



import argparse

import torch

from diffusers import UNetaDConditionModel







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase=None				,   __UpperCAmelCase=None				,   __UpperCAmelCase=None      )			->			Any:



						'''simple docstring'''






						assert isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ), "Paths should be a list of dicts containing 'old' and 'new' keys."

						# Splits the attention layers into three variables.
						if attention_paths_to_split is not None:
												for path, path_map in attention_paths_to_split.items():
																		UpperCAmelCase_					=	old_checkpoint[path]
																		UpperCAmelCase_					=	old_tensor.shape[0] // 3

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

																		UpperCAmelCase_					=	old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

																		UpperCAmelCase_					=	query.reshape(__UpperCAmelCase      )
																		UpperCAmelCase_					=	key.reshape(__UpperCAmelCase      )
																		UpperCAmelCase_					=	value.reshape(__UpperCAmelCase      )

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

												# These have already been assigned
												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

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

        # proj_attn.weight has to be converted from conv 1D to linear
												UpperCAmelCase_					=	old_checkpoint[path['''old''']]
												UpperCAmelCase_					=	['''proj_attn.weight''']
												UpperCAmelCase_					=	['''proj_out.weight''', '''proj_in.weight''']
												if any(k in new_path for k in names      ):
																		UpperCAmelCase_					=	weight[:, :, 0]
												elif any(k in new_path for k in names_a      ) and len(weight.shape      ) > 2 and ".attentions." not in new_path:
																		UpperCAmelCase_					=	weight[:, :, 0]
												else:
																		UpperCAmelCase_					=	weight







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=0      )			->			Any:



						'''simple docstring'''






						UpperCAmelCase_					=	[]
						for old_item in old_list:
												UpperCAmelCase_					=	old_item

												#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
												#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

												#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
												#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

												#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

						return mapping







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=1      )			->			Optional[int]:



						'''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     (						__UpperCAmelCase				,   __UpperCAmelCase=0      )			->			List[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	[]
						for old_item in old_list:
												mapping.append({'''old''': old_item, '''new''': old_item}      )

						return mapping







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=0      )			->			int:



						'''simple docstring'''






						UpperCAmelCase_					=	[]
						for old_item in old_list:
												UpperCAmelCase_					=	old_item.replace('''in_layers.0'''				,   '''norm1'''      )
												UpperCAmelCase_					=	new_item.replace('''in_layers.2'''				,   '''conv1'''      )

												UpperCAmelCase_					=	new_item.replace('''out_layers.0'''				,   '''norm2'''      )
												UpperCAmelCase_					=	new_item.replace('''out_layers.3'''				,   '''conv2'''      )

												UpperCAmelCase_					=	new_item.replace('''emb_layers.1'''				,   '''time_emb_proj'''      )
												UpperCAmelCase_					=	new_item.replace('''skip_connection'''				,   '''conv_shortcut'''      )

												UpperCAmelCase_					=	shave_segments(__UpperCAmelCase				,   n_shave_prefix_segments=__UpperCAmelCase      )

												if "temopral_conv" not in old_item:
																		mapping.append({'''old''': old_item, '''new''': new_item}      )

						return mapping







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase=None				,   __UpperCAmelCase=False      )			->			Dict:



						'''simple docstring'''






						UpperCAmelCase_					=	{}
						UpperCAmelCase_					=	list(checkpoint.keys()      )

						UpperCAmelCase_					=	'''model.diffusion_model.'''

						# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
						if sum(k.startswith('''model_ema'''      ) for k in keys      ) > 100 and extract_ema:
												print(f"Checkpoint {path} has both EMA and non-EMA weights."      )
												print(
												    '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
												    ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'''      )
												for key in keys:
																		if key.startswith('''model.diffusion_model'''      ):
																								UpperCAmelCase_					=	'''model_ema.''' + ''''''.join(key.split('''.'''      )[1:]      )
																								UpperCAmelCase_					=	checkpoint.pop(__UpperCAmelCase      )
						else:
												if sum(k.startswith('''model_ema'''      ) for k in keys      ) > 100:
																		print(
																		    '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
																		    ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'''      )

												for key in keys:
																		UpperCAmelCase_					=	checkpoint.pop(__UpperCAmelCase      )

