code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_=None				,							UpperCAmelCase_=None				,							UpperCAmelCase_=None       )      ->int:
  	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():
  			snake_case__  					=      old_checkpoint[path]
  			snake_case__  					=      old_tensor.shape[0] // 3
  
  			snake_case__  					=      (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)
  
  			snake_case__  					=      old_tensor.shape[0] // config['num_head_channels'] // 3
  
  			snake_case__  					=      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
  			snake_case__					,     snake_case__					,     snake_case__  					=      old_tensor.split(channels // num_heads				,							dim=1       )
  
  			snake_case__  					=      query.reshape(UpperCAmelCase_       )
  			snake_case__  					=      key.reshape(UpperCAmelCase_       )
  			snake_case__  					=      value.reshape(UpperCAmelCase_       )
  
  	for path in paths:
  		snake_case__  					=      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:
  				snake_case__  					=      new_path.replace(replacement['old']				,							replacement['new']       )
  
          # proj_attn.weight has to be converted from conv 1D to linear
  		snake_case__  					=      old_checkpoint[path['old']]
  		snake_case__  					=      ['proj_attn.weight']
  		snake_case__  					=      ['proj_out.weight', 'proj_in.weight']
  		if any(k in new_path for k in names       ):
  			snake_case__  					=      weight[:, :, 0]
  		elif any(k in new_path for k in names_a       ) and len(weight.shape       ) > 2 and ".attentions." not in new_path:
  			snake_case__  					=      weight[:, :, 0]
  		else:
  			snake_case__  					=      weight
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_=0       )      ->Optional[Any]:
  	snake_case__  					=      []
  	for old_item in old_list:
  		snake_case__  					=      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 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_=1       )      ->Dict:
  	if n_shave_prefix_segments >= 0:
  		return ".".join(path.split('.'       )[n_shave_prefix_segments:]       )
  	else:
  		return ".".join(path.split('.'       )[:n_shave_prefix_segments]       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_=0       )      ->Dict:
  	snake_case__  					=      []
  	for old_item in old_list:
  		mapping.append({'old': old_item, 'new': old_item}       )
  
  	return mapping
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_=0       )      ->Any:
  	snake_case__  					=      []
  	for old_item in old_list:
  		snake_case__  					=      old_item.replace('in_layers.0'				,							'norm1'       )
  		snake_case__  					=      new_item.replace('in_layers.2'				,							'conv1'       )
  
  		snake_case__  					=      new_item.replace('out_layers.0'				,							'norm2'       )
  		snake_case__  					=      new_item.replace('out_layers.3'				,							'conv2'       )
  
  		snake_case__  					=      new_item.replace('emb_layers.1'				,							'time_emb_proj'       )
  		snake_case__  					=      new_item.replace('skip_connection'				,							'conv_shortcut'       )
  
  		snake_case__  					=      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 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_=None				,							UpperCAmelCase_=False       )      ->str:
  	snake_case__  					=      {}
  	snake_case__  					=      list(checkpoint.keys()       )
  
  	snake_case__  					=      '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       ) > 1_00 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'       ):
  				snake_case__  					=      'model_ema.' + ''.join(key.split('.'       )[1:]       )
  				snake_case__  					=      checkpoint.pop(UpperCAmelCase_       )
  	else:
  		if sum(k.startswith('model_ema'       ) for k in keys       ) > 1_00:
  			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:
  			snake_case__  					=      checkpoint.pop(UpperCAmelCase_       )
  
  	snake_case__  					=      {}
  
  	snake_case__  					=      unet_state_dict['time_embed.0.weight']
  	snake_case__  					=      unet_state_dict['time_embed.0.bias']
  	snake_case__  					=      unet_state_dict['time_embed.2.weight']
  	snake_case__  					=      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":
  		snake_case__  					=      unet_state_dict['label_emb.0.0.weight']
  		snake_case__  					=      unet_state_dict['label_emb.0.0.bias']
  		snake_case__  					=      unet_state_dict['label_emb.0.2.weight']
  		snake_case__  					=      unet_state_dict['label_emb.0.2.bias']
  	else:
  		raise NotImplementedError(f'''Not implemented `class_embed_type`: {config['class_embed_type']}'''       )
  
  	snake_case__  					=      unet_state_dict['input_blocks.0.0.weight']
  	snake_case__  					=      unet_state_dict['input_blocks.0.0.bias']
  
  	snake_case__  					=      [v for v in unet_state_dict if v.startswith('input_blocks.0.1'       )]
  	snake_case__  					=      renew_attention_paths(UpperCAmelCase_       )
  	snake_case__  					=      {'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  	assign_to_checkpoint(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							additional_replacements=[meta_path]				,							config=UpperCAmelCase_       )
  
  	snake_case__  					=      unet_state_dict['out.0.weight']
  	snake_case__  					=      unet_state_dict['out.0.bias']
  	snake_case__  					=      unet_state_dict['out.2.weight']
  	snake_case__  					=      unet_state_dict['out.2.bias']
  
