
"""simple docstring"""






import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__=1			)	->Dict:
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split("."			)[n_shave_prefix_segments:]			)
			else:
						return ".".join(path.split("."			)[:n_shave_prefix_segments]			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__=0			)	->Dict:
			__lowercase						      =					[]
			for old_item in old_list:
						__lowercase						      =					old_item
						__lowercase						      =					new_item.replace("block." ,      "resnets."			)
						__lowercase						      =					new_item.replace("conv_shorcut" ,      "conv1"			)
						__lowercase						      =					new_item.replace("in_shortcut" ,      "conv_shortcut"			)
						__lowercase						      =					new_item.replace("temb_proj" ,      "time_emb_proj"			)

						__lowercase						      =					shave_segments(_SCREAMING_SNAKE_CASE ,      n_shave_prefix_segments=_SCREAMING_SNAKE_CASE			)

						mapping.append({"old": old_item, "new": new_item}			)

			return mapping



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__=0 ,      __magic_name__=False			)	->Tuple:
			__lowercase						      =					[]
			for old_item in old_list:
						__lowercase						      =					old_item

						# In `model.mid`, the layer is called `attn`.
						if not in_mid:
									__lowercase						      =					new_item.replace("attn" ,      "attentions"			)
						__lowercase						      =					new_item.replace(".k." ,      ".key."			)
						__lowercase						      =					new_item.replace(".v." ,      ".value."			)
						__lowercase						      =					new_item.replace(".q." ,      ".query."			)

						__lowercase						      =					new_item.replace("proj_out" ,      "proj_attn"			)
						__lowercase						      =					new_item.replace("norm" ,      "group_norm"			)

						__lowercase						      =					shave_segments(_SCREAMING_SNAKE_CASE ,      n_shave_prefix_segments=_SCREAMING_SNAKE_CASE			)
						mapping.append({"old": old_item, "new": new_item}			)

			return mapping



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__=None ,      __magic_name__=None ,      __magic_name__=None			)	->Union[str, Any]:
			assert isinstance(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE			), "Paths should be a list of dicts containing 'old' and 'new' keys."

			if attention_paths_to_split is not None:
						if config is None:
									raise ValueError("Please specify the config if setting \'attention_paths_to_split\' to \'True\'."			)

						for path, path_map in attention_paths_to_split.items():
									__lowercase						      =					old_checkpoint[path]
									__lowercase						      =					old_tensor.shape[0] // 3

									__lowercase						      =					(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

									__lowercase						      =					old_tensor.shape[0] // config.get("num_head_channels" ,      1			) // 3

									__lowercase						      =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
									__lowercase						      =					old_tensor.split(channels // num_heads ,      dim=1			)

									__lowercase						      =					query.reshape(_SCREAMING_SNAKE_CASE			).squeeze()
									__lowercase						      =					key.reshape(_SCREAMING_SNAKE_CASE			).squeeze()
									__lowercase						      =					value.reshape(_SCREAMING_SNAKE_CASE			).squeeze()

			for path in paths:
						__lowercase						      =					path['new']

						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						__lowercase						      =					new_path.replace("down." ,      "down_blocks."			)
						__lowercase						      =					new_path.replace("up." ,      "up_blocks."			)

						if additional_replacements is not None:
									for replacement in additional_replacements:
												__lowercase						      =					new_path.replace(replacement["old"] ,      replacement["new"]			)

						if "attentions" in new_path:
									__lowercase						      =					old_checkpoint[path['old']].squeeze()
						else:
									__lowercase						      =					old_checkpoint[path['old']]



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->Tuple:
			__lowercase						      =					{}

			__lowercase						      =					checkpoint['temb.dense.0.weight']
			__lowercase						      =					checkpoint['temb.dense.0.bias']
			__lowercase						      =					checkpoint['temb.dense.1.weight']
			__lowercase						      =					checkpoint['temb.dense.1.bias']

			__lowercase						      =					checkpoint['norm_out.weight']
			__lowercase						      =					checkpoint['norm_out.bias']

			__lowercase						      =					checkpoint['conv_in.weight']
			__lowercase						      =					checkpoint['conv_in.bias']
			__lowercase						      =					checkpoint['conv_out.weight']
			__lowercase						      =					checkpoint['conv_out.bias']

			__lowercase						      =					len({".".join(layer.split("."			)[:2]			) for layer in checkpoint if "down" in layer}			)
			__lowercase						      =					{
			    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE			)
			}