						UpperCAmelCase_					=	{}

						UpperCAmelCase_					=	unet_state_dict['''time_embed.0.weight''']
						UpperCAmelCase_					=	unet_state_dict['''time_embed.0.bias''']
						UpperCAmelCase_					=	unet_state_dict['''time_embed.2.weight''']
						UpperCAmelCase_					=	unet_state_dict['''time_embed.2.bias''']

						if config["class_embed_type"] is None:
												# No parameters to port
												...
						elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
												UpperCAmelCase_					=	unet_state_dict['''label_emb.0.0.weight''']
												UpperCAmelCase_					=	unet_state_dict['''label_emb.0.0.bias''']
												UpperCAmelCase_					=	unet_state_dict['''label_emb.0.2.weight''']
												UpperCAmelCase_					=	unet_state_dict['''label_emb.0.2.bias''']
						else:
												raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}"      )

						UpperCAmelCase_					=	unet_state_dict['''input_blocks.0.0.weight''']
						UpperCAmelCase_					=	unet_state_dict['''input_blocks.0.0.bias''']

						UpperCAmelCase_					=	[v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''      )]
						UpperCAmelCase_					=	renew_attention_paths(__UpperCAmelCase      )
						UpperCAmelCase_					=	{'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
						assign_to_checkpoint(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

						UpperCAmelCase_					=	unet_state_dict['''out.0.weight''']
						UpperCAmelCase_					=	unet_state_dict['''out.0.bias''']
						UpperCAmelCase_					=	unet_state_dict['''out.2.weight''']
						UpperCAmelCase_					=	unet_state_dict['''out.2.bias''']

						# Retrieves the keys for the input blocks only
						UpperCAmelCase_					=	len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in unet_state_dict if '''input_blocks''' in layer}      )
						UpperCAmelCase_					=	{
						    layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
						    for layer_id in range(__UpperCAmelCase      )
						}

						# Retrieves the keys for the middle blocks only
						UpperCAmelCase_					=	len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in unet_state_dict if '''middle_block''' in layer}      )
						UpperCAmelCase_					=	{
						    layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
						    for layer_id in range(__UpperCAmelCase      )
						}

						# Retrieves the keys for the output blocks only
						UpperCAmelCase_					=	len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in unet_state_dict if '''output_blocks''' in layer}      )
						UpperCAmelCase_					=	{
						    layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
						    for layer_id in range(__UpperCAmelCase      )
						}

						for i in range(1				,   __UpperCAmelCase      ):
												UpperCAmelCase_					=	(i - 1) // (config['''layers_per_block'''] + 1)
												UpperCAmelCase_					=	(i - 1) % (config['''layers_per_block'''] + 1)

												UpperCAmelCase_					=	[
												    key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
												]
												UpperCAmelCase_					=	[key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
												UpperCAmelCase_					=	[key for key in input_blocks[i] if f"input_blocks.{i}.2" in key]

												if f"input_blocks.{i}.op.weight" in unet_state_dict:
																		UpperCAmelCase_					=	unet_state_dict.pop(
																		    f"input_blocks.{i}.op.weight"      )
																		UpperCAmelCase_					=	unet_state_dict.pop(
																		    f"input_blocks.{i}.op.bias"      )

												UpperCAmelCase_					=	renew_resnet_paths(__UpperCAmelCase      )
												UpperCAmelCase_					=	{'''old''': f"input_blocks.{i}.0", '''new''': f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
												assign_to_checkpoint(
												    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

												UpperCAmelCase_					=	[key for key in resnets if '''temopral_conv''' in key]
												UpperCAmelCase_					=	renew_temp_conv_paths(__UpperCAmelCase      )
												UpperCAmelCase_					=	{
												    '''old''': f"input_blocks.{i}.0.temopral_conv",
												    '''new''': f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}",
												}
												assign_to_checkpoint(
												    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

												if len(__UpperCAmelCase      ):
																		UpperCAmelCase_					=	renew_attention_paths(__UpperCAmelCase      )
																		UpperCAmelCase_					=	{'''old''': f"input_blocks.{i}.1", '''new''': f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
																		assign_to_checkpoint(
																		    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

												if len(__UpperCAmelCase      ):
																		UpperCAmelCase_					=	renew_attention_paths(__UpperCAmelCase      )
																		UpperCAmelCase_					=	{
																		    '''old''': f"input_blocks.{i}.2",
																		    '''new''': f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
																		}
																		assign_to_checkpoint(
																		    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

						UpperCAmelCase_					=	middle_blocks[0]
						UpperCAmelCase_					=	[key for key in resnet_a if '''temopral_conv''' in key]
						UpperCAmelCase_					=	middle_blocks[1]
						UpperCAmelCase_					=	middle_blocks[2]
						UpperCAmelCase_					=	middle_blocks[3]
						UpperCAmelCase_					=	[key for key in resnet_a if '''temopral_conv''' in key]

