






import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class    _SCREAMING_SNAKE_CASE							(		__snake_case       ,unittest.TestCase	):


			'''simple docstring'''





			__a							:							Union[str, Any]					     =					AudioLDMPipeline
			__a							:							List[Any]					     =					TEXT_TO_AUDIO_PARAMS
			__a							:							Dict					     =					TEXT_TO_AUDIO_BATCH_PARAMS
			__a							:							Tuple					     =					frozenset(
			    [
			        "num_inference_steps",
			        "num_waveforms_per_prompt",
			        "generator",
			        "latents",
			        "output_type",
			        "return_dict",
			        "callback",
			        "callback_steps",
			    ]	)






			def 				A      (   self							:						str			)							->    Dict:

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4) ,      layers_per_block=2 ,      sample_size=3_2 ,      in_channels=4 ,      out_channels=4 ,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,      cross_attention_dim=(3_2, 6_4) ,      class_embed_type="""simple_projection""" ,      projection_class_embeddings_input_dim=3_2 ,      class_embeddings_concat=_lowercase ,      )
									UpperCamelCase__         = DDIMScheduler(
									    beta_start=0.0_0_0_8_5 ,      beta_end=0.0_1_2 ,      beta_schedule="""scaled_linear""" ,      clip_sample=_lowercase ,      set_alpha_to_one=_lowercase ,      )
									torch.manual_seed(0			)
									UpperCamelCase__         = AutoencoderKL(
									    block_out_channels=[3_2, 6_4] ,      in_channels=1 ,      out_channels=1 ,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,      latent_channels=4 ,      )
									torch.manual_seed(0			)
									UpperCamelCase__         = ClapTextConfig(
									    bos_token_id=0 ,      eos_token_id=2 ,      hidden_size=3_2 ,      intermediate_size=3_7 ,      layer_norm_eps=1e-05 ,      num_attention_heads=4 ,      num_hidden_layers=5 ,      pad_token_id=1 ,      vocab_size=1_0_0_0 ,      projection_dim=3_2 ,      )
									UpperCamelCase__         = ClapTextModelWithProjection(_lowercase			)
									UpperCamelCase__         = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" ,      model_max_length=7_7			)

									UpperCamelCase__         = SpeechTaHifiGanConfig(
									    model_in_dim=8 ,      sampling_rate=1_6_0_0_0 ,      upsample_initial_channel=1_6 ,      upsample_rates=[2, 2] ,      upsample_kernel_sizes=[4, 4] ,      resblock_kernel_sizes=[3, 7] ,      resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] ,      normalize_before=_lowercase ,      )

									UpperCamelCase__         = SpeechTaHifiGan(_lowercase			)

									UpperCamelCase__         = {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """vocoder""": vocoder,
									}
									return components






			def 				A      (   self							:						Dict ,      lowercase							:						int ,      lowercase							:						Union[str, Any]=0			)							->    Any:

									'''simple docstring'''







									if str(_lowercase			).startswith("""mps"""			):
															UpperCamelCase__         = torch.manual_seed(_lowercase			)
									else:
															UpperCamelCase__         = torch.Generator(device=_lowercase			).manual_seed(_lowercase			)
									UpperCamelCase__         = {
									    """prompt""": """A hammer hitting a wooden surface""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									}
									return inputs






			def 				A      (   self							:						List[str]			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = """cpu"""  # ensure determinism for the device-dependent torch.Generator

									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = AudioLDMPipeline(**_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)

									UpperCamelCase__         = self.get_dummy_inputs(_lowercase			)
									UpperCamelCase__         = audioldm_pipe(**_lowercase			)
									UpperCamelCase__         = output.audios[0]

									assert audio.ndim == 1
									assert len(_lowercase			) == 2_5_6

									UpperCamelCase__         = audio[:1_0]
									UpperCamelCase__         = np.array(
									    [-0.0_0_5_0, 0.0_0_5_0, -0.0_0_6_0, 0.0_0_3_3, -0.0_0_2_6, 0.0_0_3_3, -0.0_0_2_7, 0.0_0_3_3, -0.0_0_2_8, 0.0_0_3_3]			)

									assert np.abs(audio_slice - expected_slice			).max() < 1e-2






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

									'''simple docstring'''







									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = AudioLDMPipeline(**_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)