			__lowercase						      =					len({".".join(layer.split("."			)[:2]			) for layer in checkpoint if "up" in layer}			)
			__lowercase						      =					{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE			)}

			for i in range(_SCREAMING_SNAKE_CASE			):
						__lowercase						      =					(i - 1) // (config['layers_per_block'] + 1)

						if any("downsample" in layer for layer in down_blocks[i]			):
									__lowercase						      =					checkpoint[
									    F'''down.{i}.downsample.op.weight'''
									]
									__lowercase						      =					checkpoint[F'''down.{i}.downsample.op.bias''']
						#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
						#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

						if any("block" in layer for layer in down_blocks[i]			):
									__lowercase						      =					len(
									    {".".join(shave_segments(_SCREAMING_SNAKE_CASE ,      2			).split("."			)[:2]			) for layer in down_blocks[i] if "block" in layer}			)
									__lowercase						      =					{
									    layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
									    for layer_id in range(_SCREAMING_SNAKE_CASE			)
									}

									if num_blocks > 0:
												for j in range(config["layers_per_block"]			):
															__lowercase						      =					renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE			)

						if any("attn" in layer for layer in down_blocks[i]			):
									__lowercase						      =					len(
									    {".".join(shave_segments(_SCREAMING_SNAKE_CASE ,      2			).split("."			)[:2]			) for layer in down_blocks[i] if "attn" in layer}			)
									__lowercase						      =					{
									    layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
									    for layer_id in range(_SCREAMING_SNAKE_CASE			)
									}

									if num_attn > 0:
												for j in range(config["layers_per_block"]			):
															__lowercase						      =					renew_attention_paths(attns[j]			)
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      config=_SCREAMING_SNAKE_CASE			)

			__lowercase						      =					[key for key in checkpoint if 'mid.block_1' in key]
			__lowercase						      =					[key for key in checkpoint if 'mid.block_2' in key]
			__lowercase						      =					[key for key in checkpoint if 'mid.attn_1' in key]

			# Mid new 2
			__lowercase						      =					renew_resnet_paths(_SCREAMING_SNAKE_CASE			)
			assign_to_checkpoint(
			    _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}] ,      )

			__lowercase						      =					renew_resnet_paths(_SCREAMING_SNAKE_CASE			)
			assign_to_checkpoint(
			    _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}] ,      )

			__lowercase						      =					renew_attention_paths(_SCREAMING_SNAKE_CASE ,      in_mid=_SCREAMING_SNAKE_CASE			)
			assign_to_checkpoint(
			    _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}] ,      )

			for i in range(_SCREAMING_SNAKE_CASE			):
						__lowercase						      =					num_up_blocks - 1 - i

						if any("upsample" in layer for layer in up_blocks[i]			):
									__lowercase						      =					checkpoint[
									    F'''up.{i}.upsample.conv.weight'''
									]
									__lowercase						      =					checkpoint[F'''up.{i}.upsample.conv.bias''']

						if any("block" in layer for layer in up_blocks[i]			):
									__lowercase						      =					len(
									    {".".join(shave_segments(_SCREAMING_SNAKE_CASE ,      2			).split("."			)[:2]			) for layer in up_blocks[i] if "block" in layer}			)
									__lowercase						      =					{
									    layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE			)
									}

									if num_blocks > 0:
												for j in range(config["layers_per_block"] + 1			):
															__lowercase						      =					{'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
															__lowercase						      =					renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[replace_indices]			)

						if any("attn" in layer for layer in up_blocks[i]			):
									__lowercase						      =					len(
									    {".".join(shave_segments(_SCREAMING_SNAKE_CASE ,      2			).split("."			)[:2]			) for layer in up_blocks[i] if "attn" in layer}			)
									__lowercase						      =					{
									    layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE			)
									}

									if num_attn > 0:
												for j in range(config["layers_per_block"] + 1			):
															__lowercase						      =					{'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
															__lowercase						      =					renew_attention_paths(attns[j]			)
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[replace_indices]			)

			__lowercase						      =					{k.replace("mid_new_2" ,      "mid_block"			): v for k, v in new_checkpoint.items()}
			return new_checkpoint



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->int:
			__lowercase						      =					{}

			__lowercase						      =					checkpoint['encoder.norm_out.weight']
			__lowercase						      =					checkpoint['encoder.norm_out.bias']

			__lowercase						      =					checkpoint['encoder.conv_in.weight']
			__lowercase						      =					checkpoint['encoder.conv_in.bias']
			__lowercase						      =					checkpoint['encoder.conv_out.weight']
			__lowercase						      =					checkpoint['encoder.conv_out.bias']

			__lowercase						      =					checkpoint['decoder.norm_out.weight']
			__lowercase						      =					checkpoint['decoder.norm_out.bias']

			__lowercase						      =					checkpoint['decoder.conv_in.weight']
			__lowercase						      =					checkpoint['decoder.conv_in.bias']
			__lowercase						      =					checkpoint['decoder.conv_out.weight']
			__lowercase						      =					checkpoint['decoder.conv_out.bias']