						UpperCAmelCase_					=	renew_resnet_paths(__UpperCAmelCase      )
						UpperCAmelCase_					=	{'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
						assign_to_checkpoint(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   config=__UpperCAmelCase				,   additional_replacements=[meta_path]      )

						UpperCAmelCase_					=	renew_temp_conv_paths(__UpperCAmelCase      )
						UpperCAmelCase_					=	{'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
						assign_to_checkpoint(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   config=__UpperCAmelCase				,   additional_replacements=[meta_path]      )

						UpperCAmelCase_					=	renew_resnet_paths(__UpperCAmelCase      )
						UpperCAmelCase_					=	{'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
						assign_to_checkpoint(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   config=__UpperCAmelCase				,   additional_replacements=[meta_path]      )

						UpperCAmelCase_					=	renew_temp_conv_paths(__UpperCAmelCase      )
						UpperCAmelCase_					=	{'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
						assign_to_checkpoint(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   config=__UpperCAmelCase				,   additional_replacements=[meta_path]      )

						UpperCAmelCase_					=	renew_attention_paths(__UpperCAmelCase      )
						UpperCAmelCase_					=	{'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
						assign_to_checkpoint(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

						UpperCAmelCase_					=	renew_attention_paths(__UpperCAmelCase      )
						UpperCAmelCase_					=	{'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
						assign_to_checkpoint(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

						for i in range(__UpperCAmelCase      ):
												UpperCAmelCase_					=	i // (config['''layers_per_block'''] + 1)
												UpperCAmelCase_					=	i % (config['''layers_per_block'''] + 1)
												UpperCAmelCase_					=	[shave_segments(__UpperCAmelCase				,   2      ) for name in output_blocks[i]]
												UpperCAmelCase_					=	{}

												for layer in output_block_layers:
																		UpperCAmelCase_				, UpperCAmelCase_					=	layer.split('''.'''      )[0], shave_segments(__UpperCAmelCase				,   1      )
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(__UpperCAmelCase      )
																		else:
																								UpperCAmelCase_					=	[layer_name]

												if len(__UpperCAmelCase      ) > 1:
																		UpperCAmelCase_					=	[key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
																		UpperCAmelCase_					=	[key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
																		UpperCAmelCase_					=	[key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]

																		UpperCAmelCase_					=	renew_resnet_paths(__UpperCAmelCase      )
																		UpperCAmelCase_					=	renew_resnet_paths(__UpperCAmelCase      )

																		UpperCAmelCase_					=	{'''old''': f"output_blocks.{i}.0", '''new''': f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
																		assign_to_checkpoint(
																		    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

																		UpperCAmelCase_					=	[key for key in resnets if '''temopral_conv''' in key]
																		UpperCAmelCase_					=	renew_temp_conv_paths(__UpperCAmelCase      )
																		UpperCAmelCase_					=	{
																		    '''old''': f"output_blocks.{i}.0.temopral_conv",
																		    '''new''': f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
																		}
																		assign_to_checkpoint(
																		    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

																		UpperCAmelCase_					=	{k: sorted(__UpperCAmelCase      ) for k, v in output_block_list.items()}
																		if ["conv.bias", "conv.weight"] in output_block_list.values():
																								UpperCAmelCase_					=	list(output_block_list.values()      ).index(['''conv.bias''', '''conv.weight''']      )
																								UpperCAmelCase_					=	unet_state_dict[
																								    f"output_blocks.{i}.{index}.conv.weight"
																								]
																								UpperCAmelCase_					=	unet_state_dict[
																								    f"output_blocks.{i}.{index}.conv.bias"
																								]

																								# Clear attentions as they have been attributed above.
																								if len(__UpperCAmelCase      ) == 2:
																														UpperCAmelCase_					=	[]

																		if len(__UpperCAmelCase      ):
																								UpperCAmelCase_					=	renew_attention_paths(__UpperCAmelCase      )
																								UpperCAmelCase_					=	{
																								    '''old''': f"output_blocks.{i}.1",
																								    '''new''': f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
																								}
																								assign_to_checkpoint(
																								    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )

