




"""simple docstring"""




from typing import Any






class        __magic_name__				:






				'''simple docstring'''





				def __init__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   data
							lowerCamelCase      =   None





				def __repr__(			self					):





							"""simple docstring"""
							return f'Node({self.data})'






class        __magic_name__				:






				'''simple docstring'''





				def __init__(			self					):





							"""simple docstring"""
							lowerCamelCase      =   None





				def __iter__(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.head
							while node:
										yield node.data
										lowerCamelCase      =   node.next





				def __len__(			self					):





							"""simple docstring"""
							return sum(1 for _ in self					)





				def __repr__(			self					):





							"""simple docstring"""
							return "->".join([str(_UpperCamelCase					) for item in self]					)





				def __getitem__(			self						,					_a					):





							"""simple docstring"""
							if not 0 <= index < len(self					):
										raise ValueError("""list index out of range."""					)
							for i, node in enumerate(self					):
										if i == index:
													return node
							return None





				def __setitem__(			self						,					_a						,					_a					):





							"""simple docstring"""
							if not 0 <= index < len(self					):
										raise ValueError("""list index out of range."""					)
							lowerCamelCase      =   self.head
							for _ in range(_UpperCamelCase					):
										lowerCamelCase      =   current.next
							lowerCamelCase      =   data





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							self.insert_nth(len(self					)						,					_UpperCamelCase					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							self.insert_nth(0						,					_UpperCamelCase					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a					):





							"""simple docstring"""
							if not 0 <= index <= len(self					):
										raise IndexError("""list index out of range"""					)
							lowerCamelCase      =   Node(_UpperCamelCase					)
							if self.head is None:
										lowerCamelCase      =   new_node
							elif index == 0:
										lowerCamelCase      =   self.head  # link new_node to head
										lowerCamelCase      =   new_node
							else:
										lowerCamelCase      =   self.head
										for _ in range(index - 1					):
													lowerCamelCase      =   temp.next
										lowerCamelCase      =   temp.next
										lowerCamelCase      =   new_node





				def   _lowerCAmelCase							(			self					):  # print every node data





							"""simple docstring"""
							print(self					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return self.delete_nth(0					)





				def   _lowerCAmelCase							(			self					):  # delete from tail





							"""simple docstring"""
							return self.delete_nth(len(self					) - 1					)





				def   _lowerCAmelCase							(			self						,					_a = 0					):





							"""simple docstring"""
							if not 0 <= index <= len(self					) - 1:  # test if index is valid
										raise IndexError("""List index out of range."""					)
							lowerCamelCase      =   self.head  # default first node
							if index == 0:
										lowerCamelCase      =   self.head.next
							else:
										lowerCamelCase      =   self.head
										for _ in range(index - 1					):
													lowerCamelCase      =   temp.next
										lowerCamelCase      =   temp.next
										lowerCamelCase      =   temp.next.next
							return delete_node.data





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return self.head is None





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   None
							lowerCamelCase      =   self.head

							while current:
										# Store the current node's next node.
										lowerCamelCase      =   current.next
										# Make the current node's next point backwards
										lowerCamelCase      =   prev
										# Make the previous node be the current node
										lowerCamelCase      =   current
										# Make the current node the next node (to progress iteration)
										lowerCamelCase      =   next_node
							# Return prev in order to put the head at the end
							lowerCamelCase      =   prev




def        a__					(							)		->   None:
			lowerCamelCase      =   LinkedList()
			assert linked_list.is_empty() is True
			assert str(__UpperCamelCase							) == ""

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

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

			for i in range(10							):
						assert len(__UpperCamelCase							) == i
						linked_list.insert_nth(__UpperCamelCase			,					i + 1							)
			assert str(__UpperCamelCase							) == "->".join(str(__UpperCamelCase							) for i in range(1			,					11							)							)

			linked_list.insert_head(0							)
			linked_list.insert_tail(11							)
			assert str(__UpperCamelCase							) == "->".join(str(__UpperCamelCase							) for i in range(0			,					12							)							)

			assert linked_list.delete_head() == 0
			assert linked_list.delete_nth(9							) == 10
			assert linked_list.delete_tail() == 11
			assert len(__UpperCamelCase							) == 9
			assert str(__UpperCamelCase							) == "->".join(str(__UpperCamelCase							) for i in range(1			,					10							)							)

			assert all(linked_list[i] == i + 1 for i in range(0			,					9							)							) is True

			for i in range(0			,					9							):
						lowerCamelCase      =   -i
			assert all(linked_list[i] == -i for i in range(0			,					9							)							) is True

			linked_list.reverse()
			assert str(__UpperCamelCase							) == "->".join(str(__UpperCamelCase							) for i in range(-8			,					1							)							)



def        a__					(							)		->   None:
			lowerCamelCase      =   [
			    -9,
			    1_00,
			    Node(77_34_51_12							),
			    """dlrow olleH""",
			    7,
			    55_55,
			    0,
			    -1_92.5_55_55,
			    """Hello, world!""",
			    77.9,
			    Node(10							),
			    None,
			    None,
			    12.20,
			]
			lowerCamelCase      =   LinkedList()

			for i in test_input:
						linked_list.insert_tail(__UpperCamelCase							)

			# Check if it's empty or not
			assert linked_list.is_empty() is False
			assert (
			    str(__UpperCamelCase							) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
			    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
			)

			# Delete the head
			lowerCamelCase      =   linked_list.delete_head()
			assert result == -9
			assert (
			    str(__UpperCamelCase							) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None->None->12.2"
			)

