






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





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

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""			)

									torch.manual_seed(0			)
									UpperCamelCase__         = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""			)

									torch.manual_seed(0			)
									UpperCamelCase__         = UNetaDConditionModel(
									    sample_size=3_2 ,      layers_per_block=1 ,      block_out_channels=[3_2, 6_4] ,      down_block_types=[
									        """ResnetDownsampleBlock2D""",
									        """SimpleCrossAttnDownBlock2D""",
									    ] ,      mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,      up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,      in_channels=3 ,      out_channels=6 ,      cross_attention_dim=3_2 ,      encoder_hid_dim=3_2 ,      attention_head_dim=8 ,      addition_embed_type="""text""" ,      addition_embed_type_num_heads=2 ,      cross_attention_norm="""group_norm""" ,      resnet_time_scale_shift="""scale_shift""" ,      act_fn="""gelu""" ,      )
									unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

									torch.manual_seed(0			)
									UpperCamelCase__         = DDPMScheduler(
									    num_train_timesteps=1_0_0_0 ,      beta_schedule="""squaredcos_cap_v2""" ,      beta_start=0.0_0_0_1 ,      beta_end=0.0_2 ,      thresholding=lowercase ,      dynamic_thresholding_ratio=0.9_5 ,      sample_max_value=1.0 ,      prediction_type="""epsilon""" ,      variance_type="""learned_range""" ,      )

									torch.manual_seed(0			)
									UpperCamelCase__         = IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}






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

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""			)

									torch.manual_seed(0			)
									UpperCamelCase__         = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""			)

									torch.manual_seed(0			)
									UpperCamelCase__         = UNetaDConditionModel(
									    sample_size=3_2 ,      layers_per_block=[1, 2] ,      block_out_channels=[3_2, 6_4] ,      down_block_types=[
									        """ResnetDownsampleBlock2D""",
									        """SimpleCrossAttnDownBlock2D""",
									    ] ,      mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,      up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,      in_channels=6 ,      out_channels=6 ,      cross_attention_dim=3_2 ,      encoder_hid_dim=3_2 ,      attention_head_dim=8 ,      addition_embed_type="""text""" ,      addition_embed_type_num_heads=2 ,      cross_attention_norm="""group_norm""" ,      resnet_time_scale_shift="""scale_shift""" ,      act_fn="""gelu""" ,      class_embed_type="""timestep""" ,      mid_block_scale_factor=1.4_1_4 ,      time_embedding_act_fn="""gelu""" ,      time_embedding_dim=3_2 ,      )
									unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

									torch.manual_seed(0			)
									UpperCamelCase__         = DDPMScheduler(
									    num_train_timesteps=1_0_0_0 ,      beta_schedule="""squaredcos_cap_v2""" ,      beta_start=0.0_0_0_1 ,      beta_end=0.0_2 ,      thresholding=lowercase ,      dynamic_thresholding_ratio=0.9_5 ,      sample_max_value=1.0 ,      prediction_type="""epsilon""" ,      variance_type="""learned_range""" ,      )

									torch.manual_seed(0			)
									UpperCamelCase__         = DDPMScheduler(
									    num_train_timesteps=1_0_0_0 ,      beta_schedule="""squaredcos_cap_v2""" ,      beta_start=0.0_0_0_1 ,      beta_end=0.0_2 ,      )

									torch.manual_seed(0			)
									UpperCamelCase__         = IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "image_noising_scheduler": image_noising_scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}






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

									'''simple docstring'''







									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = self.pipeline_class(**lowercase			)
									pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = self.get_dummy_inputs(lowercase			)

									UpperCamelCase__         = inputs["""prompt"""]
									UpperCamelCase__         = inputs["""generator"""]
									UpperCamelCase__         = inputs["""num_inference_steps"""]
									UpperCamelCase__         = inputs["""output_type"""]

									if "image" in inputs:
															UpperCamelCase__         = inputs["""image"""]
									else:
															UpperCamelCase__         = None

									if "mask_image" in inputs:
															UpperCamelCase__         = inputs["""mask_image"""]
									else:
															UpperCamelCase__         = None

									if "original_image" in inputs:
															UpperCamelCase__         = inputs["""original_image"""]
									else:
															UpperCamelCase__         = None

									UpperCamelCase__    ,   UpperCamelCase__         = pipe.encode_prompt(lowercase			)

									# inputs with prompt converted to embeddings
									UpperCamelCase__         = {
									    """prompt_embeds""": prompt_embeds,
									    """negative_prompt_embeds""": negative_prompt_embeds,
									    """generator""": generator,
									    """num_inference_steps""": num_inference_steps,
									    """output_type""": output_type,
									}

									if image is not None:
															UpperCamelCase__         = image

									if mask_image is not None:
															UpperCamelCase__         = mask_image

									if original_image is not None:
															UpperCamelCase__         = original_image

									# set all optional components to None
									for optional_component in pipe._optional_components:
															setattr(lowercase ,      lowercase ,      lowercase			)

									UpperCamelCase__         = pipe(**lowercase			)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(lowercase			)
															UpperCamelCase__         = self.pipeline_class.from_pretrained(lowercase			)
															pipe_loaded.to(lowercase			)
															pipe_loaded.set_progress_bar_config(disable=lowercase			)

									pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

									for optional_component in pipe._optional_components:
															self.assertTrue(
															    getattr(lowercase ,      lowercase			) is None ,      f"`{optional_component}` did not stay set to None after loading." ,      )

									UpperCamelCase__         = self.get_dummy_inputs(lowercase			)

									UpperCamelCase__         = inputs["""generator"""]
									UpperCamelCase__         = inputs["""num_inference_steps"""]
									UpperCamelCase__         = inputs["""output_type"""]

									# inputs with prompt converted to embeddings
									UpperCamelCase__         = {
									    """prompt_embeds""": prompt_embeds,
									    """negative_prompt_embeds""": negative_prompt_embeds,
									    """generator""": generator,
									    """num_inference_steps""": num_inference_steps,
									    """output_type""": output_type,
									}

									if image is not None:
															UpperCamelCase__         = image

									if mask_image is not None:
															UpperCamelCase__         = mask_image

									if original_image is not None:
															UpperCamelCase__         = original_image

									UpperCamelCase__         = pipe_loaded(**lowercase			)[0]

									UpperCamelCase__         = np.abs(to_np(lowercase			) - to_np(lowercase			)			).max()
									self.assertLess(lowercase ,      1e-4			)







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

									'''simple docstring'''







									UpperCamelCase__         = self.get_dummy_components()
									UpperCamelCase__         = self.pipeline_class(**lowercase			)
									pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = self.get_dummy_inputs(lowercase			)
									UpperCamelCase__         = pipe(**lowercase			)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(lowercase			)
															UpperCamelCase__         = self.pipeline_class.from_pretrained(lowercase			)
															pipe_loaded.to(lowercase			)
															pipe_loaded.set_progress_bar_config(disable=lowercase			)

									pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

									UpperCamelCase__         = self.get_dummy_inputs(lowercase			)
									UpperCamelCase__         = pipe_loaded(**lowercase			)[0]

									UpperCamelCase__         = np.abs(to_np(lowercase			) - to_np(lowercase			)			).max()
									self.assertLess(lowercase ,      1e-4			)








'''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()
