




from typing import Any






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   str		,					_lowerCamelCase:   Any		):
											SCREAMING_SNAKE_CASE_   	=    data
											SCREAMING_SNAKE_CASE_   	=    None





				def __repr__(							self:   int		):
											return f"Node({self.data})"






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    None


				def __iter__(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    self.head
											while node:
																		yield node.data
																		SCREAMING_SNAKE_CASE_   	=    node.next


				def __len__(							self:   int		):
											return sum(1 for _ in self		)


				def __repr__(							self:   Optional[int]		):
											return "->".join([str(__A		) for item in self]		)


				def __getitem__(							self:   int		,					_lowerCamelCase:   int		):
											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:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Any		):
											if not 0 <= index < len(self		):
																		raise ValueError('''list index out of range.'''		)
											SCREAMING_SNAKE_CASE_   	=    self.head
											for _ in range(__A		):
																		SCREAMING_SNAKE_CASE_   	=    current.next
											SCREAMING_SNAKE_CASE_   	=    data


				def     _A							(							self:   int		,					_lowerCamelCase:   Any		):
											self.insert_nth(len(self		)		,					__A		)


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   Any		):
											self.insert_nth(0		,					__A		)


				def     _A							(							self:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Any		):
											if not 0 <= index <= len(self		):
																		raise IndexError('''list index out of range'''		)
											SCREAMING_SNAKE_CASE_   	=    Node(__A		)
											if self.head is None:
																		SCREAMING_SNAKE_CASE_   	=    new_node
											elif index == 0:
																		SCREAMING_SNAKE_CASE_   	=    self.head  # link new_node to head
																		SCREAMING_SNAKE_CASE_   	=    new_node
											else:
																		SCREAMING_SNAKE_CASE_   	=    self.head
																		for _ in range(index - 1		):
																									SCREAMING_SNAKE_CASE_   	=    temp.next
																		SCREAMING_SNAKE_CASE_   	=    temp.next
																		SCREAMING_SNAKE_CASE_   	=    new_node


				def     _A							(							self:   Union[str, Any]		):  # print every node data
											print(self		)


				def     _A							(							self:   Dict		):
											return self.delete_nth(0		)


				def     _A							(							self:   Tuple		):  # delete from tail
											return self.delete_nth(len(self		) - 1		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   int = 0		):
											if not 0 <= index <= len(self		) - 1:  # test if index is valid
																		raise IndexError('''List index out of range.'''		)
											SCREAMING_SNAKE_CASE_   	=    self.head  # default first node
											if index == 0:
																		SCREAMING_SNAKE_CASE_   	=    self.head.next
											else:
																		SCREAMING_SNAKE_CASE_   	=    self.head
																		for _ in range(index - 1		):
																									SCREAMING_SNAKE_CASE_   	=    temp.next
																		SCREAMING_SNAKE_CASE_   	=    temp.next
																		SCREAMING_SNAKE_CASE_   	=    temp.next.next
											return delete_node.data


				def     _A							(							self:   Any		):
											return self.head is None





				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    self.head

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






def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    LinkedList()
							assert linked_list.is_empty() is True
							assert str(_lowercase     ) == ""

							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(1_0     ):
														assert len(_lowercase     ) == i
														linked_list.insert_nth(_lowercase  ,    i + 1     )
							assert str(_lowercase     ) == "->".join(str(_lowercase     ) for i in range(1  ,    1_1     )     )

							linked_list.insert_head(0     )
							linked_list.insert_tail(1_1     )
							assert str(_lowercase     ) == "->".join(str(_lowercase     ) for i in range(0  ,    1_2     )     )

							assert linked_list.delete_head() == 0
							assert linked_list.delete_nth(9     ) == 1_0
							assert linked_list.delete_tail() == 1_1
							assert len(_lowercase     ) == 9
							assert str(_lowercase     ) == "->".join(str(_lowercase     ) for i in range(1  ,    1_0     )     )

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

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

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

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    [
							    -9,
							    1_0_0,
							    Node(7_7_3_4_5_1_1_2     ),
							    '''dlrow olleH''',
							    7,
							    5_5_5_5,
							    0,
							    -1_9_2.5_5_5_5_5,
							    '''Hello, world!''',
							    77.9,
							    Node(1_0     ),
							    None,
							    None,
							    12.20,
							]
							SCREAMING_SNAKE_CASE_   	=    LinkedList()

							for i in test_input:
														linked_list.insert_tail(_lowercase     )

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

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

							# Delete the tail
							SCREAMING_SNAKE_CASE_   	=    linked_list.delete_tail()
							assert result == 12.2
							assert (
							    str(_lowercase     ) == "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
							SCREAMING_SNAKE_CASE_   	=    linked_list.delete_nth(1_0     )
							assert result is None
							assert (
							    str(_lowercase     ) == "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(_lowercase     )
							    == "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(_lowercase     )
							assert (
							    str(_lowercase     )
							    == "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(_lowercase     )
							    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
							    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
							)

def 			a   ():
							from doctest import testmod

							testmod()