									UpperCamelCase__         = self.get_dummy_inputs(_lowercase			)
									UpperCamelCase__         = 3 * [inputs["""prompt"""]]

									# forward
									UpperCamelCase__         = audioldm_pipe(**_lowercase			)
									UpperCamelCase__         = output.audios[0]

									UpperCamelCase__         = self.get_dummy_inputs(_lowercase			)
									UpperCamelCase__         = 3 * [inputs.pop("""prompt"""			)]

									UpperCamelCase__         = audioldm_pipe.tokenizer(
									    _lowercase ,      padding="""max_length""" ,      max_length=audioldm_pipe.tokenizer.model_max_length ,      truncation=_lowercase ,      return_tensors="""pt""" ,      )
									UpperCamelCase__         = text_inputs["""input_ids"""].to(_lowercase			)

									UpperCamelCase__         = audioldm_pipe.text_encoder(
									    _lowercase ,      )
									UpperCamelCase__         = prompt_embeds.text_embeds
									# additional L_2 normalization over each hidden-state
									UpperCamelCase__         = F.normalize(_lowercase ,      dim=-1			)

									UpperCamelCase__         = prompt_embeds

									# forward
									UpperCamelCase__         = audioldm_pipe(**_lowercase			)
									UpperCamelCase__         = output.audios[0]

									assert np.abs(audio_a - audio_a			).max() < 1e-2






			def 				A      (   self							:						int			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = AudioLDMPipeline(**_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)

									UpperCamelCase__         = self.get_dummy_inputs(_lowercase			)
									UpperCamelCase__         = 3 * ["""this is a negative prompt"""]
									UpperCamelCase__         = negative_prompt
									UpperCamelCase__         = 3 * [inputs["""prompt"""]]

									# forward
									UpperCamelCase__         = audioldm_pipe(**_lowercase			)
									UpperCamelCase__         = output.audios[0]

									UpperCamelCase__         = self.get_dummy_inputs(_lowercase			)
									UpperCamelCase__         = 3 * [inputs.pop("""prompt"""			)]

									UpperCamelCase__         = []
									for p in [prompt, negative_prompt]:
															UpperCamelCase__         = audioldm_pipe.tokenizer(
															    _lowercase ,      padding="""max_length""" ,      max_length=audioldm_pipe.tokenizer.model_max_length ,      truncation=_lowercase ,      return_tensors="""pt""" ,      )
															UpperCamelCase__         = text_inputs["""input_ids"""].to(_lowercase			)

															UpperCamelCase__         = audioldm_pipe.text_encoder(
															    _lowercase ,      )
															UpperCamelCase__         = text_embeds.text_embeds
															# additional L_2 normalization over each hidden-state
															UpperCamelCase__         = F.normalize(_lowercase ,      dim=-1			)

															embeds.append(_lowercase			)

									UpperCamelCase__         = embeds

									# forward
									UpperCamelCase__         = audioldm_pipe(**_lowercase			)
									UpperCamelCase__         = output.audios[0]

									assert np.abs(audio_a - audio_a			).max() < 1e-2






			def 				A      (   self							:						Any			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = PNDMScheduler(skip_prk_steps=_lowercase			)
									UpperCamelCase__         = AudioLDMPipeline(**_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)

									UpperCamelCase__         = self.get_dummy_inputs(_lowercase			)
									UpperCamelCase__         = """egg cracking"""
									UpperCamelCase__         = audioldm_pipe(**_lowercase ,      negative_prompt=_lowercase			)
									UpperCamelCase__         = output.audios[0]

									assert audio.ndim == 1
									assert len(_lowercase			) == 2_5_6

									UpperCamelCase__         = audio[:1_0]
									UpperCamelCase__         = np.array(
									    [-0.0_0_5_1, 0.0_0_5_0, -0.0_0_6_0, 0.0_0_3_4, -0.0_0_2_6, 0.0_0_3_3, -0.0_0_2_7, 0.0_0_3_3, -0.0_0_2_8, 0.0_0_3_2]			)

									assert np.abs(audio_slice - expected_slice			).max() < 1e-2






			def 				A      (   self							:						Optional[int]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = PNDMScheduler(skip_prk_steps=_lowercase			)
									UpperCamelCase__         = AudioLDMPipeline(**_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)

									UpperCamelCase__         = """A hammer hitting a wooden surface"""

