






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = StableUnCLIPImgaImgPipeline
  A_						       = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  A_						       = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  A_						       = frozenset(
      []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  A_						       = frozenset([]     )




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  32
     UpperCamelCase_						=  embedder_hidden_size

     # image encoding components

     UpperCamelCase_						=  CLIPImageProcessor(crop_size=32		,		size=32			)

     torch.manual_seed(0			)
     UpperCamelCase_						=  CLIPVisionModelWithProjection(
         CLIPVisionConfig(
             hidden_size=_UpperCAmelCase		,		projection_dim=_UpperCAmelCase		,		num_hidden_layers=5		,		num_attention_heads=4		,		image_size=32		,		intermediate_size=37		,		patch_size=1		,		)			)

     # regular denoising components

     torch.manual_seed(0			)
     UpperCamelCase_						=  StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase			)
     UpperCamelCase_						=  DDPMScheduler(beta_schedule='squaredcos_cap_v2'			)

     torch.manual_seed(0			)
     UpperCamelCase_						=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

     torch.manual_seed(0			)
     UpperCamelCase_						=  CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0		,		eos_token_id=2		,		hidden_size=_UpperCAmelCase		,		projection_dim=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		pad_token_id=1		,		vocab_size=1000		,		)			)

     torch.manual_seed(0			)
     UpperCamelCase_						=  UNetaDConditionModel(
         sample_size=32		,		in_channels=4		,		out_channels=4		,		down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')		,		up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')		,		block_out_channels=(32, 64)		,		attention_head_dim=(2, 4)		,		class_embed_type='projection'		,		projection_class_embeddings_input_dim=embedder_projection_dim * 2		,		cross_attention_dim=_UpperCAmelCase		,		layers_per_block=1		,		upcast_attention=_UpperCAmelCase		,		use_linear_projection=_UpperCAmelCase		,		)

     torch.manual_seed(0			)
     UpperCamelCase_						=  DDIMScheduler(
         beta_schedule='scaled_linear'		,		beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		prediction_type='v_prediction'		,		set_alpha_to_one=_UpperCAmelCase		,		steps_offset=1		,		)

     torch.manual_seed(0			)
     UpperCamelCase_						=  AutoencoderKL()

     UpperCamelCase_						=  {
         # image encoding components
         'feature_extractor': feature_extractor,
         'image_encoder': image_encoder.eval(),
         # image noising components
         'image_normalizer': image_normalizer.eval(),
         'image_noising_scheduler': image_noising_scheduler,
         # regular denoising components
         'tokenizer': tokenizer,
         'text_encoder': text_encoder.eval(),
         'unet': unet.eval(),
         'scheduler': scheduler,
         'vae': vae.eval(),
     }

     return components




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=0		,		_UpperCAmelCase=True			)      ->			Optional[int]:
     if str(_UpperCAmelCase			).startswith('mps'			):
        UpperCamelCase_						=  torch.manual_seed(_UpperCAmelCase			)
     else:
        UpperCamelCase_						=  torch.Generator(device=_UpperCAmelCase			).manual_seed(_UpperCAmelCase			)

     UpperCamelCase_						=  floats_tensor((1, 3, 32, 32)		,		rng=random.Random(_UpperCAmelCase			)			).to(_UpperCAmelCase			)

     if pil_image:
        UpperCamelCase_						=  input_image * 0.5 + 0.5
        UpperCamelCase_						=  input_image.clamp(0		,		1			)
        UpperCamelCase_						=  input_image.cpu().permute(0		,		2		,		3		,		1			).float().numpy()
        UpperCamelCase_						=  DiffusionPipeline.numpy_to_pil(_UpperCAmelCase			)[0]

     return {
         "prompt": "An anime racoon running a marathon",
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "np",
     }




