
"""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  lowerCAmelCase_ (					__snake_case						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			Dict					=			KandinskyVaaControlnetPipeline
						_lowerCamelCase:			List[Any]					=			["""image_embeds""", """negative_image_embeds""", """hint"""]
						_lowerCamelCase:			Union[str, Any]					=			["""image_embeds""", """negative_image_embeds""", """hint"""]
						_lowerCamelCase:			Dict					=			[
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_lowerCamelCase:			Dict					=			False


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


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Any:
							return 32


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      List[str]:
							return self.time_input_dim


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Optional[int]:
							return self.time_input_dim * 4


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Any:
							return 100


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      str:
							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(**A_					)
							return model


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Optional[int]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Tuple:
							torch.manual_seed(0					)
							A        =      VQModel(**self.dummy_movq_kwargs					)
							return model


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      List[str]:
							A        =      self.dummy_unet
							A        =      self.dummy_movq

							A        =      DDIMScheduler(
							    num_train_timesteps=1000     ,beta_schedule='linear'     ,beta_start=0.0_00_85     ,beta_end=0.0_12     ,clip_sample=A_     ,set_alpha_to_one=A_     ,steps_offset=1     ,prediction_type='epsilon'     ,thresholding=A_     ,)

							A        =      {
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}
							return components


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					str     ,A_   :					Dict=0					)   ->      List[Any]:
							A        =      floats_tensor((1, self.text_embedder_hidden_size)     ,rng=random.Random(A_					)					).to(A_					)
							A        =      floats_tensor((1, self.text_embedder_hidden_size)     ,rng=random.Random(seed + 1					)					).to(
							    A_					)

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

							if str(A_					).startswith('mps'					):
								A        =      torch.manual_seed(A_					)
							else:
								A        =      torch.Generator(device=A_					).manual_seed(A_					)
							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   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Optional[int]:
							A        =      'cpu'

							A        =      self.get_dummy_components()

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

							pipe.set_progress_bar_config(disable=A_					)

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

							A        =      pipe(
							    **self.get_dummy_inputs(A_					)     ,return_dict=A_     ,)[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(
							    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]					)

							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			):



						'''simple docstring'''


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




						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      List[Any]:
							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(A_					)					).float() / 2_55.0
							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(A_					)

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

							A        =      'A robot, 4k photo'

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

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

							A        =      output.images[0]

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

							assert_mean_pixel_difference(A_     ,A_					)


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

snake_case_			  = TypeVar('''T''')
snake_case_			  = TypeVar('''U''')


class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):





    def __init__(      self						,   a						,   a):
         lowercase__    :   List[Any]      =       key
         lowercase__    :   List[Any]      =       val
         lowercase__    :   DoubleLinkedListNode[T, U] | None      =       None
         lowercase__    :   DoubleLinkedListNode[T, U] | None      =       None





    def __repr__(      self):
         return (
             f"""Node: key: {self.key}, val: {self.val}, """
             f"""has next: {bool(self.next)}, has prev: {bool(self.prev)}"""
         )



class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):





    def __init__(      self):
         lowercase__    :   DoubleLinkedListNode[T, U]      =       DoubleLinkedListNode(a						,   a)
         lowercase__    :   DoubleLinkedListNode[T, U]      =       DoubleLinkedListNode(a						,   a)
         lowercase__      , lowercase__    :   Union[str, Any]      =       self.rear, self.head





    def __repr__(      self):
         lowercase__    :   Any      =       ['DoubleLinkedList']
         lowercase__    :   List[str]      =       self.head
         while node.next is not None:
              rep.append(str(a))
              lowercase__    :   Tuple      =       node.next
         rep.append(str(self.rear))
         return ",\n    ".join(a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Optional[Any]      =       self.rear.prev

         # All nodes other than self.head are guaranteed to have non-None previous
         assert previous is not None

         lowercase__    :   Dict      =       node
         lowercase__    :   int      =       previous
         lowercase__    :   Union[str, Any]      =       node
         lowercase__    :   Optional[int]      =       self.rear





    def       snake_case_				(      self						,   a):

         if node.prev is None or node.next is None:
              return None

         lowercase__    :   Union[str, Any]      =       node.next
         lowercase__    :   Tuple      =       node.prev
         lowercase__    :   Union[str, Any]      =       None
         lowercase__    :   List[Any]      =       None
         return node



class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):
    __lowerCamelCase						:       dict[Callable[[T], U], LRUCache[T, U]]														=     {}





    def __init__(      self						,   a):
         lowercase__    :   DoubleLinkedList[T, U]      =       DoubleLinkedList()
         lowercase__    :   Optional[Any]      =       capacity
         lowercase__    :   Union[str, Any]      =       0
         lowercase__    :   Tuple      =       0
         lowercase__    :   int      =       0
         lowercase__    :   dict[T, DoubleLinkedListNode[T, U]]      =       {}





    def __repr__(      self):
         return (
             f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
             f"""capacity={self.capacity}, current size={self.num_keys})"""
         )





    def __contains__(      self						,   a):
         return key in self.cache





    def       snake_case_				(      self						,   a):
         # Note: pythonic interface would throw KeyError rather than return None

         if key in self.cache:
              self.hits += 1
              lowercase__    :   DoubleLinkedListNode[T, U]      =       self.cache[key]
              lowercase__    :   str      =       self.list.remove(self.cache[key])
              assert node == value_node

              # node is guaranteed not None because it is in self.cache
              assert node is not None
              self.list.add(a)
              return node.val
         self.miss += 1
         return None





    def       snake_case_				(      self						,   a						,   a):

         if key not in self.cache:
              if self.num_keys >= self.capacity:
                   # delete first node (oldest) when over capacity
                   lowercase__    :   Optional[int]      =       self.list.head.next

                   # guaranteed to have a non-None first node when num_keys > 0
                   # explain to type checker via assertions
                   assert first_node is not None
                   assert first_node.key is not None
                   assert (
                       self.list.remove(a) is not None
                   )  # node guaranteed to be in list assert node.key is not None

                   del self.cache[first_node.key]
                   self.num_keys -= 1
              lowercase__    :   Optional[Any]      =       DoubleLinkedListNode(a						,   a)
              self.list.add(self.cache[key])
              self.num_keys += 1

         else:
              # bump node to the end of the list, update value
              lowercase__    :   Any      =       self.list.remove(self.cache[key])
              assert node is not None  # node guaranteed to be in list
              lowercase__    :   Union[str, Any]      =       value
              self.list.add(a)





    @classmethod
    def       snake_case_				(      cls						,   a = 128):

         def cache_decorator_inner(a) -> Callable[..., U]:
              def cache_decorator_wrapper(*a) -> U:
                   if func not in cls.decorator_function_to_instance_map:
                        lowercase__    :   Dict      =       LRUCache(a)

                   lowercase__    :   str      =       cls.decorator_function_to_instance_map[func].get(args[0])
                   if result is None:
                        lowercase__    :   str      =       func(*a)
                        cls.decorator_function_to_instance_map[func].put(args[0]						,   a)
                   return result

              def cache_info() -> LRUCache[T, U]:
                   return cls.decorator_function_to_instance_map[func]

              setattr(a						,   'cache_info'						,   a)  # noqa: B010

              return cache_decorator_wrapper

         return cache_decorator_inner


if __name__ == "__main__":
       import doctest

       doctest.testmod()

