






"""simple docstring"""



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Tuple=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    (				__snake_case				: str		,					__snake_case				: str=0	):
		lowercase_ : Dict							       =   []
		for old_item in old_list:
				lowercase_ : Optional[Any]							       =   old_item.replace('''in_layers.0'''		,					'''norm1'''	)
				lowercase_ : List[Any]							       =   new_item.replace('''in_layers.2'''		,					'''conv1'''	)

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

				lowercase_ : Any							       =   new_item.replace('''emb_layers.1'''		,					'''time_emb_proj'''	)
				lowercase_ : Tuple							       =   new_item.replace('''skip_connection'''		,					'''conv_shortcut'''	)

				lowercase_ : Union[str, Any]							       =   shave_segments(__snake_case		,					n_shave_prefix_segments=__snake_case	)

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

		return mapping



def 						lowercase    (				__snake_case				: Union[str, Any]		,					__snake_case				: Optional[int]=0	):
		lowercase_ : List[Any]							       =   []
		for old_item in old_list:
				lowercase_ : Optional[int]							       =   old_item

				lowercase_ : Tuple							       =   new_item.replace('''norm.weight'''		,					'''group_norm.weight'''	)
				lowercase_ : Dict							       =   new_item.replace('''norm.bias'''		,					'''group_norm.bias'''	)

				lowercase_ : Optional[Any]							       =   new_item.replace('''proj_out.weight'''		,					'''proj_attn.weight'''	)
				lowercase_ : Dict							       =   new_item.replace('''proj_out.bias'''		,					'''proj_attn.bias'''	)

				lowercase_ : Tuple							       =   shave_segments(__snake_case		,					n_shave_prefix_segments=__snake_case	)

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

		return mapping



def 						lowercase    (				__snake_case				: str		,					__snake_case				: int		,					__snake_case				: Optional[Any]		,					__snake_case				: Optional[int]=None		,					__snake_case				: Union[str, Any]=None		,					__snake_case				: Dict=None	):
		assert isinstance(__snake_case		,					__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():
						lowercase_ : List[str]							       =   old_checkpoint[path]
						lowercase_ : Union[str, Any]							       =   old_tensor.shape[0] // 3

						lowercase_ : List[str]							       =   (-1, channels) if len(old_tensor.shape	) == 3 else (-1)

						lowercase_ : Optional[Any]							       =   old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

						lowercase_ : int							       =   query.reshape(__snake_case	)
						lowercase_ : Optional[int]							       =   key.reshape(__snake_case	)
						lowercase_ : Optional[int]							       =   value.reshape(__snake_case	)

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

				if additional_replacements is not None:
						for replacement in additional_replacements:
								lowercase_ : List[str]							       =   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:
						lowercase_ : List[Any]							       =   old_checkpoint[path['''old''']][:, :, 0]
				else:
						lowercase_ : str							       =   old_checkpoint[path['''old''']]



def 						lowercase    (				__snake_case				: int		,					__snake_case				: List[Any]	):
		lowercase_ : Any							       =   {}

		lowercase_ : str							       =   checkpoint['''time_embed.0.weight''']
		lowercase_ : Tuple							       =   checkpoint['''time_embed.0.bias''']
		lowercase_ : Tuple							       =   checkpoint['''time_embed.2.weight''']
		lowercase_ : Dict							       =   checkpoint['''time_embed.2.bias''']

		lowercase_ : Tuple							       =   checkpoint['''input_blocks.0.0.weight''']
		lowercase_ : Union[str, Any]							       =   checkpoint['''input_blocks.0.0.bias''']

		lowercase_ : Dict							       =   checkpoint['''out.0.weight''']
		lowercase_ : Optional[int]							       =   checkpoint['''out.0.bias''']
		lowercase_ : Union[str, Any]							       =   checkpoint['''out.2.weight''']
		lowercase_ : Tuple							       =   checkpoint['''out.2.bias''']

		# Retrieves the keys for the input blocks only
		lowercase_ : Union[str, Any]							       =   len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''input_blocks''' in layer}	)
		lowercase_ : Tuple							       =   {
		    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
		    for layer_id in range(__snake_case	)
		}

		# Retrieves the keys for the middle blocks only
		lowercase_ : str							       =   len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''middle_block''' in layer}	)
		lowercase_ : Optional[int]							       =   {
		    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
		    for layer_id in range(__snake_case	)
		}

