



'''simple docstring'''






from __future__ import annotations

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

lowerCAmelCase:      List[Any]		 =		TypeVar('T')
lowerCAmelCase:      Dict		 =		TypeVar('U')

class 							a__(      Generic[T, U]			):







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







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

class 							a__(      Generic[T, U]			):







    def __init__(   self				:						str			):
      a		:      DoubleLinkedListNode[T, U]       =  DoubleLinkedListNode(__snake_case		,    __snake_case			)
      a		:      DoubleLinkedListNode[T, U]       =  DoubleLinkedListNode(__snake_case		,    __snake_case			)
      a     ,			a		:      Optional[int]       =  self.rear, self.head







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







    def     lowercase_ (   self				:						Any		,    __snake_case				:						DoubleLinkedListNode[T, U]			):
      a		:      Any       =  self.rear.prev

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

      a		:      Any       =  node
      a		:      Union[str, Any]       =  previous
      a		:      Dict       =  node
      a		:      List[Any]       =  self.rear







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						DoubleLinkedListNode[T, U]			):

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

      a		:      List[str]       =  node.next
      a		:      Union[str, Any]       =  node.prev
      a		:      Dict       =  None
      a		:      int       =  None
      return node

class 							a__(      Generic[T, U]			):
    lowercase__				=					{}







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







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







    def __contains__(   self				:						Dict		,    __snake_case				:						T			):
      return key in self.cache







    def     lowercase_ (   self				:						str		,    __snake_case				:						T			):
      # Note: pythonic interface would throw KeyError rather than return None

      if key in self.cache:
        self.hits += 1
        a		:      DoubleLinkedListNode[T, U]       =  self.cache[key]
        a		:      int       =  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(__snake_case			)
        return node.val
      self.miss += 1
      return None







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						T		,    __snake_case				:						U			):

      if key not in self.cache:
        if self.num_keys >= self.capacity:
          # delete first node (oldest) when over capacity
          a		:      str       =  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(__snake_case			) 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
        a		:      int       =  DoubleLinkedListNode(__snake_case		,    __snake_case			)
        self.list.add(self.cache[key]			)
        self.num_keys += 1

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







    @classmethod
    def     lowercase_ (   cls				:						Tuple		,    __snake_case				:						int = 1_28			):

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

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

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

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

        return cache_decorator_wrapper

      return cache_decorator_inner


if __name__ == "__main__":
     import doctest

     doctest.testmod()




'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


lowerCAmelCase:      Any		 =		False

@skip_mps
class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					StableDiffusionAttendAndExcitePipeline
    lowercase__				=					False
    lowercase__				=					TEXT_TO_IMAGE_PARAMS
    lowercase__				=					TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}			)
    lowercase__				=					TEXT_TO_IMAGE_IMAGE_PARAMS
    lowercase__				=					TEXT_TO_IMAGE_IMAGE_PARAMS







    @classmethod
    def     lowercase_ (   cls				:						Union[str, Any]			):
      super().setUpClass()
      torch.use_deterministic_algorithms(__snake_case			)







    @classmethod
    def     lowercase_ (   cls				:						Optional[Any]			):
      super().tearDownClass()
      torch.use_deterministic_algorithms(__snake_case			)







    def     lowercase_ (   self				:						List[Any]			):
      torch.manual_seed(0			)
      a		:      List[Any]       =  UNetaDConditionModel(
          block_out_channels=(32, 64)		,    layers_per_block=1		,    sample_size=32		,    in_channels=4		,    out_channels=4		,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,    cross_attention_dim=32		,    attention_head_dim=(2, 4)		,    use_linear_projection=__snake_case		,    )
      a		:      Optional[int]       =  DDIMScheduler(
          beta_start=0.00085		,    beta_end=0.012		,    beta_schedule='scaled_linear'		,    clip_sample=__snake_case		,    set_alpha_to_one=__snake_case		,    )
      torch.manual_seed(0			)
      a		:      str       =  AutoencoderKL(
          block_out_channels=[32, 64]		,    in_channels=3		,    out_channels=3		,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,    latent_channels=4		,    sample_size=1_28		,    )
      torch.manual_seed(0			)
      a		:      str       =  CLIPTextConfig(
          bos_token_id=0		,    eos_token_id=2		,    hidden_size=32		,    intermediate_size=37		,    layer_norm_eps=1e-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=10_00		,    hidden_act='gelu'		,    projection_dim=5_12		,    )
      a		:      Any       =  CLIPTextModel(__snake_case			)
      a		:      Tuple       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

      a		:      List[str]       =  {
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }

      return components







    def     lowercase_ (   self				:						str		,    __snake_case				:						Tuple		,    __snake_case				:						Optional[int]=0			):
      if str(__snake_case			).startswith('mps'			):
        a		:      Any       =  torch.manual_seed(__snake_case			)
      else:
        a		:      Any       =  torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
      a		:      int       =  {
          'prompt': 'a cat and a frog',
          'token_indices': [2, 5],
          'generator': generator,
          'num_inference_steps': 1,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
          'max_iter_to_alter': 2,
          'thresholds': {0: 0.7},
      }
      return inputs







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Union[str, Any]       =  'cpu'

      a		:      Any       =  self.get_dummy_components()
      a		:      List[str]       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Any       =  self.get_dummy_inputs(__snake_case			)
      a		:      int       =  pipe(**__snake_case			).images
      a		:      int       =  image[0, -3:, -3:, -1]

      self.assertEqual(image.shape		,    (1, 64, 64, 3)			)
      a		:      str       =  np.array(
          [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]			)
      a		:      int       =  np.abs(image_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(__snake_case		,    1e-3			)







    def     lowercase_ (   self				:						Dict			):
      super().test_cpu_offload_forward_pass(expected_max_diff=5e-4			)







    def     lowercase_ (   self				:						Tuple			):
      # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
      self._test_inference_batch_consistent(batch_sizes=[1, 2]			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      self._test_inference_batch_single_identical(batch_size=2		,    expected_max_diff=7e-4			)







    def     lowercase_ (   self				:						Tuple			):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3			)







    def     lowercase_ (   self				:						str			):
      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4			)







    def     lowercase_ (   self				:						Any			):
      super().test_save_load_local(expected_max_difference=5e-4			)







    def     lowercase_ (   self				:						List[Any]			):
      super().test_save_load_optional_components(expected_max_difference=4e-4			)

@require_torch_gpu
@slow
class 							a__(      unittest.TestCase			):







    @classmethod
    def     lowercase_ (   cls				:						Union[str, Any]			):
      super().setUpClass()
      torch.use_deterministic_algorithms(__snake_case			)







    @classmethod
    def     lowercase_ (   cls				:						Union[str, Any]			):
      super().tearDownClass()
      torch.use_deterministic_algorithms(__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      List[Any]       =  torch.manual_seed(51			)

      a		:      Dict       =  StableDiffusionAttendAndExcitePipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'		,    safety_checker=__snake_case		,    torch_dtype=torch.floataa			)
      pipe.to('cuda'			)

      a		:      Optional[Any]       =  'a painting of an elephant with glasses'
      a		:      Any       =  [5, 7]

      a		:      Tuple       =  pipe(
          prompt=__snake_case		,    token_indices=__snake_case		,    guidance_scale=7.5		,    generator=__snake_case		,    num_inference_steps=5		,    max_iter_to_alter=5		,    output_type='numpy'		,    ).images[0]

      a		:      str       =  load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'			)
      assert np.abs((expected_image - image).max()			) < 5e-1