


'''simple docstring'''
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def 			a      (			__a ,					__a=1   )  ->	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      (			__a ,					__a=0   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :Optional[int]												= []
					for old_item in old_list:
										UpperCamelCase__   :Union[str, Any]												= old_item.replace('''in_layers.0''' ,					'''norm1'''   )
										UpperCamelCase__   :List[str]												= new_item.replace('''in_layers.2''' ,					'''conv1'''   )

										UpperCamelCase__   :Dict												= new_item.replace('''out_layers.0''' ,					'''norm2'''   )
										UpperCamelCase__   :Dict												= new_item.replace('''out_layers.3''' ,					'''conv2'''   )

										UpperCamelCase__   :Any												= new_item.replace('''emb_layers.1''' ,					'''time_emb_proj'''   )
										UpperCamelCase__   :Union[str, Any]												= new_item.replace('''skip_connection''' ,					'''conv_shortcut'''   )

										UpperCamelCase__   :List[str]												= shave_segments(__a ,					n_shave_prefix_segments=__a   )

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

					return mapping


def 			a      (			__a ,					__a=0   )  ->	Dict:



					'''simple docstring'''






					UpperCamelCase__   :Optional[int]												= []
					for old_item in old_list:
										UpperCamelCase__   :Tuple												= old_item

										UpperCamelCase__   :Any												= new_item.replace('''norm.weight''' ,					'''group_norm.weight'''   )
										UpperCamelCase__   :List[Any]												= new_item.replace('''norm.bias''' ,					'''group_norm.bias'''   )

										UpperCamelCase__   :Union[str, Any]												= new_item.replace('''proj_out.weight''' ,					'''proj_attn.weight'''   )
										UpperCamelCase__   :Tuple												= new_item.replace('''proj_out.bias''' ,					'''proj_attn.bias'''   )

										UpperCamelCase__   :Optional[int]												= shave_segments(__a ,					n_shave_prefix_segments=__a   )

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

					return mapping


def 			a      (			__a ,					__a ,					__a ,					__a=None ,					__a=None ,					__a=None   )  ->	str:



					'''simple docstring'''






					assert isinstance(__a ,					__a   ), "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__   :Any												= old_checkpoint[path]
															UpperCamelCase__   :List[str]												= old_tensor.shape[0] // 3

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

															UpperCamelCase__   :int												= old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

															UpperCamelCase__   :Union[str, Any]												= query.reshape(__a   )
															UpperCamelCase__   :List[Any]												= key.reshape(__a   )
															UpperCamelCase__   :Union[str, Any]												= value.reshape(__a   )

					for path in paths:
										UpperCamelCase__   :Tuple												= path['''new''']

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

										# Global renaming happens here
										UpperCamelCase__   :Dict												= new_path.replace('''middle_block.0''' ,					'''mid_block.resnets.0'''   )
										UpperCamelCase__   :List[str]												= new_path.replace('''middle_block.1''' ,					'''mid_block.attentions.0'''   )
										UpperCamelCase__   :str												= new_path.replace('''middle_block.2''' ,					'''mid_block.resnets.1'''   )

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

        # proj_attn.weight has to be converted from conv 1D to linear
										if "proj_attn.weight" in new_path:
															UpperCamelCase__   :List[str]												= old_checkpoint[path['''old''']][:, :, 0]
										else:
															UpperCamelCase__   :List[str]												= old_checkpoint[path['''old''']]


def 			a      (			__a ,					__a   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__   :int												= {}

					UpperCamelCase__   :Union[str, Any]												= checkpoint['''time_embed.0.weight''']
					UpperCamelCase__   :Tuple												= checkpoint['''time_embed.0.bias''']
					UpperCamelCase__   :Union[str, Any]												= checkpoint['''time_embed.2.weight''']
					UpperCamelCase__   :Union[str, Any]												= checkpoint['''time_embed.2.bias''']

