




'''simple docstring'''







from __future__ import annotations

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

_lowercase       						=			TypeVar('T')
_lowercase       						=			TypeVar('U')
class 			_lowercase						(       Generic[T, U]					):







     def __init__( self    ,   A__    ,   A__    ) ->	str:
      snake_case				=			key
      snake_case				=			val
      snake_case				=			None
      snake_case				=			None







     def __repr__( self    ) ->	List[str]:
      return (
          F"""Node: key: {self.key}, val: {self.val}, """
          F"""has next: {bool(self.next    )}, has prev: {bool(self.prev    )}"""
      )




class 			_lowercase						(       Generic[T, U]					):







     def __init__( self    ) ->	List[Any]:
      snake_case				=			DoubleLinkedListNode(lowercase_    ,   lowercase_    )
      snake_case				=			DoubleLinkedListNode(lowercase_    ,   lowercase_    )
      snake_case				=			self.rear, self.head







     def __repr__( self    ) ->	Tuple:
      snake_case				=			["DoubleLinkedList"]
      snake_case				=			self.head
      while node.next is not None:
       rep.append(str(lowercase_    )    )
       snake_case				=			node.next
      rep.append(str(self.rear    )    )
      return ",\n    ".join(lowercase_    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			self.rear.prev

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

      snake_case				=			node
      snake_case				=			previous
      snake_case				=			node
      snake_case				=			self.rear







     def     UpperCamelCase			( self    ,   A__    ) ->	Tuple:

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

      snake_case				=			node.next
      snake_case				=			node.prev
      snake_case				=			None
      snake_case				=			None
      return node




class 			_lowercase						(       Generic[T, U]					):
     _UpperCAmelCase										=					{}







     def __init__( self    ,   A__    ) ->	List[str]:
      snake_case				=			DoubleLinkedList()
      snake_case				=			capacity
      snake_case				=			0
      snake_case				=			0
      snake_case				=			0
      snake_case				=			{}







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







     def __contains__( self    ,   A__    ) ->	Dict:
      return key in self.cache







     def     UpperCamelCase			( self    ,   A__    ) ->	List[Any]:
      # Note: pythonic interface would throw KeyError rather than return None

      if key in self.cache:
       self.hits += 1
       snake_case				=			self.cache[key]
       snake_case				=			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(lowercase_    )
       return node.val
      self.miss += 1
      return None







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	Tuple:

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

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







     @classmethod
     def     UpperCamelCase			( cls    ,   A__ = 1_28    ) ->	List[str]:

      def cache_decorator_inner(A__    ) -> Callable[..., U]:
       def cache_decorator_wrapper(*A__    ) -> U:
        if func not in cls.decorator_function_to_instance_map:
         snake_case				=			LRUCache(lowercase_    )

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

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

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

       return cache_decorator_wrapper

      return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()








'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					IFInpaintingSuperResolutionPipeline
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
     _UpperCAmelCase										=					PipelineTesterMixin.required_optional_params - {'''latents'''}







     def     UpperCamelCase			( self    ) ->	int:
      return self._get_superresolution_dummy_components()







     def     UpperCamelCase			( self    ,   A__    ,   A__=0    ) ->	Union[str, Any]:
      if str(A__    ).startswith('''mps'''    ):
       snake_case				=			torch.manual_seed(A__    )
      else:
       snake_case				=			torch.Generator(device=A__    ).manual_seed(A__    )

      snake_case				=			floats_tensor((1, 3, 16, 16)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )

      snake_case				=			{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''original_image''': original_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }

      return inputs







     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()    ,   reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,   )
     def     UpperCamelCase			( self    ) ->	List[Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_optional_components()







     @unittest.skipIf(torch_device != '''cuda'''    ,   reason='''float16 requires CUDA'''    )
     def     UpperCamelCase			( self    ) ->	List[str]:
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1e-1    )







     def     UpperCamelCase			( self    ) ->	int:
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_local()







     def     UpperCamelCase			( self    ) ->	Dict:
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2    ,   )