			__lowercase						      =					len({".".join(layer.split("."			)[:3]			) for layer in checkpoint if "down" in layer}			)
			__lowercase						      =					{
			    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE			)
			}

			__lowercase						      =					len({".".join(layer.split("."			)[:3]			) for layer in checkpoint if "up" in layer}			)
			__lowercase						      =					{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE			)}

			for i in range(_SCREAMING_SNAKE_CASE			):
						__lowercase						      =					(i - 1) // (config['layers_per_block'] + 1)

						if any("downsample" in layer for layer in down_blocks[i]			):
									__lowercase						      =					checkpoint[
									    F'''encoder.down.{i}.downsample.conv.weight'''
									]
									__lowercase						      =					checkpoint[
									    F'''encoder.down.{i}.downsample.conv.bias'''
									]

						if any("block" in layer for layer in down_blocks[i]			):
									__lowercase						      =					len(
									    {".".join(shave_segments(_SCREAMING_SNAKE_CASE ,      3			).split("."			)[:3]			) for layer in down_blocks[i] if "block" in layer}			)
									__lowercase						      =					{
									    layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
									    for layer_id in range(_SCREAMING_SNAKE_CASE			)
									}

									if num_blocks > 0:
												for j in range(config["layers_per_block"]			):
															__lowercase						      =					renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE			)

						if any("attn" in layer for layer in down_blocks[i]			):
									__lowercase						      =					len(
									    {".".join(shave_segments(_SCREAMING_SNAKE_CASE ,      3			).split("."			)[:3]			) for layer in down_blocks[i] if "attn" in layer}			)
									__lowercase						      =					{
									    layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
									    for layer_id in range(_SCREAMING_SNAKE_CASE			)
									}

									if num_attn > 0:
												for j in range(config["layers_per_block"]			):
															__lowercase						      =					renew_attention_paths(attns[j]			)
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      config=_SCREAMING_SNAKE_CASE			)

			__lowercase						      =					[key for key in checkpoint if 'mid.block_1' in key]
			__lowercase						      =					[key for key in checkpoint if 'mid.block_2' in key]
			__lowercase						      =					[key for key in checkpoint if 'mid.attn_1' in key]

			# Mid new 2
			__lowercase						      =					renew_resnet_paths(_SCREAMING_SNAKE_CASE			)
			assign_to_checkpoint(
			    _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}] ,      )

			__lowercase						      =					renew_resnet_paths(_SCREAMING_SNAKE_CASE			)
			assign_to_checkpoint(
			    _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}] ,      )

			__lowercase						      =					renew_attention_paths(_SCREAMING_SNAKE_CASE ,      in_mid=_SCREAMING_SNAKE_CASE			)
			assign_to_checkpoint(
			    _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}] ,      )

			for i in range(_SCREAMING_SNAKE_CASE			):
						__lowercase						      =					num_up_blocks - 1 - i

						if any("upsample" in layer for layer in up_blocks[i]			):
									__lowercase						      =					checkpoint[
									    F'''decoder.up.{i}.upsample.conv.weight'''
									]
									__lowercase						      =					checkpoint[
									    F'''decoder.up.{i}.upsample.conv.bias'''
									]

						if any("block" in layer for layer in up_blocks[i]			):
									__lowercase						      =					len(
									    {".".join(shave_segments(_SCREAMING_SNAKE_CASE ,      3			).split("."			)[:3]			) for layer in up_blocks[i] if "block" in layer}			)
									__lowercase						      =					{
									    layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE			)
									}

									if num_blocks > 0:
												for j in range(config["layers_per_block"] + 1			):
															__lowercase						      =					{'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
															__lowercase						      =					renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[replace_indices]			)

						if any("attn" in layer for layer in up_blocks[i]			):
									__lowercase						      =					len(
									    {".".join(shave_segments(_SCREAMING_SNAKE_CASE ,      3			).split("."			)[:3]			) for layer in up_blocks[i] if "attn" in layer}			)
									__lowercase						      =					{
									    layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE			)
									}

									if num_attn > 0:
												for j in range(config["layers_per_block"] + 1			):
															__lowercase						      =					{'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
															__lowercase						      =					renew_attention_paths(attns[j]			)
															assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      _SCREAMING_SNAKE_CASE ,      additional_replacements=[replace_indices]			)

			__lowercase						      =					{k.replace("mid_new_2" ,      "mid_block"			): v for k, v in new_checkpoint.items()}
			__lowercase						      =					checkpoint['quant_conv.weight']
			__lowercase						      =					checkpoint['quant_conv.bias']
			if "quantize.embedding.weight" in checkpoint:
						__lowercase						      =					checkpoint['quantize.embedding.weight']
			__lowercase						      =					checkpoint['post_quant_conv.weight']
			__lowercase						      =					checkpoint['post_quant_conv.bias']

			return new_checkpoint


if __name__ == "__main__":
							_lowercase							 =							argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
							)

