





"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterator
from typing import Any
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :Any			):
											__lowerCamelCase      :      Any         =data
											__lowerCamelCase      :      Node | None         =None

class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =None
											__lowerCamelCase      :      List[str]         =None




				def __iter__(     self  :Optional[Any]			):
											__lowerCamelCase      :      Union[str, Any]         =self.head
											while self.head:
																		yield node.data
																		__lowerCamelCase      :      Union[str, Any]         =node.next
																		if node == self.head:
																									break




				def __len__(     self  :Optional[Any]			):
											return sum(1 for _ in self			)




				def __repr__(     self  :int			):
											return "->".join(str(__lowercase			) for item in iter(self			)			)




				def 					__lowercase  (     self  :str						,    __lowercase  :Any			):
											self.insert_nth(len(self			)						,    __lowercase			)




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :Any			):
											self.insert_nth(0						,    __lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :int						,    __lowercase  :Any			):
											if index < 0 or index > len(self			):
																		raise IndexError('''list index out of range.'''			)
											__lowerCamelCase      :      Tuple         =Node(__lowercase			)
											if self.head is None:
																		__lowerCamelCase      :      Optional[int]         =new_node  # first node points itself
																		__lowerCamelCase      :      int         =new_node
											elif index == 0:  # insert at head
																		__lowerCamelCase      :      Any         =self.head
																		__lowerCamelCase      :      List[Any]         =new_node
											else:
																		__lowerCamelCase      :      Optional[int]         =self.head
																		for _ in range(index - 1			):
																									__lowerCamelCase      :      Any         =temp.next
																		__lowerCamelCase      :      List[str]         =temp.next
																		__lowerCamelCase      :      Union[str, Any]         =new_node
																		if index == len(self			) - 1:  # insert at tail
																									__lowerCamelCase      :      Union[str, Any]         =new_node




				def 					__lowercase  (     self  :Tuple			):
											return self.delete_nth(0			)




				def 					__lowercase  (     self  :Dict			):
											return self.delete_nth(len(self			) - 1			)




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :int = 0			):
											if not 0 <= index < len(self			):
																		raise IndexError('''list index out of range.'''			)
											__lowerCamelCase      :      List[Any]         =self.head
											if self.head == self.tail:  # just one node
																		__lowerCamelCase      :      Dict         =None
											elif index == 0:  # delete head node
																		__lowerCamelCase      :      Optional[Any]         =self.tail.next.next
																		__lowerCamelCase      :      Dict         =self.head.next
											else:
																		__lowerCamelCase      :      str         =self.head
																		for _ in range(index - 1			):
																									__lowerCamelCase      :      Optional[Any]         =temp.next
																		__lowerCamelCase      :      List[str]         =temp.next
																		__lowerCamelCase      :      str         =temp.next.next
																		if index == len(self			) - 1:  # delete at tail
																									__lowerCamelCase      :      Optional[int]         =temp
											return delete_node.data




				def 					__lowercase  (     self  :int			):
											return len(self			) == 0



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =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 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Dict	 =		CycleDiffusionPipeline
				__snake_case   :       Optional[int]	 =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    """negative_prompt""",
				    """height""",
				    """width""",
				    """negative_prompt_embeds""",
				}
				__snake_case   :       Any	 =		PipelineTesterMixin.required_optional_params - {"""latents"""}
				__snake_case   :       int	 =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
				__snake_case   :       Optional[int]	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				__snake_case   :       str	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 					__lowercase  (     self  :Dict			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      List[str]         =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						,    )
											__lowerCamelCase      :      List[Any]         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    num_train_timesteps=1000						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =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			)
											__lowerCamelCase      :      int         =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						,    )
											__lowerCamelCase      :      Optional[Any]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Tuple         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :Optional[int]						,    __lowercase  :str=0			):
											__lowerCamelCase      :      List[Any]         =floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__lowercase			)			).to(__lowercase			)
											__lowerCamelCase      :      str         =image / 2 + 0.5
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Union[str, Any]         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      Any         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''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 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      int         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      Tuple         =self.get_dummy_components()
											__lowerCamelCase      :      List[str]         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      Tuple         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      int         =pipe(**__lowercase			)
											__lowerCamelCase      :      Dict         =output.images

											__lowerCamelCase      :      Union[str, Any]         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]			)

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




				@unittest.skipIf(torch_device != '''cuda'''						,    '''This test requires a GPU'''			)
				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      str         =self.get_dummy_components()
											for name, module in components.items():
																		if hasattr(__lowercase						,    '''half'''			):
																									__lowerCamelCase      :      Union[str, Any]         =module.half()
											__lowerCamelCase      :      int         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      List[str]         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Optional[int]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      Dict         =pipe(**__lowercase			)
											__lowerCamelCase      :      List[str]         =output.images

											__lowerCamelCase      :      Dict         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      str         =np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]			)

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




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_save_load_local()




				@unittest.skip('''non-deterministic pipeline'''			)
				def 					__lowercase  (     self  :Dict			):
											return super().test_inference_batch_single_identical()




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def 					__lowercase  (     self  :str			):
											return super().test_save_load_optional_components()




				@skip_mps
				def 					__lowercase  (     self  :Dict			):
											return super().test_attention_slicing_forward_pass()

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




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




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Dict         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      Union[str, Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
											__lowerCamelCase      :      Any         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Optional[Any]         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      Optional[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      Optional[Any]         =CycleDiffusionPipeline.from_pretrained(
											    __lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase						,    torch_dtype=torch.floataa						,    revision='''fp16'''			)

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

											__lowerCamelCase      :      Dict         ='''A black colored car'''
											__lowerCamelCase      :      Union[str, Any]         ='''A blue colored car'''

											__lowerCamelCase      :      Dict         =torch.manual_seed(0			)
											__lowerCamelCase      :      Tuple         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Tuple         =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 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      List[Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
											__lowerCamelCase      :      Optional[Any]         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Any         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      List[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      str         =CycleDiffusionPipeline.from_pretrained(__lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase			)

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

											__lowerCamelCase      :      Any         ='''A black colored car'''
											__lowerCamelCase      :      int         ='''A blue colored car'''

											__lowerCamelCase      :      Tuple         =torch.manual_seed(0			)
											__lowerCamelCase      :      Union[str, Any]         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Dict         =output.images

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




