



from typing import Any

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Any							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase             =			data
							__lowercase             =			None



						def __repr__(	self	:							List[Any]	):






							'''simple docstring'''



							return f"""Node({self.data})"""

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase             =			None



						def __iter__(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase             =			self.head
							while node:
								yield node.data
								__lowercase             =			node.next



						def __len__(	self	:							Dict	):






							'''simple docstring'''



							return sum(1 for _ in self	)



						def __repr__(	self	:							str	):






							'''simple docstring'''



							return "->".join([str(lowerCamelCase_	) for item in self]	)



						def __getitem__(	self	:							Optional[Any]							,      lowerCamelCase	:							int	):






							'''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	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							self.insert_nth(len(self	)							,      lowerCamelCase_	)



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							self.insert_nth(0							,      lowerCamelCase_	)



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Optional[Any]	):  # print every node data






							'''simple docstring'''



							print(self	)



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							return self.delete_nth(0	)



						def     _snake_case	(	self	:							List[Any]	):  # delete from tail






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							int = 0	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							return self.head is None



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase             =			None
							__lowercase             =			self.head

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



def    snake_case_				(    ):
	__lowercase             =			LinkedList()
	assert linked_list.is_empty() is True
	assert str(lowerCamelCase_   ) == ""

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

	linked_list.insert_head(0   )
	linked_list.insert_tail(1_1   )
	assert str(lowerCamelCase_   ) == "->".join(str(lowerCamelCase_   ) 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(lowerCamelCase_   ) == 9
	assert str(lowerCamelCase_   ) == "->".join(str(lowerCamelCase_   ) 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   ):
		__lowercase             =			-i
	assert all(linked_list[i] == -i for i in range(0  ,	9   )   ) is True

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



def    snake_case_				(    ):
	__lowercase             =			[
	    -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!""",
	    7_7.9,
	    Node(1_0   ),
	    None,
	    None,
	    1_2.2_0,
	]
	__lowercase             =			LinkedList()

	for i in test_input:
		linked_list.insert_tail(lowerCamelCase_   )

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

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

	# Delete the tail
	__lowercase             =			linked_list.delete_tail()
	assert result == 1_2.2
	assert (
	    str(lowerCamelCase_   ) == "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
	__lowercase             =			linked_list.delete_nth(1_0   )
	assert result is None
	assert (
	    str(lowerCamelCase_   ) == "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(lowerCamelCase_   )
	    == "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(lowerCamelCase_   )
	assert (
	    str(lowerCamelCase_   )
	    == "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(lowerCamelCase_   )
	    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
	    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
	)



def    snake_case_				(    ):
	from doctest import testmod

	testmod()

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


if __name__ == "__main__":
	main()








import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class   _A   :


						'''simple docstring'''





						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			UNetaDConditionModel(
							    sample_size=32							,      layers_per_block=1							,      block_out_channels=[32, 64]							,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]							,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,      in_channels=3							,      out_channels=6							,      cross_attention_dim=32							,      encoder_hid_dim=32							,      attention_head_dim=8							,      addition_embed_type="text"							,      addition_embed_type_num_heads=2							,      cross_attention_norm="group_norm"							,      resnet_time_scale_shift="scale_shift"							,      act_fn="gelu"							,      )
							unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      thresholding=lowerCamelCase							,      dynamic_thresholding_ratio=0.95							,      sample_max_value=1.0							,      prediction_type="epsilon"							,      variance_type="learned_range"							,      )

							torch.manual_seed(0	)
							__lowercase		       =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			UNetaDConditionModel(
							    sample_size=32							,      layers_per_block=[1, 2]							,      block_out_channels=[32, 64]							,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]							,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,      in_channels=6							,      out_channels=6							,      cross_attention_dim=32							,      encoder_hid_dim=32							,      attention_head_dim=8							,      addition_embed_type="text"							,      addition_embed_type_num_heads=2							,      cross_attention_norm="group_norm"							,      resnet_time_scale_shift="scale_shift"							,      act_fn="gelu"							,      class_embed_type="timestep"							,      mid_block_scale_factor=1.414							,      time_embedding_act_fn="gelu"							,      time_embedding_dim=32							,      )
							unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      thresholding=lowerCamelCase							,      dynamic_thresholding_ratio=0.95							,      sample_max_value=1.0							,      prediction_type="epsilon"							,      variance_type="learned_range"							,      )

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      )

							torch.manual_seed(0	)
							__lowercase		       =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			self.pipeline_class(**lowerCamelCase	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)

							__lowercase		       =			inputs["prompt"]
							__lowercase		       =			inputs["generator"]
							__lowercase		       =			inputs["num_inference_steps"]
							__lowercase		       =			inputs["output_type"]

							if "image" in inputs:
								__lowercase		       =			inputs["image"]
							else:
								__lowercase		       =			None

							if "mask_image" in inputs:
								__lowercase		       =			inputs["mask_image"]
							else:
								__lowercase		       =			None

							if "original_image" in inputs:
								__lowercase		       =			inputs["original_image"]
							else:
								__lowercase		       =			None

							__lowercase       ,    __lowercase		       =			pipe.encode_prompt(lowerCamelCase	)

							# inputs with prompt converted to embeddings
							__lowercase		       =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								__lowercase		       =			image

							if mask_image is not None:
								__lowercase		       =			mask_image

							if original_image is not None:
								__lowercase		       =			original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
								setattr(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			pipe(**lowerCamelCase	)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(lowerCamelCase	)
								__lowercase		       =			self.pipeline_class.from_pretrained(lowerCamelCase	)
								pipe_loaded.to(lowerCamelCase	)
								pipe_loaded.set_progress_bar_config(disable=lowerCamelCase	)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							for optional_component in pipe._optional_components:
								self.assertTrue(
								    getattr(lowerCamelCase							,      lowerCamelCase	) is None							,      f"""`{optional_component}` did not stay set to None after loading."""							,      )

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)

							__lowercase		       =			inputs["generator"]
							__lowercase		       =			inputs["num_inference_steps"]
							__lowercase		       =			inputs["output_type"]

							# inputs with prompt converted to embeddings
							__lowercase		       =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								__lowercase		       =			image

							if mask_image is not None:
								__lowercase		       =			mask_image

							if original_image is not None:
								__lowercase		       =			original_image

							__lowercase		       =			pipe_loaded(**lowerCamelCase	)[0]

							__lowercase		       =			np.abs(to_np(lowerCamelCase	) - to_np(lowerCamelCase	)	).max()
							self.assertLess(lowerCamelCase							,      1e-4	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			self.pipeline_class(**lowerCamelCase	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			pipe(**lowerCamelCase	)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(lowerCamelCase	)
								__lowercase		       =			self.pipeline_class.from_pretrained(lowerCamelCase	)
								pipe_loaded.to(lowerCamelCase	)
								pipe_loaded.set_progress_bar_config(disable=lowerCamelCase	)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			pipe_loaded(**lowerCamelCase	)[0]

							__lowercase		       =			np.abs(to_np(lowerCamelCase	) - to_np(lowerCamelCase	)	).max()
							self.assertLess(lowerCamelCase							,      1e-4	)



