


"""simple docstring"""
from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class        __A       :





							'''simple docstring'''

							def __init__(			self							: Tuple						,    UpperCAmelCase_							: Any    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						data
														snake_case_          =						None





class        __A       :





							'''simple docstring'''

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



														"""simple docstring"""



														snake_case_          =						None
														snake_case_          =						None



							def __iter__(			self							: Optional[int]    )   ->Iterator[Any]:



														"""simple docstring"""



														snake_case_          =						self.head
														while self.head:
																					yield node.data
																					snake_case_          =						node.next
																					if node == self.head:
																												break



							def __len__(			self							: str    )   ->int:



														"""simple docstring"""



														return sum(1 for _ in self    )



							def __repr__(			self							: Union[str, Any]    )   ->int:



														"""simple docstring"""



														return "->".join(str(UpperCAmelCase_    ) for item in iter(self    )    )



							def 						lowerCAmelCase    (			self							: Union[str, Any]						,    UpperCAmelCase_							: Any    )   ->None:



														"""simple docstring"""



														self.insert_nth(len(self    )						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: Any    )   ->None:



														"""simple docstring"""



														self.insert_nth(0						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Any						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: Any    )   ->None:



														"""simple docstring"""



														if index < 0 or index > len(self    ):
																					raise IndexError("""list index out of range."""    )
														snake_case_          =						Node(UpperCAmelCase_    )
														if self.head is None:
																					snake_case_          =						new_node  # first node points itself
																					snake_case_          =						snake_case_          =						new_node
														elif index == 0:  # insert at head
																					snake_case_          =						self.head
																					snake_case_          =						snake_case_          =						new_node
														else:
																					snake_case_          =						self.head
																					for _ in range(index - 1    ):
																												snake_case_          =						temp.next
																					snake_case_          =						temp.next
																					snake_case_          =						new_node
																					if index == len(self    ) - 1:  # insert at tail
																												snake_case_          =						new_node



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Any:



														"""simple docstring"""



														return self.delete_nth(0    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Any:



														"""simple docstring"""



														return self.delete_nth(len(self    ) - 1    )



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: int = 0    )   ->Any:



														"""simple docstring"""



														if not 0 <= index < len(self    ):
																					raise IndexError("""list index out of range."""    )
														snake_case_          =						self.head
														if self.head == self.tail:  # just one node
																					snake_case_          =						snake_case_          =						None
														elif index == 0:  # delete head node
																					snake_case_          =						self.tail.next.next
																					snake_case_          =						self.head.next
														else:
																					snake_case_          =						self.head
																					for _ in range(index - 1    ):
																												snake_case_          =						temp.next
																					snake_case_          =						temp.next
																					snake_case_          =						temp.next.next
																					if index == len(self    ) - 1:  # delete at tail
																												snake_case_          =						temp
														return delete_node.data



							def 						lowerCAmelCase    (			self							: Optional[Any]    )   ->bool:



														"""simple docstring"""



														return len(self    ) == 0







def   _a			( )      ->			None:
							snake_case_          =						CircularLinkedList()
							assert len(_SCREAMING_SNAKE_CASE			) == 0
							assert circular_linked_list.is_empty() is True
							assert str(_SCREAMING_SNAKE_CASE			) == ""

							try:
														circular_linked_list.delete_front()
														raise AssertionError  # This should not happen
							except IndexError:
														assert True  # This should happen

							try:
														circular_linked_list.delete_tail()
														raise AssertionError  # This should not happen
							except IndexError:
														assert True  # This should happen

							try:
														circular_linked_list.delete_nth(-1			)
														raise AssertionError
							except IndexError:
														assert True

							try:
														circular_linked_list.delete_nth(0			)
														raise AssertionError
							except IndexError:
														assert True

							assert circular_linked_list.is_empty() is True
							for i in range(5			):
														assert len(_SCREAMING_SNAKE_CASE			) == i
														circular_linked_list.insert_nth(_SCREAMING_SNAKE_CASE			,	i + 1			)
							assert str(_SCREAMING_SNAKE_CASE			) == "->".join(str(_SCREAMING_SNAKE_CASE			) for i in range(1			,	6			)			)

							circular_linked_list.insert_tail(6			)
							assert str(_SCREAMING_SNAKE_CASE			) == "->".join(str(_SCREAMING_SNAKE_CASE			) for i in range(1			,	7			)			)
							circular_linked_list.insert_head(0			)
							assert str(_SCREAMING_SNAKE_CASE			) == "->".join(str(_SCREAMING_SNAKE_CASE			) for i in range(0			,	7			)			)