  	# Retrieves the keys for the input blocks only
  	snake_case__  					=      len({'.'.join(layer.split('.'       )[:2]       ) for layer in unet_state_dict if 'input_blocks' in layer}       )
  	snake_case__  					=      {
  	    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
  	snake_case__  					=      len({'.'.join(layer.split('.'       )[:2]       ) for layer in unet_state_dict if 'middle_block' in layer}       )
  	snake_case__  					=      {
  	    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
  	snake_case__  					=      len({'.'.join(layer.split('.'       )[:2]       ) for layer in unet_state_dict if 'output_blocks' in layer}       )
  	snake_case__  					=      {
  	    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_       ):
  		snake_case__  					=      (i - 1) // (config['layers_per_block'] + 1)
  		snake_case__  					=      (i - 1) % (config['layers_per_block'] + 1)
  
  		snake_case__  					=      [
  		    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
  		]
  		snake_case__  					=      [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
  		snake_case__  					=      [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:
  			snake_case__  					=      unet_state_dict.pop(
  			    f'''input_blocks.{i}.op.weight'''       )
  			snake_case__  					=      unet_state_dict.pop(
  			    f'''input_blocks.{i}.op.bias'''       )
  
  		snake_case__  					=      renew_resnet_paths(UpperCAmelCase_       )
  		snake_case__  					=      {'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_       )
  
  		snake_case__  					=      [key for key in resnets if 'temopral_conv' in key]
  		snake_case__  					=      renew_temp_conv_paths(UpperCAmelCase_       )
  		snake_case__  					=      {
  		    '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_       ):
  			snake_case__  					=      renew_attention_paths(UpperCAmelCase_       )
  			snake_case__  					=      {'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_       ):
  			snake_case__  					=      renew_attention_paths(UpperCAmelCase_       )
  			snake_case__  					=      {
  			    '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_       )
  
  	snake_case__  					=      middle_blocks[0]
  	snake_case__  					=      [key for key in resnet_a if 'temopral_conv' in key]
  	snake_case__  					=      middle_blocks[1]
  	snake_case__  					=      middle_blocks[2]
  	snake_case__  					=      middle_blocks[3]
  	snake_case__  					=      [key for key in resnet_a if 'temopral_conv' in key]
  
  	snake_case__  					=      renew_resnet_paths(UpperCAmelCase_       )
  	snake_case__  					=      {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  	assign_to_checkpoint(
  	    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							config=UpperCAmelCase_				,							additional_replacements=[meta_path]       )
  
  	snake_case__  					=      renew_temp_conv_paths(UpperCAmelCase_       )
  	snake_case__  					=      {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  	assign_to_checkpoint(
  	    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							config=UpperCAmelCase_				,							additional_replacements=[meta_path]       )
  
  	snake_case__  					=      renew_resnet_paths(UpperCAmelCase_       )
  	snake_case__  					=      {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  	assign_to_checkpoint(
  	    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							config=UpperCAmelCase_				,							additional_replacements=[meta_path]       )
  
  	snake_case__  					=      renew_temp_conv_paths(UpperCAmelCase_       )
  	snake_case__  					=      {'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  	assign_to_checkpoint(
  	    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							config=UpperCAmelCase_				,							additional_replacements=[meta_path]       )
  
  	snake_case__  					=      renew_attention_paths(UpperCAmelCase_       )
  	snake_case__  					=      {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  	assign_to_checkpoint(
  	    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							additional_replacements=[meta_path]				,							config=UpperCAmelCase_       )
  
  	snake_case__  					=      renew_attention_paths(UpperCAmelCase_       )
  	snake_case__  					=      {'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_       ):
  		snake_case__  					=      i // (config['layers_per_block'] + 1)
  		snake_case__  					=      i % (config['layers_per_block'] + 1)
  		snake_case__  					=      [shave_segments(UpperCAmelCase_				,							2       ) for name in output_blocks[i]]
  		snake_case__  					=      {}
  
  		for layer in output_block_layers:
  			snake_case__					,     snake_case__  					=      layer.split('.'       )[0], shave_segments(UpperCAmelCase_				,							1       )
  			if layer_id in output_block_list:
  				output_block_list[layer_id].append(UpperCAmelCase_       )
  			else:
  				snake_case__  					=      [layer_name]
  
