




"""simple docstring"""





import argparse
import json

import torch

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

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase=1						):
						'''simple docstring'''


						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							(	lowerCamelCase ,     lowerCamelCase=0						):
						'''simple docstring'''


						snake_case__    :   int							     =   []
						for old_item in old_list:
												snake_case__    :   List[Any]							     =   old_item
												snake_case__    :   Union[str, Any]							     =   new_item.replace('''block.''' ,     '''resnets.'''						)
												snake_case__    :   Tuple							     =   new_item.replace('''conv_shorcut''' ,     '''conv1'''						)
												snake_case__    :   List[str]							     =   new_item.replace('''in_shortcut''' ,     '''conv_shortcut'''						)
												snake_case__    :   Optional[Any]							     =   new_item.replace('''temb_proj''' ,     '''time_emb_proj'''						)

												snake_case__    :   List[Any]							     =   shave_segments(lowerCamelCase ,     n_shave_prefix_segments=lowerCamelCase						)

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

						return mapping

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase=0 ,     lowerCamelCase=False						):
						'''simple docstring'''


						snake_case__    :   int							     =   []
						for old_item in old_list:
												snake_case__    :   Dict							     =   old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		snake_case__    :   Tuple							     =   new_item.replace('''attn''' ,     '''attentions'''						)
												snake_case__    :   Union[str, Any]							     =   new_item.replace('''.k.''' ,     '''.key.'''						)
												snake_case__    :   Optional[int]							     =   new_item.replace('''.v.''' ,     '''.value.'''						)
												snake_case__    :   Optional[int]							     =   new_item.replace('''.q.''' ,     '''.query.'''						)

												snake_case__    :   int							     =   new_item.replace('''proj_out''' ,     '''proj_attn'''						)
												snake_case__    :   int							     =   new_item.replace('''norm''' ,     '''group_norm'''						)

												snake_case__    :   Optional[int]							     =   shave_segments(lowerCamelCase ,     n_shave_prefix_segments=lowerCamelCase						)
												mapping.append({'''old''': old_item, '''new''': new_item}						)

						return mapping

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase=None ,     lowerCamelCase=None ,     lowerCamelCase=None						):
						'''simple docstring'''


						assert isinstance(lowerCamelCase ,     lowerCamelCase						), "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():
																		snake_case__    :   Optional[Any]							     =   old_checkpoint[path]
																		snake_case__    :   List[Any]							     =   old_tensor.shape[0] // 3

																		snake_case__    :   str							     =   (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

																		snake_case__    :   int							     =   old_tensor.shape[0] // config.get('''num_head_channels''' ,     1						) // 3

																		snake_case__    :   Optional[int]							     =   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
																		snake_case__     ,		snake_case__     ,		snake_case__    :   List[str]							     =   old_tensor.split(channels // num_heads ,     dim=1						)

																		snake_case__    :   List[str]							     =   query.reshape(lowerCamelCase						).squeeze()
																		snake_case__    :   int							     =   key.reshape(lowerCamelCase						).squeeze()
																		snake_case__    :   str							     =   value.reshape(lowerCamelCase						).squeeze()

						for path in paths:
												snake_case__    :   Union[str, Any]							     =   path['''new''']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												snake_case__    :   Dict							     =   new_path.replace('''down.''' ,     '''down_blocks.'''						)
												snake_case__    :   Union[str, Any]							     =   new_path.replace('''up.''' ,     '''up_blocks.'''						)

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								snake_case__    :   List[Any]							     =   new_path.replace(replacement['''old'''] ,     replacement['''new''']						)

												if "attentions" in new_path:
																		snake_case__    :   List[Any]							     =   old_checkpoint[path['''old''']].squeeze()
												else:
																		snake_case__    :   Tuple							     =   old_checkpoint[path['''old''']]