							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The config json file corresponding to the architecture.''',
							)

							parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

							_lowercase							 =							parser.parse_args()
							_lowercase							 =							torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														_lowercase							 =							json.loads(f.read())

							# unet case
							_lowercase							 =							{key.split('''.''')[0] for key in checkpoint.keys()}
							if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
														_lowercase							 =							convert_vq_autoenc_checkpoint(checkpoint, config)
							else:
														_lowercase							 =							convert_ddpm_checkpoint(checkpoint, config)

							if "ddpm" in config:
														del config["ddpm"]

							if config["_class_name"] == "VQModel":
														_lowercase							 =							VQModel(**config)
														model.load_state_dict(converted_checkpoint)
														model.save_pretrained(args.dump_path)
							elif config["_class_name"] == "AutoencoderKL":
														_lowercase							 =							AutoencoderKL(**config)
														model.load_state_dict(converted_checkpoint)
														model.save_pretrained(args.dump_path)
							else:
														_lowercase							 =							UNetaDModel(**config)
														model.load_state_dict(converted_checkpoint)

														_lowercase							 =							DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														_lowercase							 =							DDPMPipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)







"""simple docstring"""






class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					data
										__lowercase						      =					None
										__lowercase						      =					None





							def __str__(    self					) ->       str:




										'''simple docstring'''
										return f'''{self.data}'''





class 				__a    :



							'''simple docstring'''







							def __init__(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					None
										__lowercase						      =					None
							def __iter__(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					self.head
										while node:
													yield node.data
													__lowercase						      =					node.next
							def __str__(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										return "->".join([str(_lowerCamelCase					) for item in self]					)
							def __len__(    self					) ->       Dict:




										'''simple docstring'''
										return sum(1 for _ in self					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Union[str, Any]:




										'''simple docstring'''
										self.insert_at_nth(0     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       List[str]:




										'''simple docstring'''
										self.insert_at_nth(len(self					)     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					len(self					)

										if not 0 <= index <= length:
													raise IndexError("list index out of range"					)
										__lowercase						      =					Node(_lowerCamelCase					)
										if self.head is None:
													__lowercase						      =					__lowercase						      =					new_node
										elif index == 0:
													__lowercase						      =					new_node
													__lowercase						      =					self.head
													__lowercase						      =					new_node
										elif index == length:
													__lowercase						      =					new_node
													__lowercase						      =					self.tail
													__lowercase						      =					new_node
										else:
													__lowercase						      =					self.head
													for _ in range(0     ,						_lowerCamelCase					):
																__lowercase						      =					temp.next
													__lowercase						      =					new_node
													__lowercase						      =					temp.previous
													__lowercase						      =					temp
													__lowercase						      =					new_node
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										return self.delete_at_nth(0					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										return self.delete_at_nth(len(self					) - 1					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					len(self					)

										if not 0 <= index <= length - 1:
													raise IndexError("list index out of range"					)
										__lowercase						      =					self.head  # default first node
										if length == 1:
													__lowercase						      =					__lowercase						      =					None
										elif index == 0:
													__lowercase						      =					self.head.next
													__lowercase						      =					None
										elif index == length - 1:
													__lowercase						      =					self.tail
													__lowercase						      =					self.tail.previous
													__lowercase						      =					None
										else:
													__lowercase						      =					self.head
													for _ in range(0     ,						_lowerCamelCase					):
																__lowercase						      =					temp.next
													__lowercase						      =					temp
													__lowercase						      =					temp.previous
													__lowercase						      =					temp.next
										return delete_node.data
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					self.head

										while current.data != data:  # Find the position to delete
													if current.next:
																__lowercase						      =					current.next
													else:  # We have reached the end an no value matches
																raise ValueError("No data matching given value"					)

										if current == self.head:
													self.delete_head()

										elif current == self.tail:
													self.delete_tail()

										else:  # Before: 1 <--> 2(current) <--> 3
													__lowercase						      =					current.next  # 1 --> 3
													__lowercase						      =					current.previous  # 1 <--> 3
										return data





							def      SCREAMING_SNAKE_CASE	(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										return len(self					) == 0





def     lowerCAmelCase__ (		)	->None:
			__lowercase						      =					DoublyLinkedList()
			assert linked_list.is_empty() is True
			assert str(__magic_name__			) == ""

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

			linked_list.insert_at_head(0			)
			linked_list.insert_at_tail(1_1			)
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(0 ,      1_2			)			)

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


if __name__ == "__main__":
					from doctest import testmod

					testmod()





