



'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :List[Any]=1       ):
				if n_shave_prefix_segments >= 0:
								return ".".join(path.split('''.'''       )[n_shave_prefix_segments:]       )
				else:
								return ".".join(path.split('''.'''       )[:n_shave_prefix_segments]       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :int=0       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= []
				for old_item in old_list:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= old_item.replace('''in_layers.0'''    ,	'''norm1'''       )
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= new_item.replace('''in_layers.2'''    ,	'''conv1'''       )

								SCREAMING_SNAKE_CASE  :						int      					= new_item.replace('''out_layers.0'''    ,	'''norm2'''       )
								SCREAMING_SNAKE_CASE  :						int      					= new_item.replace('''out_layers.3'''    ,	'''conv2'''       )

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

								SCREAMING_SNAKE_CASE  :						Dict      					= shave_segments(_SCREAMING_SNAKE_CASE    ,	n_shave_prefix_segments=_SCREAMING_SNAKE_CASE       )

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

				return mapping






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :List[Any]=0       ):
				SCREAMING_SNAKE_CASE  :						str      					= []
				for old_item in old_list:
								SCREAMING_SNAKE_CASE  :						Any      					= old_item

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

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

								SCREAMING_SNAKE_CASE  :						Tuple      					= shave_segments(_SCREAMING_SNAKE_CASE    ,	n_shave_prefix_segments=_SCREAMING_SNAKE_CASE       )

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

				return mapping






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :List[Any]=None    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=None    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=None       ):
				assert isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ), "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():
												SCREAMING_SNAKE_CASE  :						Any      					= old_checkpoint[path]
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= old_tensor.shape[0] // 3

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

												SCREAMING_SNAKE_CASE  :						Dict      					= old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

												SCREAMING_SNAKE_CASE  :						int      					= query.reshape(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						str      					= key.reshape(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						int      					= value.reshape(_SCREAMING_SNAKE_CASE       )

				for path in paths:
								SCREAMING_SNAKE_CASE  :						str      					= 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
								SCREAMING_SNAKE_CASE  :						List[Any]      					= new_path.replace('''middle_block.0'''    ,	'''mid_block.resnets.0'''       )
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= new_path.replace('''middle_block.1'''    ,	'''mid_block.attentions.0'''       )
								SCREAMING_SNAKE_CASE  :						Any      					= new_path.replace('''middle_block.2'''    ,	'''mid_block.resnets.1'''       )

								if additional_replacements is not None:
												for replacement in additional_replacements:
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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:
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= old_checkpoint[path['''old''']][:, :, 0]
								else:
												SCREAMING_SNAKE_CASE  :						Any      					= old_checkpoint[path['''old''']]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Any      					= {}

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= checkpoint['''time_embed.0.weight''']
				SCREAMING_SNAKE_CASE  :						Dict      					= checkpoint['''time_embed.0.bias''']
				SCREAMING_SNAKE_CASE  :						List[str]      					= checkpoint['''time_embed.2.weight''']
				SCREAMING_SNAKE_CASE  :						List[str]      					= checkpoint['''time_embed.2.bias''']

				SCREAMING_SNAKE_CASE  :						int      					= checkpoint['''input_blocks.0.0.weight''']
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= checkpoint['''input_blocks.0.0.bias''']

				SCREAMING_SNAKE_CASE  :						str      					= checkpoint['''out.0.weight''']
				SCREAMING_SNAKE_CASE  :						List[str]      					= checkpoint['''out.0.bias''']
				SCREAMING_SNAKE_CASE  :						Tuple      					= checkpoint['''out.2.weight''']
				SCREAMING_SNAKE_CASE  :						List[Any]      					= checkpoint['''out.2.bias''']

				# Retrieves the keys for the input blocks only
				SCREAMING_SNAKE_CASE  :						List[str]      					= len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in checkpoint if '''input_blocks''' in layer}       )
				SCREAMING_SNAKE_CASE  :						int      					= {
				    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
				    for layer_id in range(_SCREAMING_SNAKE_CASE       )
				}

				# Retrieves the keys for the middle blocks only
				SCREAMING_SNAKE_CASE  :						Any      					= len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in checkpoint if '''middle_block''' in layer}       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= {
				    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
				    for layer_id in range(_SCREAMING_SNAKE_CASE       )
				}

