





"""simple docstring"""



from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

a_ =			TypeVar("""KEY""")
a_ =			TypeVar("""VAL""")






@dataclass(frozen=SCREAMING_SNAKE_CASE__							,							slots=SCREAMING_SNAKE_CASE__     )
class 		__snake_case					(		Generic[KEY, VAL]     ):





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     42






class 		__snake_case					(		_Item     ):





 """simple docstring"""


 def __init__(    self  ):



     '''simple docstring'''




     super().__init__(__lowerCamelCase					,						__lowerCamelCase  )







 def __bool__(    self  ):



     '''simple docstring'''




     return False






a_ =			_DeletedItem()






class 		__snake_case					(		MutableMapping[KEY, VAL]     ):





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase = 8					,						__lowerCamelCase = 0.7_5  ):



     '''simple docstring'''




     __A    :						Tuple						 =     initial_block_size
     __A    :						list[_Item | None]						 =     [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     __A    :						List[Any]						 =     capacity_factor
     __A    :						Union[str, Any]						 =     0

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     return hash(__lowerCamelCase  ) % len(self._buckets  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     return (ind + 1) % len(self._buckets  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Any						 =     self._buckets[ind]
     if not stored:
         __A    :						Tuple						 =     _Item(__lowerCamelCase					,						__lowerCamelCase  )
         self._len += 1
         return True
     elif stored.key == key:
         __A    :						List[Any]						 =     _Item(__lowerCamelCase					,						__lowerCamelCase  )
         return True
     else:
         return False

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     len(self._buckets  ) * self._capacity_factor
     return len(self  ) >= int(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if len(self._buckets  ) <= self._initial_block_size:
         return False
     __A    :						int						 =     len(self._buckets  ) * self._capacity_factor / 2
     return len(self  ) < limit

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						int						 =     self._buckets
     __A    :						Union[str, Any]						 =     [None] * new_size
     __A    :						List[Any]						 =     0
     for item in old_buckets:
         if item:
             self._add_item(item.key					,						item.val  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self._resize(len(self._buckets  ) * 2  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self._resize(len(self._buckets  ) // 2  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     self._get_bucket_index(__lowerCamelCase  )
     for _ in range(len(self._buckets  )  ):
         yield ind
         __A    :						Dict						 =     self._get_next_ind(__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     for ind in self._iterate_buckets(__lowerCamelCase  ):
         if self._try_set(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):
             break

 def __setitem__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     if self._is_full():
         self._size_up()

     self._add_item(__lowerCamelCase					,						__lowerCamelCase  )

 def __delitem__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     for ind in self._iterate_buckets(__lowerCamelCase  ):
         __A    :						List[Any]						 =     self._buckets[ind]
         if item is None:
             raise KeyError(__lowerCamelCase  )
         if item is _deleted:
             continue
         if item.key == key:
             __A    :						List[Any]						 =     _deleted
             self._len -= 1
             break
     if self._is_sparse():
         self._size_down()

 def __getitem__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     for ind in self._iterate_buckets(__lowerCamelCase  ):
         __A    :						Optional[int]						 =     self._buckets[ind]
         if item is None:
             break
         if item is _deleted:
             continue
         if item.key == key:
             return item.val
     raise KeyError(__lowerCamelCase  )

 def __len__(    self  ):



     '''simple docstring'''




     return self._len

 def __iter__(    self  ):



     '''simple docstring'''




     yield from (item.key for item in self._buckets if item)







 def __repr__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     ''' ,'''.join(
         F"""{item.key}: {item.val}""" for item in self._buckets if item  )
     return F"""HashMap({val_string})"""











"""simple docstring"""



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     1
     __A    :						Any						 =     3
     __A    :						List[str]						 =     (32, 32)

     __A    :						List[str]						 =     floats_tensor((batch_size, num_channels) + sizes					,						rng=random.Random(0  )  ).to(__lowerCamelCase  )
     return image