					UpperCamelCase__   :List[str]												= checkpoint['''input_blocks.0.0.weight''']
					UpperCamelCase__   :Tuple												= checkpoint['''input_blocks.0.0.bias''']

					UpperCamelCase__   :Any												= checkpoint['''out.0.weight''']
					UpperCamelCase__   :Dict												= checkpoint['''out.0.bias''']
					UpperCamelCase__   :str												= checkpoint['''out.2.weight''']
					UpperCamelCase__   :List[Any]												= checkpoint['''out.2.bias''']

					# Retrieves the keys for the input blocks only
					UpperCamelCase__   :Optional[Any]												= len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''input_blocks''' in layer}   )
					UpperCamelCase__   :Optional[int]												= {
					    layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
					    for layer_id in range(__a   )
					}

					# Retrieves the keys for the middle blocks only
					UpperCamelCase__   :int												= len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''middle_block''' in layer}   )
					UpperCamelCase__   :int												= {
					    layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
					    for layer_id in range(__a   )
					}

					# Retrieves the keys for the output blocks only
					UpperCamelCase__   :List[str]												= len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''output_blocks''' in layer}   )
					UpperCamelCase__   :Any												= {
					    layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
					    for layer_id in range(__a   )
					}

					for i in range(1 ,					__a   ):
										UpperCamelCase__   :Optional[int]												= (i - 1) // (config['''num_res_blocks'''] + 1)
										UpperCamelCase__   :List[Any]												= (i - 1) % (config['''num_res_blocks'''] + 1)

										UpperCamelCase__   :int												= [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
										UpperCamelCase__   :Dict												= [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

										if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
															UpperCamelCase__   :List[Any]												= checkpoint[
															    f'''input_blocks.{i}.0.op.weight'''
															]
															UpperCamelCase__   :Optional[Any]												= checkpoint[
															    f'''input_blocks.{i}.0.op.bias'''
															]
															continue

										UpperCamelCase__   :List[Any]												= renew_resnet_paths(__a   )
										UpperCamelCase__   :Union[str, Any]												= {'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
										UpperCamelCase__   :Union[str, Any]												= {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
										assign_to_checkpoint(
										    __a ,					__a ,					__a ,					additional_replacements=[meta_path, resnet_op] ,					config=__a   )

										if len(__a   ):
															UpperCamelCase__   :List[Any]												= renew_attention_paths(__a   )
															UpperCamelCase__   :List[str]												= {
															    '''old''': f'''input_blocks.{i}.1''',
															    '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
															}
															UpperCamelCase__   :Optional[Any]												= {
															    f'''input_blocks.{i}.1.qkv.bias''': {
															        '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
															        '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
															        '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
															    },
															    f'''input_blocks.{i}.1.qkv.weight''': {
															        '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
															        '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
															        '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
															    },
															}
															assign_to_checkpoint(
															    __a ,					__a ,					__a ,					additional_replacements=[meta_path] ,					attention_paths_to_split=__a ,					config=__a ,					)

					UpperCamelCase__   :str												= middle_blocks[0]
					UpperCamelCase__   :List[Any]												= middle_blocks[1]
					UpperCamelCase__   :Optional[int]												= middle_blocks[2]

					UpperCamelCase__   :List[str]												= renew_resnet_paths(__a   )
					assign_to_checkpoint(__a ,					__a ,					__a ,					config=__a   )

					UpperCamelCase__   :Optional[int]												= renew_resnet_paths(__a   )
					assign_to_checkpoint(__a ,					__a ,					__a ,					config=__a   )