							SCREAMING_SNAKE_CASE_   	=    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(_lowercase     )
							print('''\nReading/changing Node data using indexing:'''     )
							print(F"Element at Position 1: {linked_list[1]}"     )
							SCREAMING_SNAKE_CASE_   	=    input('''Enter New Value: '''     ).strip()
							print('''New list:'''     )
							print(_lowercase     )
							print(F"length of linked_list is : {len(_lowercase     )}"     )


if __name__ == "__main__":
			main()








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					ShapEImgaImgPipeline
				SCREAMING_SNAKE_CASE__					:							Dict	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False


				@property
				def     _A							(							self:   Optional[Any]		):
											return 32


				@property
				def     _A							(							self:   Optional[int]		):
											return 32


				@property
				def     _A							(							self:   List[Any]		):
											return self.time_input_dim * 4


				@property
				def     _A							(							self:   Any		):
											return 8


				@property
				def     _A							(							self:   int		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size		,					image_size=64		,					projection_dim=self.text_embedder_hidden_size		,					intermediate_size=37		,					num_attention_heads=4		,					num_channels=3		,					num_hidden_layers=5		,					patch_size=1		,					)

											SCREAMING_SNAKE_CASE_   	=    CLIPVisionModel(_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPImageProcessor(
											    crop_size=2_24		,					do_center_crop=_lowerCamelCase		,					do_normalize=_lowerCamelCase		,					do_resize=_lowerCamelCase		,					image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,					image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,					resample=3		,					size=2_24		,					)

											return image_processor


				@property
				def     _A							(							self:   Dict		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''num_attention_heads''': 2,
											    '''attention_head_dim''': 16,
											    '''embedding_dim''': self.time_input_dim,
											    '''num_embeddings''': 32,
											    '''embedding_proj_dim''': self.text_embedder_hidden_size,
											    '''time_embed_dim''': self.time_embed_dim,
											    '''num_layers''': 1,
											    '''clip_embed_dim''': self.time_input_dim * 2,
											    '''additional_embeddings''': 0,
											    '''time_embed_act_fn''': '''gelu''',
											    '''norm_in_type''': '''layer''',
											    '''embedding_proj_norm_type''': '''layer''',
											    '''encoder_hid_proj_type''': None,
											    '''added_emb_type''': None,
											}

											SCREAMING_SNAKE_CASE_   	=    PriorTransformer(**_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''param_shapes''': (
											        (self.renderer_dim, 93),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											    ),
											    '''d_latent''': self.time_input_dim,
											    '''d_hidden''': self.renderer_dim,
											    '''n_output''': 12,
											    '''background''': (
											        0.1,
											        0.1,
											        0.1,
											    ),
											}
											SCREAMING_SNAKE_CASE_   	=    ShapERenderer(**_lowerCamelCase		)
											return model


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.dummy_prior
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_encoder
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_processor
											SCREAMING_SNAKE_CASE_   	=    self.dummy_renderer

											SCREAMING_SNAKE_CASE_   	=    HeunDiscreteScheduler(
											    beta_schedule='''exp'''		,					num_train_timesteps=10_24		,					prediction_type='''sample'''		,					use_karras_sigmas=_lowerCamelCase		,					clip_sample=_lowerCamelCase		,					clip_sample_range=1.0		,					)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prior''': prior,
											    '''image_encoder''': image_encoder,
											    '''image_processor''': image_processor,
											    '''renderer''': renderer,
											    '''scheduler''': scheduler,
											}

											return components


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, 3, 64, 64)		,					rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)

											if str(_lowerCamelCase		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''image''': input_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 1,
											    '''frame_size''': 32,
											    '''output_type''': '''np''',
											}
											return inputs


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()

											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)

											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    pipe(**self.get_dummy_inputs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    output.images[0]
											SCREAMING_SNAKE_CASE_   	=    image[0, -3:, -3:, -1]

											assert image.shape == (20, 32, 32, 3)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											    ]		)

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


				def     _A							(							self:   str		):
											# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
											self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    torch_device == '''cpu'''
											SCREAMING_SNAKE_CASE_   	=    True
											self._test_inference_batch_single_identical(
											    batch_size=2		,					test_max_difference=_lowerCamelCase		,					relax_max_difference=_lowerCamelCase		,					)





				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    1
											SCREAMING_SNAKE_CASE_   	=    2

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(_lowerCamelCase		)

											for key in inputs.keys():
																		if key in self.batch_params:
																									SCREAMING_SNAKE_CASE_   	=    batch_size * [inputs[key]]

											SCREAMING_SNAKE_CASE_   	=    pipe(**_lowerCamelCase		,					num_images_per_prompt=_lowerCamelCase		)[0]

											assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







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





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''		)
											SCREAMING_SNAKE_CASE_   	=    load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/shap_e/test_shap_e_img2img_out.npy'''		)
											SCREAMING_SNAKE_CASE_   	=    ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    pipe(
											    _lowerCamelCase		,					generator=_lowerCamelCase		,					guidance_scale=3.0		,					num_inference_steps=64		,					frame_size=64		,					output_type='''np'''		,					).images[0]

											assert images.shape == (20, 64, 64, 3)

											assert_mean_pixel_difference(_lowerCamelCase		,					_lowerCamelCase		)