																		if len(__UpperCAmelCase      ):
																								UpperCAmelCase_					=	renew_attention_paths(__UpperCAmelCase      )
																								UpperCAmelCase_					=	{
																								    '''old''': f"output_blocks.{i}.2",
																								    '''new''': f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
																								}
																								assign_to_checkpoint(
																								    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   additional_replacements=[meta_path]				,   config=__UpperCAmelCase      )
												else:
																		UpperCAmelCase_					=	renew_resnet_paths(__UpperCAmelCase				,   n_shave_prefix_segments=1      )
																		for path in resnet_0_paths:
																								UpperCAmelCase_					=	'''.'''.join(['''output_blocks''', str(__UpperCAmelCase      ), path['''old''']]      )
																								UpperCAmelCase_					=	'''.'''.join(['''up_blocks''', str(__UpperCAmelCase      ), '''resnets''', str(__UpperCAmelCase      ), path['''new''']]      )
																								UpperCAmelCase_					=	unet_state_dict[old_path]

																		UpperCAmelCase_					=	[l for l in output_block_layers if '''temopral_conv''' in l]
																		for path in temopral_conv_paths:
																								UpperCAmelCase_					=	path.split('''temopral_conv.'''      )[-1]
																								UpperCAmelCase_					=	'''.'''.join(['''output_blocks''', str(__UpperCAmelCase      ), str(__UpperCAmelCase      ), '''temopral_conv''', pruned_path]      )
																								UpperCAmelCase_					=	'''.'''.join(['''up_blocks''', str(__UpperCAmelCase      ), '''temp_convs''', str(__UpperCAmelCase      ), pruned_path]      )
																								UpperCAmelCase_					=	unet_state_dict[old_path]

						return new_checkpoint


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

							parser.add_argument(
							    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
							)
							parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
							UpperCamelCase_      			=  parser.parse_args()

							UpperCamelCase_      			=  torch.load(args.checkpoint_path, map_location="cpu")
							UpperCamelCase_      			=  UNetaDConditionModel()

							UpperCamelCase_      			=  convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

							UpperCamelCase_      			=  set(unet.state_dict().keys()) - set(converted_ckpt.keys())
							UpperCamelCase_      			=  set(converted_ckpt.keys()) - set(unet.state_dict().keys())

							assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

							# load state_dict
							unet.load_state_dict(converted_ckpt)

							unet.save_pretrained(args.dump_path)

							# -- finish converting the unet --










from __future__ import annotations

import sys







class 				a_    :

						def __init__(       self      :Dict  ,		_lowercase      :str  ,		_lowercase      :int)						->   List[Any]:
												UpperCAmelCase_					=	letter
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	{}




						def __repr__(       self      :str)						->   str:
												return f"{self.letter}:{self.freq}"







class 				a_    :

						def __init__(       self      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :Letter | TreeNode  ,		_lowercase      :Letter | TreeNode)						->   Dict:
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	left
												UpperCAmelCase_					=	right







def 							A     (						__UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						UpperCAmelCase_					=	{}
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		UpperCAmelCase_					=	chars[c] + 1 if c in chars else 1
						return sorted((Letter(__UpperCAmelCase				,   __UpperCAmelCase      ) for c, f in chars.items())				,   key=lambda __UpperCAmelCase      : x.freq      )







def 							A     (						__UpperCAmelCase      )			->			Letter | TreeNode:



						'''simple docstring'''






						UpperCAmelCase_					=	letters  # type: ignore
						while len(__UpperCAmelCase      ) > 1:
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	left.freq + right.freq
												UpperCAmelCase_					=	TreeNode(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
												response.append(__UpperCAmelCase      )
												response.sort(key=lambda __UpperCAmelCase      : x.freq      )
						return response[0]







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						if isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ):
												UpperCAmelCase_					=	bitstring
												return [root]
						UpperCAmelCase_					=	root  # type: ignore
						UpperCAmelCase_					=	[]
						letters += traverse_tree(treenode.left				,   bitstring + '''0'''      )
						letters += traverse_tree(treenode.right				,   bitstring + '''1'''      )
						return letters







def 							A     (						__UpperCAmelCase      )			->			None:



						'''simple docstring'''






						UpperCAmelCase_					=	parse_file(__UpperCAmelCase      )
						UpperCAmelCase_					=	build_tree(__UpperCAmelCase      )
						UpperCAmelCase_					=	{
						    k: v for letter in traverse_tree(__UpperCAmelCase				,   ''''''      ) for k, v in letter.bitstring.items()
						}
						print(f"Huffman Coding  of {file_path}: "      )
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		print(letters[c]				,   end=''' '''      )
						print()


if __name__ == "__main__":
							# pass the file path to the huffman function
							huffman(sys.argv[1])





