

'''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 						A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						KandinskyVaaControlnetPipeline
    lowercase		   =						["image_embeds", "negative_image_embeds", "hint"]
    lowercase		   =						["image_embeds", "negative_image_embeds", "hint"]
    lowercase		   =						[
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    lowercase		   =						False



    @property
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           return 32



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





           '''simple docstring'''
           return 32



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





           '''simple docstring'''
           return self.time_input_dim



    @property
    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           return self.time_input_dim * 4



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           return 100



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





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

           A_		   = {
               """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,
           }

           A_		   = UNetaDConditionModel(**UpperCamelCase__    )
           return model



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





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = VQModel(**self.dummy_movq_kwargs    )
           return model



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





           '''simple docstring'''
           A_		   = self.dummy_unet
           A_		   = self.dummy_movq

           A_		   = DDIMScheduler(
               num_train_timesteps=1000						,			beta_schedule="""linear"""						,			beta_start=0.00085						,			beta_end=0.012						,			clip_sample=UpperCamelCase__						,			set_alpha_to_one=UpperCamelCase__						,			steps_offset=1						,			prediction_type="""epsilon"""						,			thresholding=UpperCamelCase__						,			)

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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__=0    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(UpperCamelCase__    )    ).to(UpperCamelCase__    )
           A_		   = floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(seed + 1    )    ).to(
               UpperCamelCase__    )

           # create hint
           A_		   = floats_tensor((1, 3, 64, 64)						,			rng=random.Random(UpperCamelCase__    )    ).to(UpperCamelCase__    )

           if str(UpperCamelCase__    ).startswith("""mps"""    ):
                  A_		   = torch.manual_seed(UpperCamelCase__    )
           else:
                  A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(UpperCamelCase__    )
           A_		   = {
               """image_embeds""": image_embeds,
               """negative_image_embeds""": negative_image_embeds,
               """hint""": hint,
               """generator""": generator,
               """height""": 64,
               """width""": 64,
               """guidance_scale""": 4.0,
               """num_inference_steps""": 2,
               """output_type""": """np""",
           }
           return inputs







    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = """cpu"""

           A_		   = self.get_dummy_components()

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

           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

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

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

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

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

           A_		   = np.array(
               [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]    )

           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     ):



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""    )

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

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

           A_		   = KandinskyVaaControlnetPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-controlnet-depth"""						,			torch_dtype=torch.floataa    )
           A_		   = pipeline.to(UpperCamelCase__    )
           pipeline.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A robot, 4k photo"""

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

           A_		   = torch.Generator(device="""cuda"""    ).manual_seed(0    )
           A_		   = pipeline(
               image_embeds=UpperCamelCase__						,			negative_image_embeds=UpperCamelCase__						,			hint=UpperCamelCase__						,			generator=UpperCamelCase__						,			num_inference_steps=100						,			output_type="""np"""						,			)

           A_		   = output.images[0]

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

           assert_mean_pixel_difference(UpperCamelCase__						,			UpperCamelCase__    )



'''simple docstring'''







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    )     ->     list[float]:
       A_   ,     A_		   = coefficient_matrix.shape
       A_   ,     A_		   = constant_matrix.shape

       if rowsa != colsa:
              A_		   = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if colsa != 1:
              A_		   = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if rowsa != rowsa:
              A_		   = (
                  """Coefficient and constant matrices dimensions must be nxn and nx1 but """
                  F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
              )
              raise ValueError(UpperCAmelCase__			)

       if len(UpperCAmelCase__			) != rowsa:
              A_		   = (
                  """Number of initial values must be equal to number of rows in coefficient """
                  F'''matrix but received {len(UpperCAmelCase__			)} and {rowsa}'''
              )
              raise ValueError(UpperCAmelCase__			)

       if iterations <= 0:
              raise ValueError("""Iterations must be at least 1"""			)

       A_		   = np.concatenate(
           (coefficient_matrix, constant_matrix),    axis=1			)

       A_   ,     A_		   = table.shape

       strictly_diagonally_dominant(UpperCAmelCase__			)

       # Iterates the whole matrix for given number of times
       for _ in range(UpperCAmelCase__			):
              A_		   = []
              for row in range(UpperCAmelCase__			):
                     A_		   = 0
                     for col in range(UpperCAmelCase__			):
                            if col == row:
                                   A_		   = table[row][col]
                            elif col == cols - 1:
                                   A_		   = table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     A_		   = (temp + val) / denom
                     new_val.append(UpperCAmelCase__			)
              A_		   = new_val

       return [float(UpperCAmelCase__			) for i in new_val]
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     bool:
       A_   ,     A_		   = table.shape

       A_		   = True

       for i in range(0,    UpperCAmelCase__			):
              A_		   = 0
              for j in range(0,    cols - 1			):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""			)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
      import doctest

      doctest.testmod()
