




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   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						KandinskyVaaControlnetImgaImgPipeline
 UpperCamelCase						       =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 UpperCamelCase						       =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 UpperCamelCase						       =						[
     '''generator''',
     '''height''',
     '''width''',
     '''strength''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 UpperCamelCase						       =						False

 @property
 def     a__			(    self :  Tuple  )       ->     Union[str, Any]:

       """simple docstring"""






       return 32

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

       """simple docstring"""






       return 32

 @property
 def     a__			(    self :  Any  )       ->     Tuple:

       """simple docstring"""






       return self.time_input_dim

 @property
 def     a__			(    self :  Union[str, Any]  )       ->     Dict:

       """simple docstring"""






       return self.time_input_dim * 4

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

       """simple docstring"""






       return 100

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

       """simple docstring"""






       torch.manual_seed(0  )

       lowerCamelCase_							=   {
           '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,
       }

       lowerCamelCase_							=   UNetaDConditionModel(**A_  )
       return model

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

       """simple docstring"""






       return {
           "block_out_channels": [32, 32, 64, 64],
           "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": 12,
           "out_channels": 3,
           "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }

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

       """simple docstring"""






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

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

       """simple docstring"""






       lowerCamelCase_							=   self.dummy_unet
       lowerCamelCase_							=   self.dummy_movq

       lowerCamelCase_							=   {
           'num_train_timesteps': 1000,
           'beta_schedule': 'linear',
           'beta_start': 0.00085,
           'beta_end': 0.012,
           'clip_sample': False,
           'set_alpha_to_one': False,
           'steps_offset': 0,
           'prediction_type': 'epsilon',
           'thresholding': False,
       }

       lowerCamelCase_							=   DDIMScheduler(**A_  )

       lowerCamelCase_							=   {
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components

 def     a__			(    self :  int						,							A_ :  Optional[Any]						,							A_ :  Tuple=0  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   floats_tensor((1, self.text_embedder_hidden_size)						,							rng=random.Random(A_  )  ).to(A_  )
       lowerCamelCase_							=   floats_tensor((1, self.text_embedder_hidden_size)						,							rng=random.Random(seed + 1  )  ).to(
           A_  )
       # create init_image
       lowerCamelCase_							=   floats_tensor((1, 3, 64, 64)						,							rng=random.Random(A_  )  ).to(A_  )
       lowerCamelCase_							=   image.cpu().permute(0						,							2						,							3						,							1  )[0]
       lowerCamelCase_							=   Image.fromarray(np.uinta(A_  )  ).convert('RGB'  ).resize((256, 256)  )
       # create hint
       lowerCamelCase_							=   floats_tensor((1, 3, 64, 64)						,							rng=random.Random(A_  )  ).to(A_  )

       if str(A_  ).startswith('mps'  ):
             lowerCamelCase_							=   torch.manual_seed(A_  )
       else:
             lowerCamelCase_							=   torch.Generator(device=A_  ).manual_seed(A_  )
       lowerCamelCase_							=   {
           'image': init_image,
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'hint': hint,
           'generator': generator,
           'height': 64,
           'width': 64,
           'num_inference_steps': 10,
           'guidance_scale': 7.0,
           'strength': 0.2,
           'output_type': 'np',
       }
       return inputs





 def     a__			(    self :  int  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   'cpu'

       lowerCamelCase_							=   self.get_dummy_components()

       lowerCamelCase_							=   self.pipeline_class(**A_  )
       lowerCamelCase_							=   pipe.to(A_  )

       pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_							=   pipe(**self.get_dummy_inputs(A_  )  )
       lowerCamelCase_							=   output.images

       lowerCamelCase_							=   pipe(
           **self.get_dummy_inputs(A_  )						,							return_dict=A_						,							)[0]

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

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

       lowerCamelCase_							=   np.array(
           [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]  )
       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   A(						unittest.TestCase       ):



 '''simple docstring'''


 def     a__			(    self :  Tuple  )       ->     List[str]:

       """simple docstring"""






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





 def     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'  )

       lowerCamelCase_							=   load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
       lowerCamelCase_							=   init_image.resize((512, 512)  )

       lowerCamelCase_							=   load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/hint_image_cat.png'  )
       lowerCamelCase_							=   torch.from_numpy(np.array(A_  )  ).float() / 255.0
       lowerCamelCase_							=   hint.permute(2						,							0						,							1  ).unsqueeze(0  )

       lowerCamelCase_							=   'A robot, 4k photo'

       lowerCamelCase_							=   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-prior'						,							torch_dtype=torch.floataa  )
       pipe_prior.to(A_  )

       lowerCamelCase_							=   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-controlnet-depth'						,							torch_dtype=torch.floataa  )
       lowerCamelCase_							=   pipeline.to(A_  )

       pipeline.set_progress_bar_config(disable=A_  )

       lowerCamelCase_							=   torch.Generator(device='cpu'  ).manual_seed(0  )

       lowerCamelCase_					,    lowerCamelCase_							=   pipe_prior(
           A_						,							image=A_						,							strength=0.85						,							generator=A_						,							negative_prompt=''						,							).to_tuple()

       lowerCamelCase_							=   pipeline(
           image=A_						,							image_embeds=A_						,							negative_image_embeds=A_						,							hint=A_						,							generator=A_						,							num_inference_steps=100						,							height=512						,							width=512						,							strength=0.5						,							output_type='np'						,							)

       lowerCamelCase_							=   output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(A_						,							A_  )









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	Dict     ,							lowercase     :	List[str]     ,							lowercase     :	Dict     ,							lowercase     :	Dict     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      if index == r:
            for j in range(lowercase					):
                  print(data[j]     ,							end=' '					)
            print(' '					)
            return
      #  When no more elements are there to put in data[]
      if i >= n:
            return
      # current is included, put next at next location
      lowerCamelCase_							=   arr[i]
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							index + 1     ,							lowercase     ,							i + 1					)
      # current is excluded, replace it with
      # next (Note that i+1 is passed, but
      # index is not changed)
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							i + 1					)
      # The main function that prints all combinations
      # of size r in arr[] of size n. This function
      # mainly uses combinationUtil()

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	Any     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      lowerCamelCase_							=   [0] * r
      # Print all combination using temporary array 'data[]'
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							0     ,							lowercase     ,							0					)


if __name__ == "__main__":
 # Driver code to check the function above
 lowerCamelCase    :		int			   =       [10, 20, 30, 40, 50]
 print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu



