


from __future__ import annotations

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

UpperCAmelCase     =       TypeVar("""T""")
UpperCAmelCase     =       TypeVar("""U""")







class lowerCAmelCase_		(					Generic[T, U]			):

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       snake_case_			= key
       snake_case_			= val
       snake_case_			= 0
       snake_case_			= None
       snake_case_			= None





 def __repr__(      self   ):
       return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
           self.key						, self.val						, self.freq						, self.next is not None						, self.prev is not None   )







class lowerCAmelCase_		(					Generic[T, U]			):

 '''simple docstring'''

 def __init__(      self   ):
       snake_case_			= DoubleLinkedListNode(_UpperCAmelCase						, _UpperCAmelCase   )
       snake_case_			= DoubleLinkedListNode(_UpperCAmelCase						, _UpperCAmelCase   )
       snake_case_			,       snake_case_			= self.rear, self.head



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



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       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
       node.freq += 1
       self._position_node(_UpperCAmelCase   )



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):

       while node.prev is not None and node.prev.freq > node.freq:
             # swap node with previous node
             snake_case_			= node.prev

             snake_case_			= previous_node.prev
             snake_case_			= node.prev
             snake_case_			= previous_node
             snake_case_			= node





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):

       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 lowerCAmelCase_		(					Generic[T, U]			):

 '''simple docstring'''

 __snake_case				=    {}



 def __init__(      self						, _UpperCAmelCase   ):
       snake_case_			= DoubleLinkedList()
       snake_case_			= capacity
       snake_case_			= 0
       snake_case_			= 0
       snake_case_			= 0
       snake_case_			= {}



 def __repr__(      self   ):
       return (
           F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
           F'''capacity={self.capacity}, current_size={self.num_keys})'''
       )



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



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):

       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(_UpperCAmelCase   )
             return node.val
       self.miss += 1
       return None



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):

       if key not in self.cache:
             if self.num_keys >= self.capacity:
                   # delete first node 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(_UpperCAmelCase   ) is not None
                   # first_node guaranteed to be in list

                   del self.cache[first_node.key]
                   self.num_keys -= 1
             snake_case_			= DoubleLinkedListNode(_UpperCAmelCase						, _UpperCAmelCase   )
             self.list.add(self.cache[key]   )
             self.num_keys += 1

       else:
             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(_UpperCAmelCase   )





 @classmethod
 def 		UpperCamelCase__	(      cls						, _UpperCAmelCase = 1_28   ):

       def cache_decorator_inner(_UpperCAmelCase   ) -> Callable[..., U]:
             def cache_decorator_wrapper(*_UpperCAmelCase   ) -> U:
                   if func not in cls.decorator_function_to_instance_map:
                         snake_case_			= LFUCache(_UpperCAmelCase   )

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

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

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

             return cache_decorator_wrapper

       return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()



import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

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

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # 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   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )