


'''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  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					int			      = CycleDiffusionPipeline
	_lowerCamelCase   :					Optional[int]			      = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    """negative_prompt""",
	    """height""",
	    """width""",
	    """negative_prompt_embeds""",
	}
	_lowerCamelCase   :					Optional[int]			      = PipelineTesterMixin.required_optional_params - {"""latents"""}
	_lowerCamelCase   :					Tuple			      = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}							)
	_lowerCamelCase   :					Tuple			      = IMAGE_TO_IMAGE_IMAGE_PARAMS
	_lowerCamelCase   :					str			      = IMAGE_TO_IMAGE_IMAGE_PARAMS



	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    Optional[int]      							= 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					:    str      							= DDIMScheduler(
		    beta_start=0.0_0_0_8_5 ,					beta_end=0.0_1_2 ,					beta_schedule='scaled_linear' ,					num_train_timesteps=1000 ,					clip_sample=snake_case__ ,					set_alpha_to_one=snake_case__ ,					)
		torch.manual_seed(0    )
		lowerCAmelCase					:    List[Any]      							= 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					:    Optional[Any]      							= 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					:    Dict      							= CLIPTextModel(snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

		lowerCAmelCase					:    List[Any]      							= {
		    'unet': unet,
		    'scheduler': scheduler,
		    'vae': vae,
		    'text_encoder': text_encoder,
		    'tokenizer': tokenizer,
		    'safety_checker': None,
		    'feature_extractor': None,
		}
		return components



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

		lowerCAmelCase					:    Union[str, Any]      							= self.get_dummy_components()
		lowerCAmelCase					:    Optional[int]      							= CycleDiffusionPipeline(**snake_case__    )
		lowerCAmelCase					:    str      							= pipe.to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Tuple      							= self.get_dummy_inputs(snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= pipe(**snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= output.images

		lowerCAmelCase					:    Optional[Any]      							= images[0, -3:, -3:, -1]

		assert images.shape == (1, 32, 32, 3)
		lowerCAmelCase					:    Optional[Any]      							= np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]    )

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



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

		lowerCAmelCase					:    Union[str, Any]      							= self.get_dummy_inputs(snake_case__    )
		lowerCAmelCase					:    List[str]      							= pipe(**snake_case__    )
		lowerCAmelCase					:    List[Any]      							= output.images

		lowerCAmelCase					:    Tuple      							= images[0, -3:, -3:, -1]

		assert images.shape == (1, 32, 32, 3)
		lowerCAmelCase					:    Tuple      							= np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]    )

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



	@skip_mps
	def 				lowercase     (					self    ):
		return super().test_save_load_local()



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



	@skip_mps
	def 				lowercase     (					self    ):
		return super().test_dict_tuple_outputs_equivalent()



	@skip_mps
	def 				lowercase     (					self    ):
		return super().test_save_load_optional_components()



	@skip_mps
	def 				lowercase     (					self    ):
		return super().test_attention_slicing_forward_pass()



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



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



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

		lowerCAmelCase					:    List[str]      							= 'CompVis/stable-diffusion-v1-4'
		lowerCAmelCase					:    List[Any]      							= DDIMScheduler.from_pretrained(snake_case__ ,					subfolder='scheduler'    )
		lowerCAmelCase					:    int      							= CycleDiffusionPipeline.from_pretrained(
		    snake_case__ ,					scheduler=snake_case__ ,					safety_checker=snake_case__ ,					torch_dtype=torch.floataa ,					revision='fp16'    )

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

		lowerCAmelCase					:    Optional[Any]      							= 'A black colored car'
		lowerCAmelCase					:    Union[str, Any]      							= 'A blue colored car'

		lowerCAmelCase					:    Dict      							= torch.manual_seed(0    )
		lowerCAmelCase					:    Any      							= pipe(
		    prompt=snake_case__ ,					source_prompt=snake_case__ ,					image=snake_case__ ,					num_inference_steps=100 ,					eta=0.1 ,					strength=0.8_5 ,					guidance_scale=3 ,					source_guidance_scale=1 ,					generator=snake_case__ ,					output_type='np' ,					)
		lowerCAmelCase					:    Dict      							= 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    ):
		lowerCAmelCase					:    Optional[int]      							= load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/cycle-diffusion/black_colored_car.png'    )
		lowerCAmelCase					:    str      							= load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'    )
		lowerCAmelCase					:    Union[str, Any]      							= init_image.resize((512, 512)    )

