




"""simple docstring"""





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowercase		 =      False

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


				'''simple docstring'''


				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def  _lowerCamelCase   (				self				:       int ):
										snake_case__    :   Union[str, Any]							     =   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
										# remove text_unet
										pipe.remove_unused_weights()
										pipe.to(_A )
										pipe.set_progress_bar_config(disable=_A )

										snake_case__    :   int							     =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )

										snake_case__    :   Dict							     =   torch.manual_seed(0 )
										snake_case__    :   Optional[int]							     =   pipe(
										    prompt='''first prompt''' ,image=_A ,text_to_image_strength=0.75 ,generator=_A ,guidance_scale=7.5 ,num_inference_steps=2 ,output_type='''numpy''' ,).images

										with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(_A )
																snake_case__    :   Optional[int]							     =   VersatileDiffusionDualGuidedPipeline.from_pretrained(_A )

										pipe.to(_A )
										pipe.set_progress_bar_config(disable=_A )

										snake_case__    :   Optional[Any]							     =   generator.manual_seed(0 )
										snake_case__    :   Dict							     =   pipe(
										    prompt='''first prompt''' ,image=_A ,text_to_image_strength=0.75 ,generator=_A ,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				:       Optional[Any] ):
										snake_case__    :   Optional[Any]							     =   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
										pipe.remove_unused_weights()
										pipe.to(_A )
										pipe.set_progress_bar_config(disable=_A )

										snake_case__    :   Tuple							     =   '''cyberpunk 2077'''
										snake_case__    :   Dict							     =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
										snake_case__    :   List[Any]							     =   torch.manual_seed(0 )
										snake_case__    :   str							     =   pipe(
										    prompt=_A ,image=_A ,text_to_image_strength=0.75 ,generator=_A ,guidance_scale=7.5 ,num_inference_steps=5_0 ,output_type='''numpy''' ,).images

										snake_case__    :   Optional[int]							     =   image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										snake_case__    :   Any							     =   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779] )

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








"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   Any							     =   data
										snake_case__    :   Node | None							     =   None





				def __str__(				self				:       Union[str, Any] ):
										return F'''{self.data}'''







class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       Optional[int] ):
										snake_case__    :   Node | None							     =   None
										snake_case__    :   Node | None							     =   None

				def __iter__(				self				:       Optional[int] ):
										snake_case__    :   Optional[int]							     =   self.front
										while node:
																yield node.data
																snake_case__    :   Optional[int]							     =   node.next

				def __len__(				self				:       Any ):
										return len(tuple(iter(self ) ) )

				def __str__(				self				:       Union[str, Any] ):
										return " <- ".join(str(SCREAMING_SNAKE_CASE_ ) for item in self )

				def  _lowerCamelCase   (				self				:       List[str] ):
										return len(self ) == 0

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   int							     =   Node(SCREAMING_SNAKE_CASE_ )
										if self.is_empty():
																snake_case__    :   List[Any]							     =   node
										else:
																assert isinstance(self.rear ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Union[str, Any]							     =   node
																snake_case__    :   int							     =   node

				def  _lowerCamelCase   (				self				:       int ):
										if self.is_empty():
																raise IndexError('''dequeue from empty queue''' )
										assert isinstance(self.front ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   List[str]							     =   self.front
										snake_case__    :   str							     =   self.front.next
										if self.front is None:
																snake_case__    :   Optional[Any]							     =   None
										return node.data





				def  _lowerCamelCase   (				self				:       Optional[int] ):
										snake_case__    :   Optional[Any]							     =   None


if __name__ == "__main__":
							from doctest import testmod

							testmod()


