




'''simple docstring'''


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Any=1   ):





							'''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 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						List[str]=0   ):





							'''simple docstring'''


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

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

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

														_a			  =       shave_segments(UpperCamelCase				,		n_shave_prefix_segments=UpperCamelCase   )

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

							return mapping
def 	snake_case_					(UpperCamelCase :						List[Any]				,		UpperCamelCase :						Union[str, Any]=0   ):





							'''simple docstring'''


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

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

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

														_a			  =       shave_segments(UpperCamelCase				,		n_shave_prefix_segments=UpperCamelCase   )

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

							return mapping
def 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						int				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						Tuple=None				,		UpperCamelCase :						Dict=None				,		UpperCamelCase :						List[str]=None   ):





							'''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():
																					_a			  =       old_checkpoint[path]
																					_a			  =       old_tensor.shape[0] // 3

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

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

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

																					_a			  =       query.reshape(UpperCamelCase   )
																					_a			  =       key.reshape(UpperCamelCase   )
																					_a			  =       value.reshape(UpperCamelCase   )

							for path in paths:
														_a			  =       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
														_a			  =       new_path.replace('''middle_block.0'''				,		'''mid_block.resnets.0'''   )
														_a			  =       new_path.replace('''middle_block.1'''				,		'''mid_block.attentions.0'''   )
														_a			  =       new_path.replace('''middle_block.2'''				,		'''mid_block.resnets.1'''   )

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												_a			  =       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:
																					_a			  =       old_checkpoint[path['''old''']][:, :, 0]
														else:
																					_a			  =       old_checkpoint[path['''old''']]
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       {}

							_a			  =       checkpoint['''time_embed.0.weight''']
							_a			  =       checkpoint['''time_embed.0.bias''']
							_a			  =       checkpoint['''time_embed.2.weight''']
							_a			  =       checkpoint['''time_embed.2.bias''']

							_a			  =       checkpoint['''input_blocks.0.0.weight''']
							_a			  =       checkpoint['''input_blocks.0.0.bias''']

							_a			  =       checkpoint['''out.0.weight''']
							_a			  =       checkpoint['''out.0.bias''']
							_a			  =       checkpoint['''out.2.weight''']
							_a			  =       checkpoint['''out.2.bias''']

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

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

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

							for i in range(1				,		UpperCamelCase   ):
														_a			  =       (i - 1) // (config['''num_res_blocks'''] + 1)
														_a			  =       (i - 1) % (config['''num_res_blocks'''] + 1)

														_a			  =       [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
														_a			  =       [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:
																					_a			  =       checkpoint[
																					    f'input_blocks.{i}.0.op.weight'
																					]
																					_a			  =       checkpoint[
																					    f'input_blocks.{i}.0.op.bias'
																					]
																					continue

														_a			  =       renew_resnet_paths(UpperCamelCase   )
														_a			  =       {'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
														_a			  =       {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
														assign_to_checkpoint(
														    UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		additional_replacements=[meta_path, resnet_op]				,		config=UpperCamelCase   )

														if len(UpperCamelCase   ):
																					_a			  =       renew_attention_paths(UpperCamelCase   )
																					_a			  =       {
																					    '''old''': f'input_blocks.{i}.1',
																					    '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
																					}
																					_a			  =       {
																					    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(
																					    UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		additional_replacements=[meta_path]				,		attention_paths_to_split=UpperCamelCase				,		config=UpperCamelCase				,		)

							_a			  =       middle_blocks[0]
							_a			  =       middle_blocks[1]
							_a			  =       middle_blocks[2]

							_a			  =       renew_resnet_paths(UpperCamelCase   )
							assign_to_checkpoint(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		config=UpperCamelCase   )

							_a			  =       renew_resnet_paths(UpperCamelCase   )
							assign_to_checkpoint(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		config=UpperCamelCase   )

							_a			  =       renew_attention_paths(UpperCamelCase   )
							_a			  =       {
							    '''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(
							    UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		attention_paths_to_split=UpperCamelCase				,		config=UpperCamelCase   )

							for i in range(UpperCamelCase   ):
														_a			  =       i // (config['''num_res_blocks'''] + 1)
														_a			  =       i % (config['''num_res_blocks'''] + 1)
														_a			  =       [shave_segments(UpperCamelCase				,		2   ) for name in output_blocks[i]]
														_a			  =       {}

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

														if len(UpperCamelCase   ) > 1:
																					_a			  =       [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
																					_a			  =       [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

																					_a			  =       renew_resnet_paths(UpperCamelCase   )
																					_a			  =       renew_resnet_paths(UpperCamelCase   )

																					_a			  =       {'''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   )

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

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

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

																												_a			  =       checkpoint[old_path]

							return new_checkpoint


if __name__ == "__main__":
				_snake_case   :   Union[str, Any]     							=	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   :   List[Any]     							=	parser.parse_args()

				_snake_case   :   Dict     							=	torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								_snake_case   :   Optional[Any]     							=	json.loads(f.read())

				_snake_case   :   List[Any]     							=	convert_ldm_checkpoint(checkpoint, config)

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

				_snake_case   :   str     							=	UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

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

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







'''simple docstring'''


_snake_case   :   Any     							=	tuple[float, float, float]
_snake_case   :   Optional[int]     							=	tuple[float, float, float]
def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad   ):





							'''simple docstring'''


							_a			  =       end_pointa[0] - end_pointa[0]
							_a			  =       end_pointa[1] - end_pointa[1]
							_a			  =       end_pointa[2] - end_pointa[2]
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						Vectorad   ):





							'''simple docstring'''


							_a			  =       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							_a			  =       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							_a			  =       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							return tuple(round(UpperCamelCase				,		UpperCamelCase   ) for x in vector   ) == (0, 0, 0)

def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						int = 10   ):





							'''simple docstring'''


							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							return is_zero_vector(get_ad_vectors_cross(UpperCamelCase				,		UpperCamelCase   )				,		UpperCamelCase   )

