


"""simple docstring"""






import argparse
import json

import torch

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





def 							A_					(   snake_case__					,				snake_case__=1		)				->     Union[str, Any]:
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split('''.'''		)[n_shave_prefix_segments:]		)
			else:
						return ".".join(path.split('''.'''		)[:n_shave_prefix_segments]		)





def 							A_					(   snake_case__					,				snake_case__=0		)				->     Tuple:
			_UpperCamelCase				:Optional[Any]				    =  []
			for old_item in old_list:
						_UpperCamelCase				:List[str]				    =  old_item
						_UpperCamelCase				:int				    =  new_item.replace('''block.'''					,				'''resnets.'''		)
						_UpperCamelCase				:List[str]				    =  new_item.replace('''conv_shorcut'''					,				'''conv1'''		)
						_UpperCamelCase				:List[Any]				    =  new_item.replace('''in_shortcut'''					,				'''conv_shortcut'''		)
						_UpperCamelCase				:str				    =  new_item.replace('''temb_proj'''					,				'''time_emb_proj'''		)

						_UpperCamelCase				:Optional[Any]				    =  shave_segments(snake_case__					,				n_shave_prefix_segments=snake_case__		)

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

			return mapping





def 							A_					(   snake_case__					,				snake_case__=0					,				snake_case__=False		)				->     Dict:
			_UpperCamelCase				:Union[str, Any]				    =  []
			for old_item in old_list:
						_UpperCamelCase				:List[str]				    =  old_item

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

						_UpperCamelCase				:Dict				    =  new_item.replace('''proj_out'''					,				'''proj_attn'''		)
						_UpperCamelCase				:Optional[int]				    =  new_item.replace('''norm'''					,				'''group_norm'''		)

						_UpperCamelCase				:Union[str, Any]				    =  shave_segments(snake_case__					,				n_shave_prefix_segments=snake_case__		)
						mapping.append({'''old''': old_item, '''new''': new_item}		)

			return mapping





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				snake_case__=None					,				snake_case__=None					,				snake_case__=None		)				->     int:
			assert isinstance(snake_case__					,				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():
									_UpperCamelCase				:Tuple				    =  old_checkpoint[path]
									_UpperCamelCase				:str				    =  old_tensor.shape[0] // 3

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

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

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

									_UpperCamelCase				:Dict				    =  query.reshape(snake_case__		).squeeze()
									_UpperCamelCase				:List[str]				    =  key.reshape(snake_case__		).squeeze()
									_UpperCamelCase				:List[str]				    =  value.reshape(snake_case__		).squeeze()

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

						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						_UpperCamelCase				:int				    =  new_path.replace('''down.'''					,				'''down_blocks.'''		)
						_UpperCamelCase				:Tuple				    =  new_path.replace('''up.'''					,				'''up_blocks.'''		)

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

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





def 							A_					(   snake_case__					,				snake_case__		)				->     int:
			_UpperCamelCase				:Union[str, Any]				    =  {}

			_UpperCamelCase				:int				    =  checkpoint['''temb.dense.0.weight''']
			_UpperCamelCase				:Any				    =  checkpoint['''temb.dense.0.bias''']
			_UpperCamelCase				:int				    =  checkpoint['''temb.dense.1.weight''']
			_UpperCamelCase				:List[str]				    =  checkpoint['''temb.dense.1.bias''']

			_UpperCamelCase				:int				    =  checkpoint['''norm_out.weight''']
			_UpperCamelCase				:Any				    =  checkpoint['''norm_out.bias''']

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

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

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

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

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

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

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

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

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

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

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

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

			for i in range(snake_case__		):
						_UpperCamelCase				:List[str]				    =  num_up_blocks - 1 - i

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

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

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

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

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

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





def 							A_					(   snake_case__					,				snake_case__		)				->     int:
			_UpperCamelCase				:List[str]				    =  {}

			_UpperCamelCase				:Tuple				    =  checkpoint['''encoder.norm_out.weight''']
			_UpperCamelCase				:Dict				    =  checkpoint['''encoder.norm_out.bias''']

