import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class __snake_case       (		_snake_case     ,	unittest.TestCase	):
   lowerCAmelCase_														=							KandinskyVaaPipeline
   lowerCAmelCase_														=							[
       """image_embeds""",
       """negative_image_embeds""",
   ]
   lowerCAmelCase_														=							["""image_embeds""", """negative_image_embeds"""]
   lowerCAmelCase_														=							[
       """generator""",
       """height""",
       """width""",
       """latents""",
       """guidance_scale""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   lowerCAmelCase_														=							False




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     return 32




   @property
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     return 32




   @property
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     return self.time_input_dim




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     return self.time_input_dim * 4




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     return 1_00




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__    							=   {
         "in_channels": 4,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 8,
         "addition_embed_type": "image",
         "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,
         "encoder_hid_dim": self.text_embedder_hidden_size,
         "encoder_hid_dim_type": "image_proj",
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": None,
     }

     SCREAMING_SNAKE_CASE__    							=   UNetaDConditionModel(**snake_case_   )
     return model




   @property
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__    							=   VQModel(**self.dummy_movq_kwargs   )
     return model




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.dummy_unet
     SCREAMING_SNAKE_CASE__    							=   self.dummy_movq

     SCREAMING_SNAKE_CASE__    							=   DDIMScheduler(
         num_train_timesteps=10_00 ,			beta_schedule="""linear""" ,			beta_start=0.0_00_85 ,			beta_end=0.0_12 ,			clip_sample=snake_case_ ,			set_alpha_to_one=snake_case_ ,			steps_offset=1 ,			prediction_type="""epsilon""" ,			thresholding=snake_case_ ,			)

     SCREAMING_SNAKE_CASE__    							=   {
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }
     return components




   def 			__a		(					self    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Any=0   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, self.text_embedder_hidden_size) ,			rng=random.Random(snake_case_   )   ).to(snake_case_   )
     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, self.text_embedder_hidden_size) ,			rng=random.Random(seed + 1   )   ).to(
         snake_case_   )
     if str(snake_case_   ).startswith("""mps"""   ):
       SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(snake_case_   )
     else:
       SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )
     SCREAMING_SNAKE_CASE__    							=   {
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "guidance_scale": 4.0,
         "num_inference_steps": 2,
         "output_type": "np",
     }
     return inputs




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   "cpu"

     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=snake_case_   )

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

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

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

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__    							=   np.array(
         [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]   )

     assert (
         np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
     ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
     ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







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




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""   )

     SCREAMING_SNAKE_CASE__    							=   KandinskyVaaPriorPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-prior""" ,			torch_dtype=torch.floataa   )
     pipe_prior.to(snake_case_   )

     SCREAMING_SNAKE_CASE__    							=   KandinskyVaaPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder""" ,			torch_dtype=torch.floataa   )
     SCREAMING_SNAKE_CASE__    							=   pipeline.to(snake_case_   )
     pipeline.set_progress_bar_config(disable=snake_case_   )

     SCREAMING_SNAKE_CASE__    							=   "red cat, 4k photo"

     SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cuda"""   ).manual_seed(0   )
     SCREAMING_SNAKE_CASE__    							=   pipe_prior(
         snake_case_ ,			generator=snake_case_ ,			num_inference_steps=5 ,			negative_prompt="""""" ,			).to_tuple()

     SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cuda"""   ).manual_seed(0   )
     SCREAMING_SNAKE_CASE__    							=   pipeline(
         image_embeds=snake_case_ ,			negative_image_embeds=snake_case_ ,			generator=snake_case_ ,			num_inference_steps=1_00 ,			output_type="""np""" ,			)

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(snake_case_ ,			snake_case_   )

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


if __name__ == "__main__":
   from doctest import testmod

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