									# test num_waveforms_per_prompt=1 (default)
									UpperCamelCase__         = audioldm_pipe(_lowercase ,      num_inference_steps=2			).audios

									assert audios.shape == (1, 2_5_6)

									# test num_waveforms_per_prompt=1 (default) for batch of prompts
									UpperCamelCase__         = 2
									UpperCamelCase__         = audioldm_pipe([prompt] * batch_size ,      num_inference_steps=2			).audios

									assert audios.shape == (batch_size, 2_5_6)

									# test num_waveforms_per_prompt for single prompt
									UpperCamelCase__         = 2
									UpperCamelCase__         = audioldm_pipe(_lowercase ,      num_inference_steps=2 ,      num_waveforms_per_prompt=_lowercase			).audios

									assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

									# test num_waveforms_per_prompt for batch of prompts
									UpperCamelCase__         = 2
									UpperCamelCase__         = audioldm_pipe(
									    [prompt] * batch_size ,      num_inference_steps=2 ,      num_waveforms_per_prompt=_lowercase			).audios

									assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)






			def 				A      (   self							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = AudioLDMPipeline(**_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)
									UpperCamelCase__         = audioldm_pipe.vocoder.config.sampling_rate

									UpperCamelCase__         = self.get_dummy_inputs(_lowercase			)
									UpperCamelCase__         = audioldm_pipe(audio_length_in_s=0.0_1_6 ,      **_lowercase			)
									UpperCamelCase__         = output.audios[0]

									assert audio.ndim == 1
									assert len(_lowercase			) / vocoder_sampling_rate == 0.0_1_6

									UpperCamelCase__         = audioldm_pipe(audio_length_in_s=0.0_3_2 ,      **_lowercase			)
									UpperCamelCase__         = output.audios[0]

									assert audio.ndim == 1
									assert len(_lowercase			) / vocoder_sampling_rate == 0.0_3_2






			def 				A      (   self							:						Union[str, Any]			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = AudioLDMPipeline(**_lowercase			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)

									UpperCamelCase__         = ["""hey"""]

									UpperCamelCase__         = audioldm_pipe(_lowercase ,      num_inference_steps=1			)
									UpperCamelCase__         = output.audios.shape
									assert audio_shape == (1, 2_5_6)

									UpperCamelCase__         = audioldm_pipe.vocoder.config
									config.model_in_dim *= 2
									UpperCamelCase__         = SpeechTaHifiGan(_lowercase			).to(_lowercase			)
									UpperCamelCase__         = audioldm_pipe(_lowercase ,      num_inference_steps=1			)
									UpperCamelCase__         = output.audios.shape
									# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
									assert audio_shape == (1, 2_5_6)






			def 				A      (   self							:						Union[str, Any]			)							->    List[Any]:

									'''simple docstring'''







									self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_lowercase			)






			def 				A      (   self							:						Any			)							->    Optional[Any]:

									'''simple docstring'''







									self._test_inference_batch_single_identical(test_mean_pixel_difference=_lowercase			)







			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available() ,      reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,      )
			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_lowercase			)
@slow
class    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						Optional[Any]			)							->    List[str]:

									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Tuple ,      lowercase							:						Optional[Any]="cpu" ,      lowercase							:						List[Any]=torch.floataa ,      lowercase							:						Tuple=0			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = torch.Generator(device=_lowercase			).manual_seed(_lowercase			)
									UpperCamelCase__         = np.random.RandomState(_lowercase			).standard_normal((1, 8, 1_2_8, 1_6)			)
									UpperCamelCase__         = torch.from_numpy(_lowercase			).to(device=_lowercase ,      dtype=_lowercase			)
									UpperCamelCase__         = {
									    """prompt""": """A hammer hitting a wooden surface""",
									    """latents""": latents,
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 2.5,
									}
									return inputs






			def 				A      (   self							:						List[str]			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)