			_UpperCamelCase				:List[str]				    =  checkpoint['''encoder.conv_in.weight''']
			_UpperCamelCase				:Union[str, Any]				    =  checkpoint['''encoder.conv_in.bias''']
			_UpperCamelCase				:str				    =  checkpoint['''encoder.conv_out.weight''']
			_UpperCamelCase				:Union[str, Any]				    =  checkpoint['''encoder.conv_out.bias''']

			_UpperCamelCase				:Optional[int]				    =  checkpoint['''decoder.norm_out.weight''']
			_UpperCamelCase				:Dict				    =  checkpoint['''decoder.norm_out.bias''']

			_UpperCamelCase				:Union[str, Any]				    =  checkpoint['''decoder.conv_in.weight''']
			_UpperCamelCase				:Union[str, Any]				    =  checkpoint['''decoder.conv_in.bias''']
			_UpperCamelCase				:Tuple				    =  checkpoint['''decoder.conv_out.weight''']
			_UpperCamelCase				:Tuple				    =  checkpoint['''decoder.conv_out.bias''']

			_UpperCamelCase				:Union[str, Any]				    =  len({'''.'''.join(layer.split('''.'''		)[:3]		) for layer in checkpoint if '''down''' in layer}		)
			_UpperCamelCase				:Dict				    =  {
			    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(snake_case__		)
			}

			_UpperCamelCase				:Union[str, Any]				    =  len({'''.'''.join(layer.split('''.'''		)[:3]		) for layer in checkpoint if '''up''' in layer}		)
			_UpperCamelCase				:Dict				    =  {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(snake_case__		)}

			for i in range(snake_case__		):
						_UpperCamelCase				:Union[str, Any]				    =  (i - 1) // (config['''layers_per_block'''] + 1)

						if any('''downsample''' in layer for layer in down_blocks[i]		):
									_UpperCamelCase				:str				    =  checkpoint[
									    f"encoder.down.{i}.downsample.conv.weight"
									]
									_UpperCamelCase				:Optional[int]				    =  checkpoint[
									    f"encoder.down.{i}.downsample.conv.bias"
									]

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

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

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

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

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

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

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

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

			for i in range(snake_case__		):
						_UpperCamelCase				:Dict				    =  num_up_blocks - 1 - i

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

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

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

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

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

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

			return new_checkpoint


if __name__ == "__main__":
					UpperCamelCase__							:Any       =  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.""")

					UpperCamelCase__							:List[str]       =  parser.parse_args()
					UpperCamelCase__							:Tuple       =  torch.load(args.checkpoint_path)

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

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

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

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

										UpperCamelCase__							:Tuple       =  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										UpperCamelCase__							:List[str]       =  DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)





"""simple docstring"""






from __future__ import annotations

from enum import Enum






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       24
		A         =       21
		A         =       18
		A         =       15
		A         =       12
		A         =       9
		A         =       6
		A         =       3
		A         =       2
		A         =       1
		A         =       -1
		A         =       -2
		A         =       -3
		A         =       -6
		A         =       -9
		A         =       -12
		A         =       -15
		A         =       -18
		A         =       -21
		A         =       -24






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       8
		A         =       7
		A         =       6
		A         =       5
		A         =       4
		A         =       3
		A         =       2
		A         =       1





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				)				->     float:
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Tuple				    =  SIUnit[known_prefix.lower()]
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Optional[int]				    =  SIUnit[unknown_prefix.lower()]
			_UpperCamelCase				:float				    =  known_amount * (
			    10 ** (known_prefix.value - unknown_prefix.value)
			)
			return unknown_amount





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				)				->     float:
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Any				    =  BinaryUnit[known_prefix.lower()]
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Dict				    =  BinaryUnit[unknown_prefix.lower()]
			_UpperCamelCase				:float				    =  known_amount * (
			    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
			)
			return unknown_amount


if __name__ == "__main__":
					import doctest

					doctest.testmod()