				# Retrieves the keys for the output blocks only
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in checkpoint if '''output_blocks''' in layer}       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= {
				    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
				    for layer_id in range(_SCREAMING_SNAKE_CASE       )
				}

				for i in range(1    ,	_SCREAMING_SNAKE_CASE       ):
								SCREAMING_SNAKE_CASE  :						int      					= (i - 1) // (config['''num_res_blocks'''] + 1)
								SCREAMING_SNAKE_CASE  :						Any      					= (i - 1) % (config['''num_res_blocks'''] + 1)

								SCREAMING_SNAKE_CASE  :						Dict      					= [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= [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:
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= checkpoint[
												    F'''input_blocks.{i}.0.op.weight'''
												]
												SCREAMING_SNAKE_CASE  :						int      					= checkpoint[
												    F'''input_blocks.{i}.0.op.bias'''
												]
												continue

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

								if len(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= renew_attention_paths(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						int      					= {
												    '''old''': F'''input_blocks.{i}.1''',
												    '''new''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												SCREAMING_SNAKE_CASE  :						List[str]      					= {
												    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(
												    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	additional_replacements=[meta_path]    ,	attention_paths_to_split=_SCREAMING_SNAKE_CASE    ,	config=_SCREAMING_SNAKE_CASE    ,	)

				SCREAMING_SNAKE_CASE  :						str      					= middle_blocks[0]
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= middle_blocks[1]
				SCREAMING_SNAKE_CASE  :						int      					= middle_blocks[2]

				SCREAMING_SNAKE_CASE  :						Any      					= renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
				assign_to_checkpoint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	config=_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
				assign_to_checkpoint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	config=_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= renew_attention_paths(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Dict      					= {
				    '''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(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	attention_paths_to_split=_SCREAMING_SNAKE_CASE    ,	config=_SCREAMING_SNAKE_CASE       )

				for i in range(_SCREAMING_SNAKE_CASE       ):
								SCREAMING_SNAKE_CASE  :						str      					= i // (config['''num_res_blocks'''] + 1)
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= i % (config['''num_res_blocks'''] + 1)
								SCREAMING_SNAKE_CASE  :						Tuple      					= [shave_segments(_SCREAMING_SNAKE_CASE    ,	2       ) for name in output_blocks[i]]
								SCREAMING_SNAKE_CASE  :						List[str]      					= {}

								for layer in output_block_layers:
												SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= layer.split('''.'''       )[0], shave_segments(_SCREAMING_SNAKE_CASE    ,	1       )
												if layer_id in output_block_list:
																output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE       )
												else:
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= [layer_name]

								if len(_SCREAMING_SNAKE_CASE       ) > 1:
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
												SCREAMING_SNAKE_CASE  :						Tuple      					= [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

												SCREAMING_SNAKE_CASE  :						Dict      					= renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						List[Any]      					= renew_resnet_paths(_SCREAMING_SNAKE_CASE       )

												SCREAMING_SNAKE_CASE  :						str      					= {'''old''': F'''output_blocks.{i}.0''', '''new''': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	additional_replacements=[meta_path]    ,	config=_SCREAMING_SNAKE_CASE       )

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= list(output_block_list.values()       ).index(['''conv.weight''', '''conv.bias''']       )
																SCREAMING_SNAKE_CASE  :						List[str]      					= checkpoint[
																    F'''output_blocks.{i}.{index}.conv.weight'''
																]
																SCREAMING_SNAKE_CASE  :						Dict      					= checkpoint[
																    F'''output_blocks.{i}.{index}.conv.bias'''
																]

																# Clear attentions as they have been attributed above.
																if len(_SCREAMING_SNAKE_CASE       ) == 2:
																				SCREAMING_SNAKE_CASE  :						int      					= []

												if len(_SCREAMING_SNAKE_CASE       ):
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= renew_attention_paths(_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						List[Any]      					= {
																    '''old''': F'''output_blocks.{i}.1''',
																    '''new''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																}
																SCREAMING_SNAKE_CASE  :						str      					= {
																    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(
																    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	additional_replacements=[meta_path]    ,	attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions       ) else None    ,	config=_SCREAMING_SNAKE_CASE    ,	)
								else:
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= renew_resnet_paths(_SCREAMING_SNAKE_CASE    ,	n_shave_prefix_segments=1       )
												for path in resnet_0_paths:
																SCREAMING_SNAKE_CASE  :						List[Any]      					= '''.'''.join(['''output_blocks''', str(_SCREAMING_SNAKE_CASE       ), path['''old''']]       )
																SCREAMING_SNAKE_CASE  :						Any      					= '''.'''.join(['''up_blocks''', str(_SCREAMING_SNAKE_CASE       ), '''resnets''', str(_SCREAMING_SNAKE_CASE       ), path['''new''']]       )

																SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