					UpperCamelCase__   :List[str]												= renew_attention_paths(__a   )
					UpperCamelCase__   :int												= {
					    '''middle_block.1.qkv.bias''': {
					        '''key''': '''mid_block.attentions.0.key.bias''',
					        '''query''': '''mid_block.attentions.0.query.bias''',
					        '''value''': '''mid_block.attentions.0.value.bias''',
					    },
					    '''middle_block.1.qkv.weight''': {
					        '''key''': '''mid_block.attentions.0.key.weight''',
					        '''query''': '''mid_block.attentions.0.query.weight''',
					        '''value''': '''mid_block.attentions.0.value.weight''',
					    },
					}
					assign_to_checkpoint(
					    __a ,					__a ,					__a ,					attention_paths_to_split=__a ,					config=__a   )

					for i in range(__a   ):
										UpperCamelCase__   :List[str]												= i // (config['''num_res_blocks'''] + 1)
										UpperCamelCase__   :Optional[Any]												= i % (config['''num_res_blocks'''] + 1)
										UpperCamelCase__   :Optional[int]												= [shave_segments(__a ,					2   ) for name in output_blocks[i]]
										UpperCamelCase__   :Optional[int]												= {}

										for layer in output_block_layers:
															UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= layer.split('''.'''   )[0], shave_segments(__a ,					1   )
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(__a   )
															else:
																				UpperCamelCase__   :Dict												= [layer_name]

										if len(__a   ) > 1:
															UpperCamelCase__   :Dict												= [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
															UpperCamelCase__   :str												= [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

															UpperCamelCase__   :Tuple												= renew_resnet_paths(__a   )
															UpperCamelCase__   :int												= renew_resnet_paths(__a   )

															UpperCamelCase__   :Union[str, Any]												= {'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
															assign_to_checkpoint(__a ,					__a ,					__a ,					additional_replacements=[meta_path] ,					config=__a   )

															if ["conv.weight", "conv.bias"] in output_block_list.values():
																				UpperCamelCase__   :str												= list(output_block_list.values()   ).index(['''conv.weight''', '''conv.bias''']   )
																				UpperCamelCase__   :str												= checkpoint[
																				    f'''output_blocks.{i}.{index}.conv.weight'''
																				]
																				UpperCamelCase__   :List[Any]												= checkpoint[
																				    f'''output_blocks.{i}.{index}.conv.bias'''
																				]

																				# Clear attentions as they have been attributed above.
																				if len(__a   ) == 2:
																									UpperCamelCase__   :str												= []

															if len(__a   ):
																				UpperCamelCase__   :Optional[int]												= renew_attention_paths(__a   )
																				UpperCamelCase__   :Optional[Any]												= {
																				    '''old''': f'''output_blocks.{i}.1''',
																				    '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																				}
																				UpperCamelCase__   :Optional[int]												= {
																				    f'''output_blocks.{i}.1.qkv.bias''': {
																				        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																				        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																				        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																				    },
																				    f'''output_blocks.{i}.1.qkv.weight''': {
																				        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																				        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																				        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																				    },
																				}
																				assign_to_checkpoint(
																				    __a ,					__a ,					__a ,					additional_replacements=[meta_path] ,					attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions   ) else None ,					config=__a ,					)
										else:
															UpperCamelCase__   :Dict												= renew_resnet_paths(__a ,					n_shave_prefix_segments=1   )
															for path in resnet_0_paths:
																				UpperCamelCase__   :List[Any]												= '''.'''.join(['''output_blocks''', str(__a   ), path['''old''']]   )
																				UpperCamelCase__   :Optional[int]												= '''.'''.join(['''up_blocks''', str(__a   ), '''resnets''', str(__a   ), path['''new''']]   )

																				UpperCamelCase__   :int												= checkpoint[old_path]

					return new_checkpoint


if __name__ == "__main__":
		__snake_case							      =     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.''')

		__snake_case							      =     parser.parse_args()

		__snake_case							      =     torch.load(args.checkpoint_path)

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

		__snake_case							      =     convert_ldm_checkpoint(checkpoint, config)

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

		__snake_case							      =     UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		try:
				__snake_case							      =     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
				__snake_case							      =     VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				__snake_case							      =     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)



'''simple docstring'''
import re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

if NLTK_AVAILABLE:
		with FileLock('''.lock''') as lock:
				nltk.download('''punkt''', quiet=True)


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__a   )   )