res_last,
         "prior_scheduler": prior_scheduler,
         "decoder_scheduler": decoder_scheduler,
         "super_res_scheduler": super_res_scheduler,
     }

     return components



  def    lowerCAmelCase__					(       self    :    Tuple      ,			_lowerCAmelCase    :    str      ,			_lowerCAmelCase    :    Dict=0					)				->	str:




     """simple docstring"""
     if str(_lowerCAmelCase					).startswith("mps"					):
        snake_case_         =   torch.manual_seed(_lowerCAmelCase					)
     else:
        snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
     snake_case_         =   {
         "prompt": "horse",
         "generator": generator,
         "prior_num_inference_steps": 2,
         "decoder_num_inference_steps": 2,
         "super_res_num_inference_steps": 2,
         "output_type": "numpy",
     }
     return inputs



  def    lowerCAmelCase__					(       self    :    Dict					)				->	Any:




     """simple docstring"""
     snake_case_         =   "cpu"

     snake_case_         =   self.get_dummy_components()

     snake_case_         =   self.pipeline_class(**_lowerCAmelCase					)
     snake_case_         =   pipe.to(_lowerCAmelCase					)

     pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

     snake_case_         =   pipe(**self.get_dummy_inputs(_lowerCAmelCase					)					)
     snake_case_         =   output.images

     snake_case_         =   pipe(
         **self.get_dummy_inputs(_lowerCAmelCase					)      ,			return_dict=_lowerCAmelCase      ,			)[0]

     snake_case_         =   image[0, -3:, -3:, -1]
     snake_case_         =   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 6_4, 6_4, 3)

     snake_case_         =   np.array(
         [
             0.9_997,
             0.9_988,
             0.0_028,
             0.9_997,
             0.9_984,
             0.9_965,
             0.0_029,
             0.9_986,
             0.0_025,
         ]					)

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



  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Optional[Any]:




     """simple docstring"""
     snake_case_         =   torch.device("cpu"					)


     class       __lowerCAmelCase       :






       """simple docstring"""





       _SCREAMING_SNAKE_CASE						      =    1






     snake_case_         =   self.get_dummy_components()

     snake_case_         =   self.pipeline_class(**_lowerCAmelCase					)
     snake_case_         =   pipe.to(_lowerCAmelCase					)

     snake_case_         =   components["prior"]
     snake_case_         =   components["decoder"]
     snake_case_         =   components["super_res_first"]
     snake_case_         =   components["tokenizer"]
     snake_case_         =   components["text_encoder"]

     snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
     snake_case_         =   prior.dtype
     snake_case_         =   1

     snake_case_         =   (batch_size, prior.config.embedding_dim)
     snake_case_         =   pipe.prepare_latents(
         _lowerCAmelCase      ,			dtype=_lowerCAmelCase      ,			device=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			latents=_lowerCAmelCase      ,			scheduler=DummyScheduler()					)
     snake_case_         =   (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
     snake_case_         =   pipe.prepare_latents(
         _lowerCAmelCase      ,			dtype=_lowerCAmelCase      ,			device=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			latents=_lowerCAmelCase      ,			scheduler=DummyScheduler()					)

     snake_case_         =   (
         batch_size,
         super_res_first.config.in_channels // 2,
         super_res_first.config.sample_size,
         super_res_first.config.sample_size,
     )
     snake_case_         =   pipe.prepare_latents(
         _lowerCAmelCase      ,			dtype=_lowerCAmelCase      ,			device=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			latents=_lowerCAmelCase      ,			scheduler=DummyScheduler()					)

     pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

     snake_case_         =   "this is a prompt example"

     snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
     snake_case_         =   pipe(
         [prompt]      ,			generator=_lowerCAmelCase      ,			prior_num_inference_steps=2      ,			decoder_num_inference_steps=2      ,			super_res_num_inference_steps=2      ,			prior_latents=_lowerCAmelCase      ,			decoder_latents=_lowerCAmelCase      ,			super_res_latents=_lowerCAmelCase      ,			output_type="np"      ,			)
     snake_case_         =   output.images

     snake_case_         =   tokenizer(
         _lowerCAmelCase      ,			padding="max_length"      ,			max_length=tokenizer.model_max_length      ,			return_tensors="pt"      ,			)
     snake_case_         =   text_encoder(text_inputs.input_ids					)
     snake_case_         =   text_inputs.attention_mask

     snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
     snake_case_         =   pipe(
         generator=_lowerCAmelCase      ,			prior_num_inference_steps=2      ,			decoder_num_inference_steps=2      ,			super_res_num_inference_steps=2      ,			prior_latents=_lowerCAmelCase      ,			decoder_latents=_lowerCAmelCase      ,			super_res_latents=_lowerCAmelCase      ,			text_model_output=_lowerCAmelCase      ,			text_attention_mask=_lowerCAmelCase      ,			output_type="np"      ,			)[0]