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						List[Any]						 =     UNetaDConditionModel(
         block_out_channels=(32, 32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=7					,						out_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')					,						up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')					,						cross_attention_dim=32					,						attention_head_dim=8					,						use_linear_projection=__lowerCamelCase					,						only_cross_attention=(True, True, False)					,						num_class_embeds=100					,						)
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Any						 =     AutoencoderKL(
         block_out_channels=[32, 32, 64]					,						in_channels=3					,						out_channels=3					,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,						latent_channels=4					,						)
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Tuple						 =     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=1000					,						hidden_act='''gelu'''					,						projection_dim=512					,						)
     return CLIPTextModel(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A    :						int						 =     self.dummy_cond_unet_upscale
     __A    :						Union[str, Any]						 =     DDPMScheduler()
     __A    :						Dict						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						int						 =     self.dummy_vae
     __A    :						int						 =     self.dummy_text_encoder
     __A    :						Union[str, Any]						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						Tuple						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						Any						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # make sure here that pndm scheduler skips prk
     __A    :						Dict						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						str						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						List[str]						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Any						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						List[str]						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)

     __A    :						Union[str, Any]						 =     output.images

     __A    :						List[str]						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						str						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						return_dict=__lowerCamelCase					,						)[0]

     __A    :						Tuple						 =     image[0, -3:, -3:, -1]
     __A    :						int						 =     image_from_tuple[0, -3:, -3:, -1]

     __A    :						Dict						 =     low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     __A    :						str						 =     np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]  )

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A    :						Dict						 =     self.dummy_cond_unet_upscale
     __A    :						List[str]						 =     DDPMScheduler()
     __A    :						str						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						Optional[int]						 =     self.dummy_vae
     __A    :						Optional[Any]						 =     self.dummy_text_encoder
     __A    :						Optional[Any]						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						List[Any]						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						int						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # make sure here that pndm scheduler skips prk
     __A    :						Any						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						Any						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						Any						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Any						 =     sd_pipe(
         2 * [prompt]					,						image=2 * [low_res_image]					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)
     __A    :						Union[str, Any]						 =     output.images
     assert image.shape[0] == 2

     __A    :						Optional[Any]						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						Any						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_images_per_prompt=2					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)
     __A    :						Union[str, Any]						 =     output.images
     assert image.shape[0] == 2







 @unittest.skipIf(torch_device != '''cuda'''					,						'''This test requires a GPU'''  )
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.dummy_cond_unet_upscale
     __A    :						int						 =     DDPMScheduler()
     __A    :						List[Any]						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						Optional[Any]						 =     self.dummy_vae
     __A    :						List[str]						 =     self.dummy_text_encoder
     __A    :						Dict						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						Union[str, Any]						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						int						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # put models in fp16, except vae as it overflows in fp16
     __A    :						Union[str, Any]						 =     unet.half()
     __A    :						Optional[int]						 =     text_encoder.half()

     # make sure here that pndm scheduler skips prk
     __A    :						Optional[int]						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						Union[str, Any]						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Optional[Any]						 =     torch.manual_seed(0  )
     __A    :						Tuple						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=2					,						output_type='''np'''					,						).images

     __A    :						str						 =     low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)






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





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						Dict						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat.npy'''  )

     __A    :						str						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Optional[Any]						 =     StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase  )
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing()

     __A    :						Union[str, Any]						 =     '''a cat sitting on a park bench'''

     __A    :						Union[str, Any]						 =     torch.manual_seed(0  )
     __A    :						Optional[Any]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						List[str]						 =     output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image  ).max() < 1e-3

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						List[Any]						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat_fp16.npy'''  )

     __A    :						Optional[int]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Optional[int]						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing()

     __A    :						Dict						 =     '''a cat sitting on a park bench'''

     __A    :						Any						 =     torch.manual_seed(0  )
     __A    :						Optional[int]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						Any						 =     output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image  ).max() < 5e-1







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __A    :						Union[str, Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )

     __A    :						List[str]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Dict						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing(1  )
     pipe.enable_sequential_cpu_offload()

     __A    :						Tuple						 =     '''a cat sitting on a park bench'''

     __A    :						Tuple						 =     torch.manual_seed(0  )
     __A    :						List[str]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=5					,						output_type='''np'''					,						)

     __A    :						Any						 =     torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 10**9




