


"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       int									=    KandinskyVaaImgaImgPipeline
   _a   :       Tuple									=    ['''image_embeds''', '''negative_image_embeds''', '''image''']
   _a   :       Optional[int]									=    [
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
   ]
   _a   :       Any									=    [
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   _a   :       str									=    False


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    return 32


   @property
   def 					UpperCAmelCase__(						self						)      ->							str:
    return 32


   @property
   def 					UpperCAmelCase__(						self						)      ->							str:
    return self.time_input_dim


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return self.time_input_dim * 4


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    return 100


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    torch.manual_seed(0						)

    lowercase__       :       List[str]				=						{
        """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,
    }

    lowercase__       :       Tuple				=						UNetaDConditionModel(**lowerCamelCase__						)
    return model


   @property
   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    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 					UpperCAmelCase__(						self						)      ->							str:
    torch.manual_seed(0						)
    lowercase__       :       Optional[int]				=						VQModel(**self.dummy_movq_kwargs						)
    return model


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Any				=						self.dummy_unet
    lowercase__       :       Tuple				=						self.dummy_movq

    lowercase__       :       List[str]				=						{
        """num_train_timesteps""": 1000,
        """beta_schedule""": """linear""",
        """beta_start""": 0.0_0085,
        """beta_end""": 0.012,
        """clip_sample""": False,
        """set_alpha_to_one""": False,
        """steps_offset""": 0,
        """prediction_type""": """epsilon""",
        """thresholding""": False,
    }

    lowercase__       :       List[Any]				=						DDIMScheduler(**lowerCamelCase__						)

    lowercase__       :       str				=						{
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }

    return components


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=0						)      ->							Any:
    lowercase__       :       List[Any]				=						floats_tensor((1, self.text_embedder_hidden_size) ,     rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
    lowercase__       :       Union[str, Any]				=						floats_tensor((1, self.text_embedder_hidden_size) ,     rng=random.Random(seed + 1						)						).to(
        lowerCamelCase__						)
    # create init_image
    lowercase__       :       Dict				=						floats_tensor((1, 3, 64, 64) ,     rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)
    lowercase__       :       int				=						image.cpu().permute(0 ,     2 ,     3 ,     1						)[0]
    lowercase__       :       List[str]				=						Image.fromarray(np.uinta(lowerCamelCase__						)						).convert("""RGB"""						).resize((256, 256)						)

    if str(lowerCamelCase__						).startswith("""mps"""						):
     lowercase__       :       Optional[int]				=						torch.manual_seed(lowerCamelCase__						)
    else:
     lowercase__       :       str				=						torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)
    lowercase__       :       Optional[int]				=						{
        """image""": init_image,
        """image_embeds""": image_embeds,
        """negative_image_embeds""": negative_image_embeds,
        """generator""": generator,
        """height""": 64,
        """width""": 64,
        """num_inference_steps""": 10,
        """guidance_scale""": 7.0,
        """strength""": 0.2,
        """output_type""": """np""",
    }
    return inputs







   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Union[str, Any]				=						"""cpu"""

    lowercase__       :       Dict				=						self.get_dummy_components()

    lowercase__       :       Any				=						self.pipeline_class(**lowerCamelCase__						)
    lowercase__       :       int				=						pipe.to(lowerCamelCase__						)

    pipe.set_progress_bar_config(disable=lowerCamelCase__						)

    lowercase__       :       str				=						pipe(**self.get_dummy_inputs(lowerCamelCase__						)						)
    lowercase__       :       Dict				=						output.images

    lowercase__       :       str				=						pipe(
        **self.get_dummy_inputs(lowerCamelCase__						) ,     return_dict=lowerCamelCase__ ,     )[0]

    lowercase__       :       Tuple				=						image[0, -3:, -3:, -1]
    lowercase__       :       Dict				=						image_from_tuple[0, -3:, -3:, -1]

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

    lowercase__       :       Tuple				=						np.array(
        [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]						)
    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 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Dict:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()







   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[Any]				=						load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/kandinskyv22_img2img_frog.npy"""						)

    lowercase__       :       Dict				=						load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
    lowercase__       :       List[str]				=						"""A red cartoon frog, 4k"""

    lowercase__       :       Union[str, Any]				=						KandinskyVaaPriorPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-prior""" ,     torch_dtype=torch.floataa						)
    pipe_prior.to(lowerCamelCase__						)

    lowercase__       :       List[Any]				=						KandinskyVaaImgaImgPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-decoder""" ,     torch_dtype=torch.floataa						)
    lowercase__       :       Any				=						pipeline.to(lowerCamelCase__						)

    pipeline.set_progress_bar_config(disable=lowerCamelCase__						)

    lowercase__       :       str				=						torch.Generator(device="""cpu"""						).manual_seed(0						)
    lowercase__	,       lowercase__       :       List[str]				=						pipe_prior(
        lowerCamelCase__ ,     generator=lowerCamelCase__ ,     num_inference_steps=5 ,     negative_prompt="""""" ,     ).to_tuple()

    lowercase__       :       Optional[Any]				=						pipeline(
        image=lowerCamelCase__ ,     image_embeds=lowerCamelCase__ ,     negative_image_embeds=lowerCamelCase__ ,     generator=lowerCamelCase__ ,     num_inference_steps=100 ,     height=768 ,     width=768 ,     strength=0.2 ,     output_type="""np""" ,     )

    lowercase__       :       Dict				=						output.images[0]

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

    assert_mean_pixel_difference(lowerCamelCase__ ,     lowerCamelCase__						)



"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 assert column_title.isupper()
 lowercase__       :       int				=						0
 lowercase__       :       Optional[int]				=						len(lowerCamelCase__  ) - 1
 lowercase__       :       int				=						0

 while index >= 0:
  lowercase__       :       List[str]				=						(ord(column_title[index]  ) - 64) * pow(26       ,		lowerCamelCase__  )
  answer += value
  power += 1
  index -= 1

 return answer


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

   testmod()