"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

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


__lowerCAmelCase     :				Tuple    				=    False

class    a_   (  unittest.TestCase      ):
					pass

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




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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""	,    torch_dtype=torch.floataa  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )

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

							lowerCAmelCase__   = torch.manual_seed(0  )
							lowerCAmelCase__   = pipe.dual_guided(
							    prompt="""first prompt"""	,    image=snake_case__	,    text_to_image_strength=0.75	,    generator=snake_case__	,    guidance_scale=7.5	,    num_inference_steps=2	,    output_type="""numpy"""	,    ).images

							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(snake_case__  )
									lowerCAmelCase__   = VersatileDiffusionPipeline.from_pretrained(snake_case__	,    torch_dtype=torch.floataa  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = generator.manual_seed(0  )
							lowerCAmelCase__   = pipe.dual_guided(
							    prompt="""first prompt"""	,    image=snake_case__	,    text_to_image_strength=0.75	,    generator=snake_case__	,    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     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""	,    torch_dtype=torch.floataa  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = """cyberpunk 2077"""
							lowerCAmelCase__   = load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""  )
							lowerCAmelCase__   = torch.manual_seed(0  )
							lowerCAmelCase__   = pipe.dual_guided(
							    prompt=snake_case__	,    image=snake_case__	,    text_to_image_strength=0.75	,    generator=snake_case__	,    guidance_scale=7.5	,    num_inference_steps=50	,    output_type="""numpy"""	,    ).images

							lowerCAmelCase__   = image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]  )

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

							lowerCAmelCase__   = """A painting of a squirrel eating a burger """
							lowerCAmelCase__   = torch.manual_seed(0  )
							lowerCAmelCase__   = pipe.text_to_image(
							    prompt=snake_case__	,    generator=snake_case__	,    guidance_scale=7.5	,    num_inference_steps=50	,    output_type="""numpy"""  ).images

							lowerCAmelCase__   = image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]  )

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

							lowerCAmelCase__   = pipe.image_variation(snake_case__	,    generator=snake_case__	,    output_type="""numpy"""  ).images

							lowerCAmelCase__   = image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]  )

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





"""simple docstring"""




from __future__ import annotations

from typing import Any

class    a_   :




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  int = 6  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							self.create_linked_list(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  int  ):
							lowerCAmelCase__   = Node()
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							for _ in range(1	,    snake_case__  ):
									lowerCAmelCase__   = Node()
									lowerCAmelCase__   = current_node
									lowerCAmelCase__   = previous_node
									lowerCAmelCase__   = current_node
							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = previous_node




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return (
							    self.front == self.rear
							    and self.front is not None
							    and self.front.data is None
							)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							return self.front.data if self.front else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Any  ):
							if self.rear is None:
									return

							self.check_is_full()
							if not self.is_empty():
									lowerCAmelCase__   = self.rear.next
							if self.rear:
									lowerCAmelCase__   = data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							if self.rear is None or self.front is None:
									return None
							if self.front == self.rear:
									lowerCAmelCase__   = self.front.data
									lowerCAmelCase__   = None
									return data

							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = old_front.next
							lowerCAmelCase__   = old_front.data
							lowerCAmelCase__   = None
							return data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							if self.is_empty():
									raise Exception("""Empty Queue"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							if self.rear and self.rear.next == self.front:
									raise Exception("""Full Queue"""  )

class    a_   :




					def __init__(						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None


if __name__ == "__main__":
			import doctest

			doctest.testmod()