  @skip_mps
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  'cpu'  # ensure determinism for the device-dependent torch.Generator
     UpperCamelCase_						=  self.get_dummy_components()
     UpperCamelCase_						=  StableUnCLIPImgaImgPipeline(**_UpperCAmelCase			)
     UpperCamelCase_						=  sd_pipe.to(_UpperCAmelCase			)
     sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

     UpperCamelCase_						=  self.get_dummy_inputs(_UpperCAmelCase			)
     inputs.update({'image_embeds': None}			)
     UpperCamelCase_						=  sd_pipe(**_UpperCAmelCase			).images
     UpperCamelCase_						=  image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     UpperCamelCase_						=  np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]			)

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




  def  _UpperCAmelCase  (  self			)      ->			Any:
     UpperCamelCase_						=  torch_device in ['cpu', 'mps']

     self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  torch_device in ['cpu', 'mps']

     self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase			)




  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()		,		reason='XFormers attention is only available with CUDA and `xformers` installed'		,		)
  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCAmelCase			)




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






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'			)

     UpperCamelCase_						=  load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'			)

     UpperCamelCase_						=  StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-l-img2img'		,		torch_dtype=torch.floataa			)
     pipe.to(_UpperCAmelCase			)
     pipe.set_progress_bar_config(disable=_UpperCAmelCase			)
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     UpperCamelCase_						=  torch.Generator(device='cpu'			).manual_seed(0			)
     UpperCamelCase_						=  pipe(_UpperCAmelCase		,		'anime turle'		,		generator=_UpperCAmelCase		,		output_type='np'			)

     UpperCamelCase_						=  output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_						=  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'			)

     UpperCamelCase_						=  load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'			)

     UpperCamelCase_						=  StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'		,		torch_dtype=torch.floataa			)
     pipe.to(_UpperCAmelCase			)
     pipe.set_progress_bar_config(disable=_UpperCAmelCase			)
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     UpperCamelCase_						=  torch.Generator(device='cpu'			).manual_seed(0			)
     UpperCamelCase_						=  pipe(_UpperCAmelCase		,		'anime turle'		,		generator=_UpperCAmelCase		,		output_type='np'			)

     UpperCamelCase_						=  output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'			)

     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     UpperCamelCase_						=  StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'		,		torch_dtype=torch.floataa			)
     UpperCamelCase_						=  pipe.to(_UpperCAmelCase			)
     pipe.set_progress_bar_config(disable=_UpperCAmelCase			)
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     UpperCamelCase_						=  pipe(
         _UpperCAmelCase		,		'anime turtle'		,		num_inference_steps=2		,		output_type='np'		,		)

     UpperCamelCase_						=  torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9













import math
from datetime import datetime, timedelta






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  year % 19
   UpperCamelCase_						=  year % 4
   UpperCamelCase_						=  year % 7
   UpperCamelCase_						=  math.floor(year / 100)
   UpperCamelCase_						=  math.floor((13 + 8 * leap_day_inhibits) / 25)
   UpperCamelCase_						=  leap_day_inhibits / 4
   UpperCamelCase_						=  (
       15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
   ) % 30
   UpperCamelCase_						=  (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

   # days to be added to March 21
   UpperCamelCase_						=  (19 * metonic_cycle + secular_moon_shift) % 30

   # PHM -> Paschal Full Moon
   UpperCamelCase_						=  (
       2 * julian_leap_year
       + 4 * non_leap_year
       + 6 * days_to_add
       + century_starting_point
   ) % 7

   if days_to_add == 29 and days_from_phm_to_sunday == 6:
      return datetime(__lowercase     ,   4     ,   19)
   elif days_to_add == 28 and days_from_phm_to_sunday == 6:
      return datetime(__lowercase     ,   4     ,   18)
   else:
      return datetime(__lowercase     ,   3     ,   22) + timedelta(
          days=int(days_to_add + days_from_phm_to_sunday))


if __name__ == "__main__":
 for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
  snake_case__   :						Dict													=   """will be""" if year > datetime.now().year else """was"""
  print(f'Easter in {year} {tense} {gauss_easter(year)}')





