


'''simple docstring'''




import argparse
import json

import torch

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





def 				a_							(    lowercase				,    lowercase=1     )      ->Dict:






				'''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 				a_							(    lowercase				,    lowercase=0     )      ->List[str]:






				'''simple docstring'''

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

								__lowerCamelCase		:   Tuple           =       shave_segments(lowercase				,    n_shave_prefix_segments=lowercase     )

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

				return mapping





def 				a_							(    lowercase				,    lowercase=0				,    lowercase=False     )      ->List[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   Tuple           =       []
				for old_item in old_list:
								__lowerCamelCase		:   Any           =       old_item

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

								__lowerCamelCase		:   Dict           =       new_item.replace("proj_out"				,    "proj_attn"     )
								__lowerCamelCase		:   Any           =       new_item.replace("norm"				,    "group_norm"     )

								__lowerCamelCase		:   Dict           =       shave_segments(lowercase				,    n_shave_prefix_segments=lowercase     )
								mapping.append({"old": old_item, "new": new_item}     )

				return mapping





def 				a_							(    lowercase				,    lowercase				,    lowercase				,    lowercase=None				,    lowercase=None				,    lowercase=None     )      ->List[str]:






				'''simple docstring'''

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

												__lowerCamelCase		:   Optional[Any]           =       (-1, channels) if len(old_tensor.shape     ) == 3 else (-1)

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

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

												__lowerCamelCase		:   Optional[Any]           =       query.reshape(lowercase     ).squeeze()
												__lowerCamelCase		:   Optional[int]           =       key.reshape(lowercase     ).squeeze()
												__lowerCamelCase		:   Union[str, Any]           =       value.reshape(lowercase     ).squeeze()

				for path in paths:
								__lowerCamelCase		:   Any           =       path["new"]

								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								__lowerCamelCase		:   Optional[Any]           =       new_path.replace("down."				,    "down_blocks."     )
								__lowerCamelCase		:   List[str]           =       new_path.replace("up."				,    "up_blocks."     )

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

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





def 				a_							(    lowercase				,    lowercase     )      ->Optional[int]:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       {}

				__lowerCamelCase		:   Tuple           =       checkpoint["temb.dense.0.weight"]
				__lowerCamelCase		:   Tuple           =       checkpoint["temb.dense.0.bias"]
				__lowerCamelCase		:   Tuple           =       checkpoint["temb.dense.1.weight"]
				__lowerCamelCase		:   List[Any]           =       checkpoint["temb.dense.1.bias"]

				__lowerCamelCase		:   Optional[int]           =       checkpoint["norm_out.weight"]
				__lowerCamelCase		:   List[str]           =       checkpoint["norm_out.bias"]

				__lowerCamelCase		:   List[Any]           =       checkpoint["conv_in.weight"]
				__lowerCamelCase		:   Tuple           =       checkpoint["conv_in.bias"]
				__lowerCamelCase		:   Optional[int]           =       checkpoint["conv_out.weight"]
				__lowerCamelCase		:   Optional[Any]           =       checkpoint["conv_out.bias"]

				__lowerCamelCase		:   Optional[int]           =       len({".".join(layer.split("."     )[:2]     ) for layer in checkpoint if "down" in layer}     )
				__lowerCamelCase		:   Tuple           =       {
				    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(lowercase     )
				}

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

				for i in range(lowercase     ):
								__lowerCamelCase		:   Optional[int]           =       (i - 1) // (config["layers_per_block"] + 1)

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

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

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

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

				__lowerCamelCase		:   Optional[int]           =       [key for key in checkpoint if "mid.block_1" in key]
				__lowerCamelCase		:   str           =       [key for key in checkpoint if "mid.block_2" in key]
				__lowerCamelCase		:   Any           =       [key for key in checkpoint if "mid.attn_1" in key]

				# Mid new 2
				__lowerCamelCase		:   Union[str, Any]           =       renew_resnet_paths(lowercase     )
				assign_to_checkpoint(
				    lowercase				,    lowercase				,    lowercase				,    additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]				,    )

				__lowerCamelCase		:   Union[str, Any]           =       renew_resnet_paths(lowercase     )
				assign_to_checkpoint(
				    lowercase				,    lowercase				,    lowercase				,    additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]				,    )

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

				for i in range(lowercase     ):
								__lowerCamelCase		:   Tuple           =       num_up_blocks - 1 - i

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

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

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

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

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

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





def 				a_							(    lowercase				,    lowercase     )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   List[Any]           =       {}

				__lowerCamelCase		:   Union[str, Any]           =       checkpoint["encoder.norm_out.weight"]
				__lowerCamelCase		:   Optional[Any]           =       checkpoint["encoder.norm_out.bias"]

				__lowerCamelCase		:   Dict           =       checkpoint["encoder.conv_in.weight"]
				__lowerCamelCase		:   List[Any]           =       checkpoint["encoder.conv_in.bias"]
				__lowerCamelCase		:   int           =       checkpoint["encoder.conv_out.weight"]
				__lowerCamelCase		:   Union[str, Any]           =       checkpoint["encoder.conv_out.bias"]

				__lowerCamelCase		:   str           =       checkpoint["decoder.norm_out.weight"]
				__lowerCamelCase		:   Union[str, Any]           =       checkpoint["decoder.norm_out.bias"]

				__lowerCamelCase		:   int           =       checkpoint["decoder.conv_in.weight"]
				__lowerCamelCase		:   List[Any]           =       checkpoint["decoder.conv_in.bias"]
				__lowerCamelCase		:   Optional[int]           =       checkpoint["decoder.conv_out.weight"]
				__lowerCamelCase		:   Dict           =       checkpoint["decoder.conv_out.bias"]