			# Delete the tail
			lowerCamelCase      =   linked_list.delete_tail()
			assert result == 12.2
			assert (
			    str(__UpperCamelCase							) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None->None"
			)

			# Delete a node in specific location in linked list
			lowerCamelCase      =   linked_list.delete_nth(10							)
			assert result is None
			assert (
			    str(__UpperCamelCase							) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None"
			)

			# Add a Node instance to its head
			linked_list.insert_head(Node("""Hello again, world!"""							)							)
			assert (
			    str(__UpperCamelCase							)
			    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
			    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
			)

			# Add None to its tail
			linked_list.insert_tail(__UpperCamelCase							)
			assert (
			    str(__UpperCamelCase							)
			    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
			    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
			)

			# Reverse the linked list
			linked_list.reverse()
			assert (
			    str(__UpperCamelCase							)
			    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
			    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
			)



def        a__					(							)		->   Dict:
			from doctest import testmod

			testmod()

			lowerCamelCase      =   LinkedList()
			linked_list.insert_head(input("""Inserting 1st at head """							).strip()							)
			linked_list.insert_head(input("""Inserting 2nd at head """							).strip()							)
			print("""\nPrint list:"""							)
			linked_list.print_list()
			linked_list.insert_tail(input("""\nInserting 1st at tail """							).strip()							)
			linked_list.insert_tail(input("""Inserting 2nd at tail """							).strip()							)
			print("""\nPrint list:"""							)
			linked_list.print_list()
			print("""\nDelete head"""							)
			linked_list.delete_head()
			print("""Delete tail"""							)
			linked_list.delete_tail()
			print("""\nPrint list:"""							)
			linked_list.print_list()
			print("""\nReverse linked list"""							)
			linked_list.reverse()
			print("""\nPrint list:"""							)
			linked_list.print_list()
			print("""\nString representation of linked list:"""							)
			print(__UpperCamelCase							)
			print("""\nReading/changing Node data using indexing:"""							)
			print(F'Element at Position 1: {linked_list[1]}'							)
			lowerCamelCase      =   input("""Enter New Value: """							).strip()
			print("""New list:"""							)
			print(__UpperCamelCase							)
			print(F'length of linked_list is : {len(__UpperCamelCase							)}'							)


if __name__ == "__main__":
					main()







'''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_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=CycleDiffusionPipeline
       __UpperCAmelCase					=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           'negative_prompt',
           'height',
           'width',
           'negative_prompt_embeds',
       }
       __UpperCAmelCase					=PipelineTesterMixin.required_optional_params - {'latents'}
       __UpperCAmelCase					=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}	)
       __UpperCAmelCase					=IMAGE_TO_IMAGE_IMAGE_PARAMS
       __UpperCAmelCase					=IMAGE_TO_IMAGE_IMAGE_PARAMS






       def   UpperCamelCase						( self   )->      Dict:
            torch.manual_seed(0   )
            _A				=							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			,     )
            _A				=							DDIMScheduler(
                beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule='scaled_linear'			,     num_train_timesteps=1000			,     clip_sample=_UpperCamelCase			,     set_alpha_to_one=_UpperCamelCase			,     )
            torch.manual_seed(0   )
            _A				=							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   )
            _A				=							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=1000			,     )
            _A				=							CLIPTextModel(_UpperCamelCase   )
            _A				=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

            _A				=							{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'safety_checker': None,
                'feature_extractor': None,
            }
            return components






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

            _A				=							self.get_dummy_components()
            _A				=							CycleDiffusionPipeline(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )
            _A				=							output.images

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

            assert images.shape == (1, 32, 32, 3)
            _A				=							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   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.get_dummy_components()
            for name, module in components.items():
                 if hasattr(_UpperCamelCase			,     'half'   ):
                      _A				=							module.half()
            _A				=							CycleDiffusionPipeline(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )
            _A				=							output.images

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

            assert images.shape == (1, 32, 32, 3)
            _A				=							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   UpperCamelCase						( self   )->      Any:
            return super().test_save_load_local()






       @unittest.skip('non-deterministic pipeline'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            return super().test_inference_batch_single_identical()






       @skip_mps
       def   UpperCamelCase						( self   )->      int:
            return super().test_dict_tuple_outputs_equivalent()






       @skip_mps
       def   UpperCamelCase						( self   )->      Tuple:
            return super().test_save_load_optional_components()






       @skip_mps
       def   UpperCamelCase						( self   )->      Optional[Any]:
            return super().test_attention_slicing_forward_pass()






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






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






       def   UpperCamelCase						( self   )->      int:
            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/cycle-diffusion/black_colored_car.png'   )
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'   )
            _A				=							init_image.resize((512, 512)   )

            _A				=							'CompVis/stable-diffusion-v1-4'
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase			,     subfolder='scheduler'   )
            _A				=							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()

            _A				=							'A black colored car'
            _A				=							'A blue colored car'

            _A				=							torch.manual_seed(0   )
            _A				=							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'			,     )
            _A				=							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   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/cycle-diffusion/black_colored_car.png'   )
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'   )
            _A				=							init_image.resize((512, 512)   )

            _A				=							'CompVis/stable-diffusion-v1-4'
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase			,     subfolder='scheduler'   )
            _A				=							CycleDiffusionPipeline.from_pretrained(_UpperCamelCase			,     scheduler=_UpperCamelCase			,     safety_checker=_UpperCamelCase   )

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

            _A				=							'A black colored car'
            _A				=							'A blue colored car'

            _A				=							torch.manual_seed(0   )
            _A				=							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'			,     )
            _A				=							output.images

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





