





"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A	     =					False




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					pass






@nightly
@require_torch_gpu
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self       ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  List[Any]         =					'''A painting of a squirrel eating a burger '''
									__a	:  Union[str, Any]         =					torch.manual_seed(0       )
									__a	:  List[Any]         =					pipe(
									    prompt=_UpperCAmelCase		,     generator=_UpperCAmelCase		,     guidance_scale=7.5		,     num_inference_steps=2		,     output_type='''numpy'''       ).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(_UpperCAmelCase       )
													__a	:  Any         =					VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase       )
									pipe.to(_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Union[str, Any]         =					generator.manual_seed(0       )
									__a	:  Any         =					pipe(
									    prompt=_UpperCAmelCase		,     generator=_UpperCAmelCase		,     guidance_scale=7.5		,     num_inference_steps=2		,     output_type='''numpy'''       ).images

									assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					VersatileDiffusionTextToImagePipeline.from_pretrained(
									    '''shi-labs/versatile-diffusion'''		,     torch_dtype=torch.floataa       )
									pipe.to(_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Optional[int]         =					'''A painting of a squirrel eating a burger '''
									__a	:  Tuple         =					torch.manual_seed(0       )
									__a	:  int         =					pipe(
									    prompt=_UpperCAmelCase		,     generator=_UpperCAmelCase		,     guidance_scale=7.5		,     num_inference_steps=50		,     output_type='''numpy'''       ).images

									__a	:  List[str]         =					image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									__a	:  Optional[int]         =					np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]       )

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






"""simple docstring"""






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A	     =					TypeVar('''T''')




class 			__lowercase     (     Generic[T]							):







					'''simple docstring'''




					__lowerCAmelCase					=					42  # Cache store of keys
					__lowerCAmelCase					=					42  # References of the keys in cache
					__lowerCAmelCase					=					10  # Maximum capacity of cache







					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Optional[int]         =					deque()
									__a	:  Dict         =					set()
									if not n:
													__a	:  List[Any]         =					sys.maxsize
									elif n < 0:
													raise ValueError('''n should be an integer greater than 0.'''       )
									else:
													__a	:  str         =					n







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									if x not in self.key_reference:
													if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																	__a	:  int         =					self.dq_store.pop()
																	self.key_reference.remove(_UpperCAmelCase       )
									else:
													self.dq_store.remove(_UpperCAmelCase       )

									self.dq_store.appendleft(_UpperCAmelCase       )
									self.key_reference.add(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									for k in self.dq_store:
													print(_UpperCAmelCase       )







					def __repr__(  self       ):
									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A	     =					LRUCache(4)
		lru_cache.refer('''A''')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('''A''')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"