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

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

				for i in range(lowercase     ):
								__lowerCamelCase		:   Union[str, Any]           =       (i - 1) // (config["layers_per_block"] + 1)

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

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

												if num_blocks > 0:
																for j in range(config["layers_per_block"]     ):
																				__lowerCamelCase		:   Optional[int]           =       renew_resnet_paths(blocks[j]     )
																				assign_to_checkpoint(lowercase				,    lowercase				,    lowercase     )

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

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

				__lowerCamelCase		:   Dict           =       [key for key in checkpoint if "mid.block_1" in key]
				__lowerCamelCase		:   Optional[int]           =       [key for key in checkpoint if "mid.block_2" in key]
				__lowerCamelCase		:   Tuple           =       [key for key in checkpoint if "mid.attn_1" in key]

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

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

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

				for i in range(lowercase     ):
								__lowerCamelCase		:   List[str]           =       num_up_blocks - 1 - i

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

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

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

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

												if num_attn > 0:
																for j in range(config["layers_per_block"] + 1     ):
																				__lowerCamelCase		:   Optional[int]           =       {"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
																				__lowerCamelCase		:   Union[str, Any]           =       renew_attention_paths(attns[j]     )
																				assign_to_checkpoint(lowercase				,    lowercase				,    lowercase				,    additional_replacements=[replace_indices]     )

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

				return new_checkpoint


if __name__ == "__main__":
				__lowercase	:   Dict   		=					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	:   int   		=					parser.parse_args()
				__lowercase	:   Optional[int]   		=					torch.load(args.checkpoint_path)

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

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

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

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

								__lowercase	:   List[Any]   		=					DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

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









'''simple docstring'''




#
# Sort large text files in a minimum amount of memory
#
import argparse
import os





class _snake_case :
							"""simple docstring"""





							__a				       =  '''block_{0}.dat'''







							def __init__( self      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       filename
											__lowerCamelCase		:   Union[str, Any]           =       []







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.BLOCK_FILENAME_FORMAT.format(__A						)
											with open(__A      ,					"w"						) as file:
															file.write(__A						)
											self.block_filenames.append(__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return self.block_filenames







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       0
											with open(self.filename						) as file:
															while True:
																			__lowerCamelCase		:   Union[str, Any]           =       file.readlines(__A						)

																			if lines == []:
																							break

																			if sort_key is None:
																							lines.sort()
																			else:
																							lines.sort(key=__A						)

																			self.write_block("".join(__A						)      ,					__A						)
																			i += 1







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											map(os.remove      ,					self.block_filenames						)





class _snake_case :
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       -1
											__lowerCamelCase		:   Optional[Any]           =       None

											for i in range(len(__A						)						):
															if min_str is None or choices[i] < min_str:
																			__lowerCamelCase		:   Tuple           =       i

											return min_index





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       files
											__lowerCamelCase		:   Tuple           =       set()
											__lowerCamelCase		:   Optional[Any]           =       len(__A						)
											__lowerCamelCase		:   List[str]           =       {i: None for i in range(self.num_buffers						)}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											return {
											    i: self.buffers[i] for i in range(self.num_buffers						) if i not in self.empty
											}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											for i in range(self.num_buffers						):
															if self.buffers[i] is None and i not in self.empty:
																			__lowerCamelCase		:   Tuple           =       self.files[i].readline()

																			if self.buffers[i] == "":
																							self.empty.add(__A						)
																							self.files[i].close()

											if len(self.empty						) == self.num_buffers:
															return False

											return True







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       self.buffers[index]
											__lowerCamelCase		:   List[Any]           =       None

											return value





class _snake_case :
							"""simple docstring"""





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



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       merge_strategy







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       FilesArray(self.get_file_handles(__A      ,					__A						)						)
											with open(__A      ,					"w"      ,					__A						) as outfile:
															while buffers.refresh():
																			__lowerCamelCase		:   Optional[Any]           =       self.merge_strategy.select(buffers.get_dict()						)
																			outfile.write(buffers.unshift(__A						)						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       {}

											for i in range(len(__A						)						):
															__lowerCamelCase		:   Tuple           =       open(filenames[i]      ,					"r"      ,					__A						)  # noqa: UP015

											return files





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       block_size







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.get_number_blocks(__A      ,					self.block_size						)
											__lowerCamelCase		:   Optional[int]           =       FileSplitter(__A						)
											splitter.split(self.block_size      ,					__A						)

											__lowerCamelCase		:   List[str]           =       FileMerger(NWayMerge()						)
											__lowerCamelCase		:   str           =       self.block_size / (num_blocks + 1)
											merger.merge(splitter.get_block_filenames()      ,					filename + ".out"      ,					__A						)

											splitter.cleanup()







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->List[str]:



											"""simple docstring"""







											return (os.stat(__A						).st_size / block_size) + 1





def 				a_							(    lowercase     )      ->List[Any]:






				'''simple docstring'''

				if string[-1].lower() == "k":
								return int(string[:-1]     ) * 1024
				elif string[-1].lower() == "m":
								return int(string[:-1]     ) * 1024 * 1024
				elif string[-1].lower() == "g":
								return int(string[:-1]     ) * 1024 * 1024 * 1024
				else:
								return int(lowercase     )





def 				a_							(    )      ->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       argparse.ArgumentParser()
				parser.add_argument(
				    "-m"				,    "--mem"				,    help="amount of memory to use for sorting"				,    default="100M"     )
				parser.add_argument(
				    "filename"				,    metavar="<filename>"				,    nargs=1				,    help="name of file to sort"     )
				__lowerCamelCase		:   List[str]           =       parser.parse_args()

				__lowerCamelCase		:   Tuple           =       ExternalSort(parse_memory(args.mem     )     )
				sorter.sort(args.filename[0]     )


if __name__ == "__main__":
				main()