     # make sure passing text embeddings manually is identical
     assert np.abs(image - image_from_text					).max() < 1e-4



  @skip_mps
  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	Any:




     """simple docstring"""
     snake_case_         =   torch_device == "cpu"

     self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase      ,			expected_max_diff=0.01					)



  @skip_mps
  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Any:




     """simple docstring"""
     snake_case_         =   torch_device == "cpu"
     snake_case_         =   True
     snake_case_         =   [
         "prior_num_inference_steps",
         "decoder_num_inference_steps",
         "super_res_num_inference_steps",
     ]

     self._test_inference_batch_single_identical(
         test_max_difference=_lowerCAmelCase      ,			relax_max_difference=_lowerCAmelCase      ,			additional_params_copy_to_batched_inputs=_lowerCAmelCase      ,			)



  def    lowerCAmelCase__					(       self    :    Optional[Any]					)				->	Any:




     """simple docstring"""
     snake_case_         =   [
         "prior_num_inference_steps",
         "decoder_num_inference_steps",
         "super_res_num_inference_steps",
     ]

     if torch_device == "mps":
        # TODO: MPS errors with larger batch sizes
        snake_case_         =   [2, 3]
        self._test_inference_batch_consistent(
            batch_sizes=_lowerCAmelCase      ,			additional_params_copy_to_batched_inputs=_lowerCAmelCase      ,			)
     else:
        self._test_inference_batch_consistent(
            additional_params_copy_to_batched_inputs=_lowerCAmelCase					)



  @skip_mps
  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	int:




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



  @skip_mps
  def    lowerCAmelCase__					(       self    :    Any					)				->	Optional[int]:




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



  @skip_mps
  def    lowerCAmelCase__					(       self    :    str					)				->	str:




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






@nightly
class       __lowerCAmelCase       (	unittest.TestCase   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    int					)				->	Tuple:




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



  def    lowerCAmelCase__					(       self    :    Any					)				->	int:




     """simple docstring"""
     snake_case_         =   load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/unclip/karlo_v1_alpha_horse_cpu.npy"					)

     snake_case_         =   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"					)
     pipeline.set_progress_bar_config(disable=_lowerCAmelCase					)

     snake_case_         =   torch.manual_seed(0					)
     snake_case_         =   pipeline(
         "horse"      ,			num_images_per_prompt=1      ,			generator=_lowerCAmelCase      ,			output_type="np"      ,			)

     snake_case_         =   output.images[0]

     assert image.shape == (2_5_6, 2_5_6, 3)
     assert np.abs(expected_image - image					).max() < 1e-1






@slow
@require_torch_gpu
class       __lowerCAmelCase       (	unittest.TestCase   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    int					)				->	List[str]:




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



  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Dict:




     """simple docstring"""
     snake_case_         =   load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/unclip/karlo_v1_alpha_horse_fp16.npy"					)

     snake_case_         =   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"      ,			torch_dtype=torch.floataa					)
     snake_case_         =   pipeline.to(_lowerCAmelCase					)
     pipeline.set_progress_bar_config(disable=_lowerCAmelCase					)

     snake_case_         =   torch.Generator(device="cpu"					).manual_seed(0					)
     snake_case_         =   pipeline(
         "horse"      ,			generator=_lowerCAmelCase      ,			output_type="np"      ,			)

     snake_case_         =   output.images[0]

     assert image.shape == (2_5_6, 2_5_6, 3)

     assert_mean_pixel_difference(_lowerCAmelCase      ,			_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    Dict					)				->	List[Any]:




     """simple docstring"""
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     snake_case_         =   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"      ,			torch_dtype=torch.floataa					)
     snake_case_         =   pipe.to(_lowerCAmelCase					)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     snake_case_         =   pipe(
         "horse"      ,			num_images_per_prompt=1      ,			prior_num_inference_steps=2      ,			decoder_num_inference_steps=2      ,			super_res_num_inference_steps=2      ,			output_type="np"      ,			)

     snake_case_         =   torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 1_0**9








import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE		:Dict											=       {'''UserAgent''': UserAgent().random}
SCREAMING_SNAKE_CASE		:Dict											=       '''https://www.mywaifulist.moe/random'''



def      _lowerCAmelCase  (							lowerCAmelCase_							:str						,  lowerCAmelCase_							:str							)->None:
   '''simple docstring'''






   snake_case_         =   requests.get(lowerCAmelCase_						,  headers=lowerCAmelCase_							)
   with open(lowerCAmelCase_						,  "wb"							) as file:
      file.write(image.content							)



def      _lowerCAmelCase  (							)->tuple[str, str, str]:
   '''simple docstring'''






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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE		:Optional[Any]											=       random_anime_character()
   print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')