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   {}

						snake_case__    :   str							     =   checkpoint['''temb.dense.0.weight''']
						snake_case__    :   Tuple							     =   checkpoint['''temb.dense.0.bias''']
						snake_case__    :   int							     =   checkpoint['''temb.dense.1.weight''']
						snake_case__    :   Optional[Any]							     =   checkpoint['''temb.dense.1.bias''']

						snake_case__    :   Tuple							     =   checkpoint['''norm_out.weight''']
						snake_case__    :   Any							     =   checkpoint['''norm_out.bias''']

						snake_case__    :   List[str]							     =   checkpoint['''conv_in.weight''']
						snake_case__    :   Any							     =   checkpoint['''conv_in.bias''']
						snake_case__    :   Optional[int]							     =   checkpoint['''conv_out.weight''']
						snake_case__    :   Optional[int]							     =   checkpoint['''conv_out.bias''']

						snake_case__    :   str							     =   len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''down''' in layer}						)
						snake_case__    :   Union[str, Any]							     =   {
						    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowerCamelCase						)
						}

						snake_case__    :   Dict							     =   len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''up''' in layer}						)
						snake_case__    :   Optional[Any]							     =   {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(lowerCamelCase						)}

						for i in range(lowerCamelCase						):
												snake_case__    :   str							     =   (i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]						):
																		snake_case__    :   Dict							     =   checkpoint[
																		    f'''down.{i}.downsample.op.weight'''
																		]
																		snake_case__    :   int							     =   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]						):
																		snake_case__    :   Optional[Any]							     =   len(
																		    {'''.'''.join(shave_segments(lowerCamelCase ,     2						).split('''.'''						)[:2]						) for layer in down_blocks[i] if '''block''' in layer}						)
																		snake_case__    :   Any							     =   {
																		    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																		    for layer_id in range(lowerCamelCase						)
																		}

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block''']						):
																														snake_case__    :   int							     =   renew_resnet_paths(blocks[j]						)
																														assign_to_checkpoint(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

												if any('''attn''' in layer for layer in down_blocks[i]						):
																		snake_case__    :   Optional[Any]							     =   len(
																		    {'''.'''.join(shave_segments(lowerCamelCase ,     2						).split('''.'''						)[:2]						) for layer in down_blocks[i] if '''attn''' in layer}						)
																		snake_case__    :   int							     =   {
																		    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																		    for layer_id in range(lowerCamelCase						)
																		}

																		if num_attn > 0:
																								for j in range(config['''layers_per_block''']						):
																														snake_case__    :   Union[str, Any]							     =   renew_attention_paths(attns[j]						)
																														assign_to_checkpoint(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     config=lowerCamelCase						)

						snake_case__    :   Union[str, Any]							     =   [key for key in checkpoint if '''mid.block_1''' in key]
						snake_case__    :   Optional[Any]							     =   [key for key in checkpoint if '''mid.block_2''' in key]
						snake_case__    :   Tuple							     =   [key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						snake_case__    :   Optional[int]							     =   renew_resnet_paths(lowerCamelCase						)
						assign_to_checkpoint(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}] ,     )

						snake_case__    :   Optional[Any]							     =   renew_resnet_paths(lowerCamelCase						)
						assign_to_checkpoint(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}] ,     )

						snake_case__    :   Union[str, Any]							     =   renew_attention_paths(lowerCamelCase ,     in_mid=lowerCamelCase						)
						assign_to_checkpoint(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}] ,     )

						for i in range(lowerCamelCase						):
												snake_case__    :   Optional[int]							     =   num_up_blocks - 1 - i

												if any('''upsample''' in layer for layer in up_blocks[i]						):
																		snake_case__    :   Optional[int]							     =   checkpoint[
																		    f'''up.{i}.upsample.conv.weight'''
																		]
																		snake_case__    :   int							     =   checkpoint[f'''up.{i}.upsample.conv.bias''']

												if any('''block''' in layer for layer in up_blocks[i]						):
																		snake_case__    :   List[str]							     =   len(
																		    {'''.'''.join(shave_segments(lowerCamelCase ,     2						).split('''.'''						)[:2]						) for layer in up_blocks[i] if '''block''' in layer}						)
																		snake_case__    :   Tuple							     =   {
																		    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(lowerCamelCase						)
																		}