		lowerCAmelCase					:    str      							= 'CompVis/stable-diffusion-v1-4'
		lowerCAmelCase					:    Optional[int]      							= DDIMScheduler.from_pretrained(snake_case__ ,					subfolder='scheduler'    )
		lowerCAmelCase					:    Optional[Any]      							= CycleDiffusionPipeline.from_pretrained(snake_case__ ,					scheduler=snake_case__ ,					safety_checker=snake_case__    )

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

		lowerCAmelCase					:    Tuple      							= 'A black colored car'
		lowerCAmelCase					:    List[Any]      							= 'A blue colored car'

		lowerCAmelCase					:    Union[str, Any]      							= torch.manual_seed(0    )
		lowerCAmelCase					:    Dict      							= pipe(
		    prompt=snake_case__ ,					source_prompt=snake_case__ ,					image=snake_case__ ,					num_inference_steps=100 ,					eta=0.1 ,					strength=0.8_5 ,					guidance_scale=3 ,					source_guidance_scale=1 ,					generator=snake_case__ ,					output_type='np' ,					)
		lowerCAmelCase					:    Dict      							= output.images

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




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				List[str]				)    ->      Optional[Any]:




	"""simple docstring"""





	if not head:
		return True
	# split the list to two parts
	lowerCAmelCase							,   lowerCAmelCase					:    str      							= head.next, head
	while fast and fast.next:
		lowerCAmelCase					:    Optional[int]      							= fast.next.next
		lowerCAmelCase					:    int      							= slow.next
	lowerCAmelCase					:    int      							= slow.next
	lowerCAmelCase					:    Optional[Any]      							= None  # Don't forget here! But forget still works!
	# reverse the second part
	lowerCAmelCase					:    List[Any]      							= None
	while second:
		lowerCAmelCase					:    List[Any]      							= second.next
		lowerCAmelCase					:    Union[str, Any]      							= node
		lowerCAmelCase					:    Optional[Any]      							= second
		lowerCAmelCase					:    Any      							= nxt
	# compare two parts
	# second part has the same or one less node
	while node:
		if node.val != head.val:
			return False
		lowerCAmelCase					:    Optional[Any]      							= node.next
		lowerCAmelCase					:    Tuple      							= head.next
	return True

def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      Optional[int]:




	"""simple docstring"""





	if not head or not head.next:
		return True

	# 1. Get the midpoint (slow)
	lowerCAmelCase					:    Optional[int]      							= head
	while fast and fast.next:
		lowerCAmelCase							,   lowerCAmelCase					:    Optional[Any]      							= fast.next.next, slow.next

	# 2. Push the second half into the stack
	lowerCAmelCase					:    Tuple      							= [slow.val]
	while slow.next:
		lowerCAmelCase					:    Tuple      							= slow.next
		stack.append(slow.val				)

	# 3. Comparison
	while stack:
		if stack.pop() != cur.val:
			return False
		lowerCAmelCase					:    Union[str, Any]      							= cur.next

	return True






def 			__UpperCamelCase    (    _A    :				Tuple				)    ->      Optional[int]:




	"""simple docstring"""





	if not head or not head.next:
		return True
	lowerCAmelCase					:    Optional[int]      							= {}
	lowerCAmelCase					:    int      							= 0
	while head:
		if head.val in d:
			d[head.val].append(_A				)
		else:
			lowerCAmelCase					:    Any      							= [pos]
		lowerCAmelCase					:    int      							= head.next
		pos += 1
	lowerCAmelCase					:    str      							= pos - 1
	lowerCAmelCase					:    Optional[Any]      							= 0
	for v in d.values():
		if len(_A				) % 2 != 0:
			middle += 1
		else:
			lowerCAmelCase					:    Any      							= 0
			for i in range(0					,   len(_A				)				):
				if v[i] + v[len(_A				) - 1 - step] != checksum:
					return False
				step += 1
		if middle > 1:
			return False
	return True
