






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 			lowerCAmelCase__			(    __magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =KandinskyVaaControlnetImgaImgPipeline
   SCREAMING_SNAKE_CASE_    =['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   SCREAMING_SNAKE_CASE_    =['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   SCREAMING_SNAKE_CASE_    =[
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   SCREAMING_SNAKE_CASE_    =False
   @property
   def       __a							(		self      :	int							):


        '''simple docstring'''
        return 3_2
   @property
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        return 3_2
   @property
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        return self.time_input_dim
   @property
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        return self.time_input_dim * 4
   @property
   def       __a							(		self      :	int							):


        '''simple docstring'''
        return 1_0_0
   @property
   def       __a							(		self      :	List[Any]							):


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

        UpperCAmelCase__						:    int							=							{
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "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,
        }

        UpperCAmelCase__						:    Any							=							UNetaDConditionModel(**snake_case__							)
        return model
   @property
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        return {
            "block_out_channels": [3_2, 3_2, 6_4, 6_4],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 1_2,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }
   @property
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        torch.manual_seed(0							)
        UpperCAmelCase__						:    Tuple							=							VQModel(**self.dummy_movq_kwargs							)
        return model
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.dummy_unet
        UpperCAmelCase__						:    Optional[int]							=							self.dummy_movq

        UpperCAmelCase__						:    List[Any]							=							{
            "num_train_timesteps": 1_0_0_0,
            "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,
        }

        UpperCAmelCase__						:    Tuple							=							DDIMScheduler(**snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							{
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }

        return components
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[str]							,			snake_case__      :	int=0							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							floats_tensor((1, self.text_embedder_hidden_size)							,			rng=random.Random(snake_case__							)							).to(snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							floats_tensor((1, self.text_embedder_hidden_size)							,			rng=random.Random(seed + 1							)							).to(
            snake_case__							)
        # create init_image
        UpperCAmelCase__						:    Tuple							=							floats_tensor((1, 3, 6_4, 6_4)							,			rng=random.Random(snake_case__							)							).to(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							image.cpu().permute(0							,			2							,			3							,			1							)[0]
        UpperCAmelCase__						:    Any							=							Image.fromarray(np.uinta(snake_case__							)							).convert("RGB"							).resize((2_5_6, 2_5_6)							)
        # create hint
        UpperCAmelCase__						:    List[str]							=							floats_tensor((1, 3, 6_4, 6_4)							,			rng=random.Random(snake_case__							)							).to(snake_case__							)

        if str(snake_case__							).startswith("mps"							):
             UpperCAmelCase__						:    Union[str, Any]							=							torch.manual_seed(snake_case__							)
        else:
             UpperCAmelCase__						:    Optional[int]							=							torch.Generator(device=snake_case__							).manual_seed(snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							{
            "image": init_image,
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 6_4,
            "width": 6_4,
            "num_inference_steps": 1_0,
            "guidance_scale": 7.0,
            "strength": 0.2,
            "output_type": "np",
        }
        return inputs




   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							"cpu"

        UpperCAmelCase__						:    List[str]							=							self.get_dummy_components()

        UpperCAmelCase__						:    Any							=							self.pipeline_class(**snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							pipe.to(snake_case__							)

        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							pipe(**self.get_dummy_inputs(snake_case__							)							)
        UpperCAmelCase__						:    int							=							output.images

        UpperCAmelCase__						:    int							=							pipe(
            **self.get_dummy_inputs(snake_case__							)							,			return_dict=snake_case__							,			)[0]

        UpperCAmelCase__						:    Optional[int]							=							image[0, -3:, -3:, -1]
        UpperCAmelCase__						:    str							=							image_from_tuple[0, -3:, -3:, -1]

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

        UpperCAmelCase__						:    List[str]							=							np.array(
            [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]							)
        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 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"							)

        UpperCAmelCase__						:    Union[str, Any]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"							)
        UpperCAmelCase__						:    int							=							init_image.resize((5_1_2, 5_1_2)							)

        UpperCAmelCase__						:    List[str]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/hint_image_cat.png"							)
        UpperCAmelCase__						:    str							=							torch.from_numpy(np.array(snake_case__							)							).float() / 255.0
        UpperCAmelCase__						:    Tuple							=							hint.permute(2							,			0							,			1							).unsqueeze(0							)

        UpperCAmelCase__						:    List[Any]							=							"A robot, 4k photo"

        UpperCAmelCase__						:    Optional[Any]							=							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"							,			torch_dtype=torch.floataa							)
        pipe_prior.to(snake_case__							)

        UpperCAmelCase__						:    Tuple							=							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-controlnet-depth"							,			torch_dtype=torch.floataa							)
        UpperCAmelCase__						:    Tuple							=							pipeline.to(snake_case__							)

        pipeline.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    str							=							torch.Generator(device="cpu"							).manual_seed(0							)

        UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							pipe_prior(
            snake_case__							,			image=snake_case__							,			strength=0.85							,			generator=snake_case__							,			negative_prompt=""							,			).to_tuple()

        UpperCAmelCase__						:    List[Any]							=							pipeline(
            image=snake_case__							,			image_embeds=snake_case__							,			negative_image_embeds=snake_case__							,			hint=snake_case__							,			generator=snake_case__							,			num_inference_steps=1_0_0							,			height=5_1_2							,			width=5_1_2							,			strength=0.5							,			output_type="np"							,			)

        UpperCAmelCase__						:    Optional[Any]							=							output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        assert_mean_pixel_difference(snake_case__							,			snake_case__							)










"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		str:






     '''simple docstring'''



     if not isinstance(snake_case			,					snake_case				):
          raise ValueError("iterations must be defined as integers"				)
     if not isinstance(snake_case			,					snake_case				) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"				)
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

     UpperCAmelCase__						:    str							=							""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(snake_case				)

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()


