





'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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 					__snake_case  (					__SCREAMING_SNAKE_CASE		,				unittest.TestCase							):






   '''simple docstring'''





   lowerCamelCase__    							=      KandinskyVaaControlnetPipeline
   lowerCamelCase__    							=      ['''image_embeds''', '''negative_image_embeds''', '''hint''']
   lowerCamelCase__    							=      ['''image_embeds''', '''negative_image_embeds''', '''hint''']
   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 		__UpperCamelCase						(							self       ):
    return 3_2

   @property
   def 		__UpperCamelCase						(							self       ):
    return 3_2

   @property
   def 		__UpperCamelCase						(							self       ):
    return self.time_input_dim

   @property
   def 		__UpperCamelCase						(							self       ):
    return self.time_input_dim * 4

   @property
   def 		__UpperCamelCase						(							self       ):
    return 1_0_0

   @property
   def 		__UpperCamelCase						(							self       ):
    torch.manual_seed(0       )

    snake_case__     :					List[Any]    =  {
        """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,
    }

    snake_case__     :					int    =  UNetaDConditionModel(**__SCREAMING_SNAKE_CASE       )
    return model

   @property
   def 		__UpperCamelCase						(							self       ):
    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 		__UpperCamelCase						(							self       ):
    torch.manual_seed(0       )
    snake_case__     :					Any    =  VQModel(**self.dummy_movq_kwargs       )
    return model

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					str    =  self.dummy_unet
    snake_case__     :					Optional[int]    =  self.dummy_movq

    snake_case__     :					Dict    =  DDIMScheduler(
        num_train_timesteps=1_0_0_0	,		beta_schedule="""linear"""	,		beta_start=0.0_0085	,		beta_end=0.012	,		clip_sample=__SCREAMING_SNAKE_CASE	,		set_alpha_to_one=__SCREAMING_SNAKE_CASE	,		steps_offset=1	,		prediction_type="""epsilon"""	,		thresholding=__SCREAMING_SNAKE_CASE	,		)

    snake_case__     :					Optional[Any]    =  {
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }
    return components

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE=0       ):
    snake_case__     :					Dict    =  floats_tensor((1, self.text_embedder_hidden_size)	,		rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					List[Any]    =  floats_tensor((1, self.text_embedder_hidden_size)	,		rng=random.Random(seed + 1       )       ).to(
        __SCREAMING_SNAKE_CASE       )

    # create hint
    snake_case__     :					Optional[int]    =  floats_tensor((1, 3, 6_4, 6_4)	,		rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )

    if str(__SCREAMING_SNAKE_CASE       ).startswith("""mps"""       ):
     snake_case__     :					Optional[int]    =  torch.manual_seed(__SCREAMING_SNAKE_CASE       )
    else:
     snake_case__     :					List[str]    =  torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  {
        """image_embeds""": image_embeds,
        """negative_image_embeds""": negative_image_embeds,
        """hint""": hint,
        """generator""": generator,
        """height""": 6_4,
        """width""": 6_4,
        """guidance_scale""": 4.0,
        """num_inference_steps""": 2,
        """output_type""": """np""",
    }
    return inputs


   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  """cpu"""

    snake_case__     :					Any    =  self.get_dummy_components()

    snake_case__     :					Optional[Any]    =  self.pipeline_class(**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Union[str, Any]    =  pipe.to(__SCREAMING_SNAKE_CASE       )

    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[str]    =  pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )       )
    snake_case__     :					int    =  output.images

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

    snake_case__     :					Union[str, Any]    =  image[0, -3:, -3:, -1]
    snake_case__     :					Optional[Any]    =  image_from_tuple[0, -3:, -3:, -1]

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

    snake_case__     :					Tuple    =  np.array(
        [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]       )

    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							):






   '''simple docstring'''





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


   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""       )

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

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

    snake_case__     :					int    =  KandinskyVaaControlnetPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,		torch_dtype=torch.floataa       )
    snake_case__     :					List[Any]    =  pipeline.to(__SCREAMING_SNAKE_CASE       )
    pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[int]    =  """A robot, 4k photo"""

    snake_case__     :					int    =  torch.Generator(device="""cuda"""       ).manual_seed(0       )
    snake_case__  ,							snake_case__     :					str    =  pipe_prior(
        __SCREAMING_SNAKE_CASE	,		generator=__SCREAMING_SNAKE_CASE	,		num_inference_steps=5	,		negative_prompt=""""""	,		).to_tuple()

    snake_case__     :					Any    =  torch.Generator(device="""cuda"""       ).manual_seed(0       )
    snake_case__     :					List[Any]    =  pipeline(
        image_embeds=__SCREAMING_SNAKE_CASE	,		negative_image_embeds=__SCREAMING_SNAKE_CASE	,		hint=__SCREAMING_SNAKE_CASE	,		generator=__SCREAMING_SNAKE_CASE	,		num_inference_steps=1_0_0	,		output_type="""np"""	,		)

    snake_case__     :					List[str]    =  output.images[0]

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

    assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )












from __future__ import annotations




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :str 							=    order

            # a_{0} ... a_{k}
            __snake_case      :int 							=    [1.0] + [0.0] * order
            # b_{0} ... b_{k}
            __snake_case      :Any 							=    [1.0] + [0.0] * order

            # x[n-1] ... x[n-k]
            __snake_case      :List[Any] 							=    [0.0] * self.order
            # y[n-1] ... y[n-k]
            __snake_case      :int 							=    [0.0] * self.order



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            if len(a__     ) < self.order:
                 __snake_case      :Optional[int] 							=    [1.0, *a_coeffs]

            if len(a__     ) != self.order + 1:
                 __snake_case      :Dict 							=    (
                     F'''Expected a_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            if len(a__     ) != self.order + 1:
                 __snake_case      :Optional[Any] 							=    (
                     F'''Expected b_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            __snake_case      :str 							=    a_coeffs
            __snake_case      :str 							=    b_coeffs



       def        __lowercase    (				self      ,    a__     )			->   float:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    0.0

            # Start at index 1 and do index 0 at the end.
            for i in range(1      ,    self.order + 1     ):
                 result += (
                     self.b_coeffs[i] * self.input_history[i - 1]
                     - self.a_coeffs[i] * self.output_history[i - 1]
                 )

            __snake_case      :Dict 							=    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

            __snake_case      :Optional[int] 							=    self.input_history[:-1]
            __snake_case      :Optional[Any] 							=    self.output_history[:-1]

            __snake_case      :Union[str, Any] 							=    sample
            __snake_case      :Optional[Any] 							=    result

            return result