							assert circular_linked_list.delete_front() == 0
							assert circular_linked_list.delete_tail() == 6
							assert str(_SCREAMING_SNAKE_CASE			) == "->".join(str(_SCREAMING_SNAKE_CASE			) for i in range(1			,	6			)			)
							assert circular_linked_list.delete_nth(2			) == 3

							circular_linked_list.insert_nth(2			,	3			)
							assert str(_SCREAMING_SNAKE_CASE			) == "->".join(str(_SCREAMING_SNAKE_CASE			) for i in range(1			,	6			)			)

							assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
				import doctest

				doctest.testmod()







"""simple docstring"""
import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class        __A       (snake_case__  ,						snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      CycleDiffusionPipeline
							__lowercase:						int							 =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    """negative_prompt""",
							    """height""",
							    """width""",
							    """negative_prompt_embeds""",
							}
							__lowercase:						str							 =      PipelineTesterMixin.required_optional_params - {"""latents"""}
							__lowercase:						Any							 =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""})
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS



							def 						lowerCAmelCase    (			self							: Dict    )   ->Any:



														"""simple docstring"""



														torch.manual_seed(0    )
														snake_case_          =						UNetaDConditionModel(
														    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,    cross_attention_dim=32						,    )
														snake_case_          =						DDIMScheduler(
														    beta_start=0.00_085						,    beta_end=0.012						,    beta_schedule="""scaled_linear"""						,    num_train_timesteps=1_000						,    clip_sample=UpperCAmelCase_						,    set_alpha_to_one=UpperCAmelCase_						,    )
														torch.manual_seed(0    )
														snake_case_          =						AutoencoderKL(
														    block_out_channels=[32, 64]						,    in_channels=3						,    out_channels=3						,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,    latent_channels=4						,    )
														torch.manual_seed(0    )
														snake_case_          =						CLIPTextConfig(
														    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1E-05						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1_000						,    )
														snake_case_          =						CLIPTextModel(UpperCAmelCase_    )
														snake_case_          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

														snake_case_          =						{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """vae""": vae,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """safety_checker""": None,
														    """feature_extractor""": None,
														}
														return components



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: List[str]=0    )   ->str:



														"""simple docstring"""



														snake_case_          =						floats_tensor((1, 3, 32, 32)						,    rng=random.Random(UpperCAmelCase_    )    ).to(UpperCAmelCase_    )
														snake_case_          =						image / 2 + 0.5
														if str(UpperCAmelCase_    ).startswith("""mps"""    ):
																					snake_case_          =						torch.manual_seed(UpperCAmelCase_    )
														else:
																					snake_case_          =						torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
														snake_case_          =						{
														    """prompt""": """An astronaut riding an elephant""",
														    """source_prompt""": """An astronaut riding a horse""",
														    """image""": image,
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """eta""": 0.1,
														    """strength""": 0.8,
														    """guidance_scale""": 3,
														    """source_guidance_scale""": 1,
														    """output_type""": """numpy""",
														}
														return inputs



							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

														snake_case_          =						self.get_dummy_components()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]    )

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



							@unittest.skipIf(torch_device != """cuda"""						,    """This test requires a GPU"""    )
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.get_dummy_components()
														for name, module in components.items():
																					if hasattr(UpperCAmelCase_						,    """half"""    ):
																												snake_case_          =						module.half()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]    )

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



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_save_load_local()



							@unittest.skip("""non-deterministic pipeline"""    )
							def 						lowerCAmelCase    (			self							: int    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_inference_batch_single_identical()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[Any]:



														"""simple docstring"""



														return super().test_dict_tuple_outputs_equivalent()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Any    )   ->Tuple:



														"""simple docstring"""



														return super().test_save_load_optional_components()



							@skip_mps
							def 						lowerCAmelCase    (			self							: List[Any]    )   ->str:



														"""simple docstring"""



														return super().test_attention_slicing_forward_pass()





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





							'''simple docstring'''

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



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->str:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(
														    UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_						,    torch_dtype=torch.floataa						,    revision="""fp16"""    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														# the values aren't exactly equal, but the images look the same visually
														assert np.abs(image - expected_image    ).max() < 5E-1



							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														assert np.abs(image - expected_image    ).max() < 2E-2