									UpperCamelCase__         = self.get_inputs(_lowercase			)
									UpperCamelCase__         = 2_5
									UpperCamelCase__         = audioldm_pipe(**_lowercase			).audios[0]

									assert audio.ndim == 1
									assert len(_lowercase			) == 8_1_9_2_0

									UpperCamelCase__         = audio[7_7_2_3_0:7_7_2_4_0]
									UpperCamelCase__         = np.array(
									    [-0.4_8_8_4, -0.4_6_0_7, 0.0_0_2_3, 0.5_0_0_7, 0.5_8_9_6, 0.5_1_5_1, 0.3_8_1_3, -0.0_2_0_8, -0.3_6_8_7, -0.4_3_1_5]			)
									UpperCamelCase__         = np.abs(expected_slice - audio_slice			).max()
									assert max_diff < 1e-2







			def 				A      (   self							:						Dict			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""			)
									UpperCamelCase__         = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config			)
									UpperCamelCase__         = audioldm_pipe.to(_lowercase			)
									audioldm_pipe.set_progress_bar_config(disable=_lowercase			)

									UpperCamelCase__         = self.get_inputs(_lowercase			)
									UpperCamelCase__         = audioldm_pipe(**_lowercase			).audios[0]

									assert audio.ndim == 1
									assert len(_lowercase			) == 8_1_9_2_0

									UpperCamelCase__         = audio[2_7_7_8_0:2_7_7_9_0]
									UpperCamelCase__         = np.array([-0.2_1_3_1, -0.0_8_7_3, -0.0_1_2_4, -0.0_1_8_9, 0.0_5_6_9, 0.1_3_7_3, 0.1_8_8_3, 0.2_8_8_6, 0.3_2_9_7, 0.2_2_1_2]			)
									UpperCamelCase__         = np.abs(expected_slice - audio_slice			).max()
									assert max_diff < 3e-2








'''simple docstring'''


from __future__ import annotations

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

lowerCamelCase_				: List[str]    	=							TypeVar('''T''')
lowerCamelCase_				: Optional[int]    	=							TypeVar('''U''')
class    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			def __init__(   self							:						Dict ,      lowercase							:						T | None ,      lowercase							:						U | None			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = key
									UpperCamelCase__         = val
									UpperCamelCase__         = None
									UpperCamelCase__         = None







			def __repr__(   self							:						List[Any]			)							->    str:

									'''simple docstring'''







									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]	):


			'''simple docstring'''





			def __init__(   self							:						Union[str, Any]			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
									UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.rear, self.head






			def __repr__(   self							:						int			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = ["""DoubleLinkedList"""]
									UpperCamelCase__         = self.head
									while node.next is not None:
															rep.append(str(lowercase			)			)
															UpperCamelCase__         = node.next
									rep.append(str(self.rear			)			)
									return ",\n    ".join(lowercase			)






			def 				A      (   self							:						str ,      lowercase							:						DoubleLinkedListNode[T, U]			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = self.rear.prev

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

									UpperCamelCase__         = node
									UpperCamelCase__         = previous
									UpperCamelCase__         = node
									UpperCamelCase__         = self.rear







			def 				A      (   self							:						Any ,      lowercase							:						DoubleLinkedListNode[T, U]			)							->    DoubleLinkedListNode[T, U] | None:

									'''simple docstring'''







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

									UpperCamelCase__         = node.next
									UpperCamelCase__         = node.prev
									UpperCamelCase__         = None
									UpperCamelCase__         = None
									return node
class    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			__a							:							dict[Callable[[T], U], LRUCache[T, U]]					     =					{}






			def __init__(   self							:						int ,      lowercase							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = DoubleLinkedList()
									UpperCamelCase__         = capacity
									UpperCamelCase__         = 0
									UpperCamelCase__         = 0
									UpperCamelCase__         = 0
									UpperCamelCase__         = {}






			def __repr__(   self							:						Any			)							->    str:

									'''simple docstring'''







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






			def __contains__(   self							:						Any ,      lowercase							:						T			)							->    bool:

									'''simple docstring'''







									return key in self.cache






			def 				A      (   self							:						Tuple ,      lowercase							:						T			)							->    U | None:

									'''simple docstring'''







									if key in self.cache:
															self.hits += 1
															UpperCamelCase__         = self.cache[key]
															UpperCamelCase__         = 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 				A      (   self							:						Dict ,      lowercase							:						T ,      lowercase							:						U			)							->    None:

									'''simple docstring'''







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

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







			@classmethod
			def 				A      (   cls							:						Optional[int] ,      lowercase							:						int = 1_2_8			)							->    Callable[[Callable[[T], U]], Callable[..., U]]:

									'''simple docstring'''







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

																					UpperCamelCase__         = cls.decorator_function_to_instance_map[func].get(args[0]			)
																					if result is None:
																											UpperCamelCase__         = 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()
