


'''simple docstring'''
from typing import Any

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Optional[Any]       =			data
  __A  :		Any       =			None







 def __repr__(      self						):

  """simple docstring"""







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


class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self						):

  """simple docstring"""







  __A  :		int       =			None


 def __iter__(      self						):

  """simple docstring"""







  __A  :		List[Any]       =			self.head
  while node:
   yield node.data
   __A  :		int       =			node.next


 def __len__(      self						):

  """simple docstring"""







  return sum(1 for _ in self						)


 def __repr__(      self						):

  """simple docstring"""







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


 def __getitem__(      self      ,      __lowercase						):

  """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      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if not 0 <= index < len(self						):
   raise ValueError('list index out of range.'						)
  __A  :		List[str]       =			self.head
  for _ in range(__lowercase						):
   __A  :		Dict       =			current.next
  __A  :		str       =			data


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  self.insert_nth(len(self						)      ,      __lowercase						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  self.insert_nth(0      ,      __lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if not 0 <= index <= len(self						):
   raise IndexError('list index out of range'						)
  __A  :		Optional[int]       =			Node(__lowercase						)
  if self.head is None:
   __A  :		Tuple       =			new_node
  elif index == 0:
   __A  :		str       =			self.head  # link new_node to head
   __A  :		List[Any]       =			new_node
  else:
   __A  :		str       =			self.head
   for _ in range(index - 1						):
    __A  :		Tuple       =			temp.next
   __A  :		Optional[Any]       =			temp.next
   __A  :		int       =			new_node


 def  snake_case__   (      self						):  # print every node data

  """simple docstring"""







  print(self						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.delete_nth(0						)


 def  snake_case__   (      self						):  # delete from tail

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase = 0						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.head is None







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			None
  __A  :		Any       =			self.head

  while current:
   # Store the current node's next node.
   __A  :		int       =			current.next
   # Make the current node's next point backwards
   __A  :		Optional[int]       =			prev
   # Make the previous node be the current node
   __A  :		Dict       =			current
   # Make the current node the next node (to progress iteration)
   __A  :		Tuple       =			next_node
  # Return prev in order to put the head at the end
  __A  :		int       =			prev


def 			_lowercase       (				):
 __A  :		Optional[int]       =			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			):
  __A  :		int       =			-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 			_lowercase       (				):
 __A  :		Optional[int]       =			[
     -9,
     100,
     Node(77345112			),
     'dlrow olleH',
     7,
     5555,
     0,
     -192.55555,
     'Hello, world!',
     77.9,
     Node(10			),
     None,
     None,
     12.20,
 ]
 __A  :		Optional[Any]       =			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
 __A  :		str       =			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
 __A  :		Optional[int]       =			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
 __A  :		str       =			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 			_lowercase       (				):
 from doctest import testmod

 testmod()

 __A  :		Union[str, Any]       =			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]}"""			)
 __A  :		Optional[int]       =			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 unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Optional[int]       =			UNetaDModel(
      sample_size=(32, 64)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('AttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'AttnUpBlock2D')      ,      )
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Dict       =			UNetaDConditionModel(
      sample_size=(64, 32)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')      ,      cross_attention_dim=10      ,      )
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Union[str, Any]       =			AutoencoderKL(
      sample_size=(128, 64)      ,      in_channels=1      ,      out_channels=1      ,      latent_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')      ,      up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')      ,      )
  __A  :		Union[str, Any]       =			UNetaDModel(
      sample_size=(64, 32)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('AttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'AttnUpBlock2D')      ,      )
  return vqvae, unet







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
  __A  :		int       =			Mel(
      x_res=self.dummy_unet.config.sample_size[1]      ,      y_res=self.dummy_unet.config.sample_size[0]      ,      )

  __A  :		Tuple       =			DDPMScheduler()
  __A  :		Optional[int]       =			AudioDiffusionPipeline(vqvae=__lowercase      ,      unet=self.dummy_unet      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Tuple       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Tuple       =			pipe(generator=__lowercase      ,      steps=4						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		Dict       =			output.images[0]

  __A  :		Optional[Any]       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[int]       =			pipe(generator=__lowercase      ,      steps=4      ,      return_dict=__lowercase						)
  __A  :		int       =			output[0][0]

  assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
  assert (
      image.height == self.dummy_unet.config.sample_size[0]
      and image.width == self.dummy_unet.config.sample_size[1]
  )
  __A  :		Optional[int]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[Any]       =			np.frombuffer(image_from_tuple.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0
  assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() == 0

  __A  :		Optional[int]       =			Mel(
      x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]      ,      y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]      ,      )

  __A  :		Optional[Any]       =			DDIMScheduler()
  __A  :		List[Any]       =			self.dummy_vqvae_and_unet
  __A  :		int       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=dummy_vqvae_and_unet[1]      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			np.random.uniform(-1      ,      1      ,      ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
  __A  :		Dict       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		List[Any]       =			pipe(raw_audio=__lowercase      ,      generator=__lowercase      ,      start_step=5      ,      steps=10						)
  __A  :		Tuple       =			output.images[0]

  assert (
      image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
      and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
  )
  __A  :		int       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[str]       =			np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0

  __A  :		Any       =			self.dummy_unet_condition
  __A  :		Optional[int]       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=__lowercase      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Union[str, Any]       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			torch.rand((1, 1, 10)						)
  __A  :		Any       =			pipe(generator=__lowercase      ,      encoding=__lowercase						)
  __A  :		Union[str, Any]       =			output.images[0]
  __A  :		List[Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		int       =			np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0


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




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			torch_device

  __A  :		Union[str, Any]       =			DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Any       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[Any]       =			pipe(generator=__lowercase						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		List[Any]       =			output.images[0]

  assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
  assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
  __A  :		Union[str, Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0