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block'''] + 1						):
																														snake_case__    :   Dict							     =   {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
																														snake_case__    :   Union[str, Any]							     =   renew_resnet_paths(blocks[j]						)
																														assign_to_checkpoint(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[replace_indices]						)

												if any('''attn''' in layer for layer in up_blocks[i]						):
																		snake_case__    :   Any							     =   len(
																		    {'''.'''.join(shave_segments(lowerCamelCase ,     2						).split('''.'''						)[:2]						) for layer in up_blocks[i] if '''attn''' in layer}						)
																		snake_case__    :   List[str]							     =   {
																		    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(lowerCamelCase						)
																		}

																		if num_attn > 0:
																								for j in range(config['''layers_per_block'''] + 1						):
																														snake_case__    :   List[Any]							     =   {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
																														snake_case__    :   Union[str, Any]							     =   renew_attention_paths(attns[j]						)
																														assign_to_checkpoint(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[replace_indices]						)

						snake_case__    :   int							     =   {k.replace('''mid_new_2''' ,     '''mid_block'''						): v for k, v in new_checkpoint.items()}
						return new_checkpoint

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   int							     =   {}

						snake_case__    :   int							     =   checkpoint['''encoder.norm_out.weight''']
						snake_case__    :   int							     =   checkpoint['''encoder.norm_out.bias''']

						snake_case__    :   List[Any]							     =   checkpoint['''encoder.conv_in.weight''']
						snake_case__    :   List[str]							     =   checkpoint['''encoder.conv_in.bias''']
						snake_case__    :   List[str]							     =   checkpoint['''encoder.conv_out.weight''']
						snake_case__    :   List[Any]							     =   checkpoint['''encoder.conv_out.bias''']

						snake_case__    :   Tuple							     =   checkpoint['''decoder.norm_out.weight''']
						snake_case__    :   Dict							     =   checkpoint['''decoder.norm_out.bias''']

						snake_case__    :   Any							     =   checkpoint['''decoder.conv_in.weight''']
						snake_case__    :   Any							     =   checkpoint['''decoder.conv_in.bias''']
						snake_case__    :   Dict							     =   checkpoint['''decoder.conv_out.weight''']
						snake_case__    :   Optional[Any]							     =   checkpoint['''decoder.conv_out.bias''']

						snake_case__    :   str							     =   len({'''.'''.join(layer.split('''.'''						)[:3]						) for layer in checkpoint if '''down''' in layer}						)
						snake_case__    :   List[Any]							     =   {
						    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowerCamelCase						)
						}

						snake_case__    :   List[str]							     =   len({'''.'''.join(layer.split('''.'''						)[:3]						) for layer in checkpoint if '''up''' in layer}						)
						snake_case__    :   List[str]							     =   {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(lowerCamelCase						)}

						for i in range(lowerCamelCase						):
												snake_case__    :   Tuple							     =   (i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]						):
																		snake_case__    :   Tuple							     =   checkpoint[
																		    f'''encoder.down.{i}.downsample.conv.weight'''
																		]
																		snake_case__    :   Any							     =   checkpoint[
																		    f'''encoder.down.{i}.downsample.conv.bias'''
																		]

												if any('''block''' in layer for layer in down_blocks[i]						):
																		snake_case__    :   Union[str, Any]							     =   len(
																		    {'''.'''.join(shave_segments(lowerCamelCase ,     3						).split('''.'''						)[:3]						) for layer in down_blocks[i] if '''block''' in layer}						)
																		snake_case__    :   str							     =   {
																		    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																		    for layer_id in range(lowerCamelCase						)
																		}

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block''']						):
																														snake_case__    :   str							     =   renew_resnet_paths(blocks[j]						)
																														assign_to_checkpoint(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

												if any('''attn''' in layer for layer in down_blocks[i]						):
																		snake_case__    :   Any							     =   len(
																		    {'''.'''.join(shave_segments(lowerCamelCase ,     3						).split('''.'''						)[:3]						) for layer in down_blocks[i] if '''attn''' in layer}						)
																		snake_case__    :   Dict							     =   {
																		    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																		    for layer_id in range(lowerCamelCase						)
																		}

																		if num_attn > 0:
																								for j in range(config['''layers_per_block''']						):
																														snake_case__    :   Tuple							     =   renew_attention_paths(attns[j]						)
																														assign_to_checkpoint(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     config=lowerCamelCase						)

						snake_case__    :   Any							     =   [key for key in checkpoint if '''mid.block_1''' in key]
						snake_case__    :   Optional[Any]							     =   [key for key in checkpoint if '''mid.block_2''' in key]
						snake_case__    :   Any							     =   [key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						snake_case__    :   Dict							     =   renew_resnet_paths(lowerCamelCase						)
						assign_to_checkpoint(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}] ,     )

						snake_case__    :   int							     =   renew_resnet_paths(lowerCamelCase						)
						assign_to_checkpoint(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}] ,     )