  		if len(UpperCAmelCase_       ) > 1:
  			snake_case__  					=      [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
  			snake_case__  					=      [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
  			snake_case__  					=      [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]
  
  			snake_case__  					=      renew_resnet_paths(UpperCAmelCase_       )
  			snake_case__  					=      renew_resnet_paths(UpperCAmelCase_       )
  
  			snake_case__  					=      {'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_       )
  
  			snake_case__  					=      [key for key in resnets if 'temopral_conv' in key]
  			snake_case__  					=      renew_temp_conv_paths(UpperCAmelCase_       )
  			snake_case__  					=      {
  			    '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_       )
  
  			snake_case__  					=      {k: sorted(UpperCAmelCase_       ) for k, v in output_block_list.items()}
  			if ["conv.bias", "conv.weight"] in output_block_list.values():
  				snake_case__  					=      list(output_block_list.values()       ).index(['conv.bias', 'conv.weight']       )
  				snake_case__  					=      unet_state_dict[
  				    f'''output_blocks.{i}.{index}.conv.weight'''
  				]
  				snake_case__  					=      unet_state_dict[
  				    f'''output_blocks.{i}.{index}.conv.bias'''
  				]
  
  				# Clear attentions as they have been attributed above.
  				if len(UpperCAmelCase_       ) == 2:
  					snake_case__  					=      []
  
  			if len(UpperCAmelCase_       ):
  				snake_case__  					=      renew_attention_paths(UpperCAmelCase_       )
  				snake_case__  					=      {
  				    '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_       ):
  				snake_case__  					=      renew_attention_paths(UpperCAmelCase_       )
  				snake_case__  					=      {
  				    '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:
  			snake_case__  					=      renew_resnet_paths(UpperCAmelCase_				,							n_shave_prefix_segments=1       )
  			for path in resnet_0_paths:
  				snake_case__  					=      '.'.join(['output_blocks', str(UpperCAmelCase_       ), path['old']]       )
  				snake_case__  					=      '.'.join(['up_blocks', str(UpperCAmelCase_       ), 'resnets', str(UpperCAmelCase_       ), path['new']]       )
  				snake_case__  					=      unet_state_dict[old_path]
  
  			snake_case__  					=      [l for l in output_block_layers if 'temopral_conv' in l]
  			for path in temopral_conv_paths:
  				snake_case__  					=      path.split('temopral_conv.'       )[-1]
  				snake_case__  					=      '.'.join(['output_blocks', str(UpperCAmelCase_       ), str(UpperCAmelCase_       ), 'temopral_conv', pruned_path]       )
  				snake_case__  					=      '.'.join(['up_blocks', str(UpperCAmelCase_       ), 'temp_convs', str(UpperCAmelCase_       ), pruned_path]       )
  				snake_case__  					=      unet_state_dict[old_path]
  
  	return new_checkpoint
  
  
  if __name__ == "__main__":
  			a__				: Optional[int]									=    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.''')
  			a__				: List[str]									=    parser.parse_args()
  
  			a__				: str									=    torch.load(args.checkpoint_path, map_location='''cpu''')
  			a__				: List[str]									=    UNetaDConditionModel()
  
  			a__				: Tuple									=    convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
  			a__				: Tuple									=    set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  			a__				: int									=    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 --
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  a__				: Optional[int]									=    sys.argv[1]
  a__				: Any									=    sys.argv[2]
  a__				: str									=    int(sys.argv[3])
  
  a__				: str									=    0
  
  a__				: int									=    AutoTokenizer.from_pretrained(model_name_or_path)
  max_len -= tokenizer.num_special_tokens_to_add()
  
  with open(dataset, '''rt''') as f_p:
  			for line in f_p:
  						a__				: Tuple									=    line.rstrip()
  
  						if not line:
  									print(line)
  									a__				: List[str]									=    0
  									continue
  
  						a__				: Optional[int]									=    line.split()[0]
  
  						a__				: Tuple									=    len(tokenizer.tokenize(token))
  
  						# Token contains strange control characters like \x96 or \x95
  						# Just filter out the complete line
  						if current_subwords_len == 0:
  									continue
  
  						if (subword_len_counter + current_subwords_len) > max_len:
  									print('''''')
  									print(line)
  									a__				: List[str]									=    current_subwords_len
  									continue
  
  						subword_len_counter += current_subwords_len
  
  						print(line)
  
  
  
  