						snake_case__    :   int							     =   renew_attention_paths(lowerCamelCase ,     in_mid=lowerCamelCase						)
						assign_to_checkpoint(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}] ,     )

						for i in range(lowerCamelCase						):
												snake_case__    :   Optional[int]							     =   num_up_blocks - 1 - i

												if any('''upsample''' in layer for layer in up_blocks[i]						):
																		snake_case__    :   Optional[Any]							     =   checkpoint[
																		    f'''decoder.up.{i}.upsample.conv.weight'''
																		]
																		snake_case__    :   str							     =   checkpoint[
																		    f'''decoder.up.{i}.upsample.conv.bias'''
																		]

												if any('''block''' in layer for layer in up_blocks[i]						):
																		snake_case__    :   Dict							     =   len(
																		    {'''.'''.join(shave_segments(lowerCamelCase ,     3						).split('''.'''						)[:3]						) for layer in up_blocks[i] if '''block''' in layer}						)
																		snake_case__    :   Union[str, Any]							     =   {
																		    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(lowerCamelCase						)
																		}

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block'''] + 1						):
																														snake_case__    :   Optional[Any]							     =   {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
																														snake_case__    :   Dict							     =   renew_resnet_paths(blocks[j]						)
																														assign_to_checkpoint(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[replace_indices]						)

												if any('''attn''' in layer for layer in up_blocks[i]						):
																		snake_case__    :   List[Any]							     =   len(
																		    {'''.'''.join(shave_segments(lowerCamelCase ,     3						).split('''.'''						)[:3]						) for layer in up_blocks[i] if '''attn''' in layer}						)
																		snake_case__    :   Union[str, Any]							     =   {
																		    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(lowerCamelCase						)
																		}

																		if num_attn > 0:
																								for j in range(config['''layers_per_block'''] + 1						):
																														snake_case__    :   List[str]							     =   {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
																														snake_case__    :   int							     =   renew_attention_paths(attns[j]						)
																														assign_to_checkpoint(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     additional_replacements=[replace_indices]						)

						snake_case__    :   Optional[Any]							     =   {k.replace('''mid_new_2''' ,     '''mid_block'''						): v for k, v in new_checkpoint.items()}
						snake_case__    :   Optional[int]							     =   checkpoint['''quant_conv.weight''']
						snake_case__    :   List[Any]							     =   checkpoint['''quant_conv.bias''']
						if "quantize.embedding.weight" in checkpoint:
												snake_case__    :   Tuple							     =   checkpoint['''quantize.embedding.weight''']
						snake_case__    :   Union[str, Any]							     =   checkpoint['''post_quant_conv.weight''']
						snake_case__    :   int							     =   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"""





from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   Any							     =   data
										snake_case__    :   Node | None							     =   None





				def __str__(				self				:       Union[str, Any] ):
										return F'''{self.data}'''







class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       Optional[int] ):
										snake_case__    :   Node | None							     =   None
										snake_case__    :   Node | None							     =   None

				def __iter__(				self				:       Optional[int] ):
										snake_case__    :   Optional[int]							     =   self.front
										while node:
																yield node.data
																snake_case__    :   Optional[int]							     =   node.next

				def __len__(				self				:       Any ):
										return len(tuple(iter(self ) ) )

				def __str__(				self				:       Union[str, Any] ):
										return " <- ".join(str(SCREAMING_SNAKE_CASE_ ) for item in self )

				def  _lowerCamelCase   (				self				:       List[str] ):
										return len(self ) == 0

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   int							     =   Node(SCREAMING_SNAKE_CASE_ )
										if self.is_empty():
																snake_case__    :   List[Any]							     =   node
										else:
																assert isinstance(self.rear ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Union[str, Any]							     =   node
																snake_case__    :   int							     =   node

				def  _lowerCamelCase   (				self				:       int ):
										if self.is_empty():
																raise IndexError('''dequeue from empty queue''' )
										assert isinstance(self.front ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   List[str]							     =   self.front
										snake_case__    :   str							     =   self.front.next
										if self.front is None:
																snake_case__    :   Optional[Any]							     =   None
										return node.data





				def  _lowerCamelCase   (				self				:       Optional[int] ):
										snake_case__    :   Optional[Any]							     =   None


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

							testmod()