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

		for i in range(1		,					__snake_case	):
				lowercase_ : List[str]							       =   (i - 1) // (config['''num_res_blocks'''] + 1)
				lowercase_ : str							       =   (i - 1) % (config['''num_res_blocks'''] + 1)

				lowercase_ : List[Any]							       =   [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
				lowercase_ : List[str]							       =   [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:
						lowercase_ : Any							       =   checkpoint[
						    F'''input_blocks.{i}.0.op.weight'''
						]
						lowercase_ : Union[str, Any]							       =   checkpoint[
						    F'''input_blocks.{i}.0.op.bias'''
						]
						continue

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

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

		lowercase_ : List[Any]							       =   middle_blocks[0]
		lowercase_ : List[str]							       =   middle_blocks[1]
		lowercase_ : Union[str, Any]							       =   middle_blocks[2]

		lowercase_ : Dict							       =   renew_resnet_paths(__snake_case	)
		assign_to_checkpoint(__snake_case		,					__snake_case		,					__snake_case		,					config=__snake_case	)

		lowercase_ : Tuple							       =   renew_resnet_paths(__snake_case	)
		assign_to_checkpoint(__snake_case		,					__snake_case		,					__snake_case		,					config=__snake_case	)

		lowercase_ : int							       =   renew_attention_paths(__snake_case	)
		lowercase_ : Union[str, Any]							       =   {
		    '''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(
		    __snake_case		,					__snake_case		,					__snake_case		,					attention_paths_to_split=__snake_case		,					config=__snake_case	)

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

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

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

						lowercase_ : int							       =   renew_resnet_paths(__snake_case	)
						lowercase_ : Dict							       =   renew_resnet_paths(__snake_case	)

						lowercase_ : Optional[Any]							       =   {'''old''': F'''output_blocks.{i}.0''', '''new''': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						assign_to_checkpoint(__snake_case		,					__snake_case		,					__snake_case		,					additional_replacements=[meta_path]		,					config=__snake_case	)

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

								# Clear attentions as they have been attributed above.
								if len(__snake_case	) == 2:
										lowercase_ : Dict							       =   []

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

								lowercase_ : Tuple							       =   checkpoint[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(
						    '''--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.''')

						__A		:       Tuple  				=		parser.parse_args()

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

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

						__A		:       str  				=		convert_ldm_checkpoint(checkpoint, config)

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

						__A		:       Optional[Any]  				=		UNetaDModel(**config)
						model.load_state_dict(converted_checkpoint)

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

												__A		:       Tuple  				=		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 operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							return getitem, k



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			List[str]:
							return setitem, k, v



def       __lowerCamelCase  (					_lowercase		)      ->			int:
							return delitem, k



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				*_lowercase		)      ->			Optional[Any]:
							try:
														return fun(_lowercase	,				*_lowercase		), None
							except Exception as e:
														return None, e


a   :			List[str]					  =    (
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

a   :			List[Any]					  =    [
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

a   :			int					  =    [
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

a   :			List[Any]					  =    [
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

a   :			Tuple					  =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a   :			Optional[Any]					  =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]



@pytest.mark.parametrize(
    """operations"""	,				(
        pytest.param(_add_items	,				id="""add items"""		),
        pytest.param(_overwrite_items	,				id="""overwrite items"""		),
        pytest.param(_delete_items	,				id="""delete items"""		),
        pytest.param(_access_absent_items	,				id="""access absent items"""		),
        pytest.param(_add_with_resize_up	,				id="""add with resize up"""		),
        pytest.param(_add_with_resize_down	,				id="""add with resize down"""		),
    )	,				)
def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		List[str]							     =		HashMap(initial_block_size=4		)
							UpperCAmelCase     :		Dict							     =		{}
							for _, (fun, *args) in enumerate(_lowercase		):
														UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		_run_operation(_lowercase	,				_lowercase	,				*_lowercase		)
														UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		_run_operation(_lowercase	,				_lowercase	,				*_lowercase		)
														assert my_res == py_res
														assert str(_lowercase		) == str(_lowercase		)
														assert set(_lowercase		) == set(_lowercase		)
														assert len(_lowercase		) == len(_lowercase		)
														assert set(my.items()		) == set(py.items()		)



def       __lowerCamelCase  (					)      ->			List[Any]:
							def is_public(_lowercase		) -> bool:
														return not name.startswith("""_"""		)

							UpperCAmelCase     :		int							     =		{name for name in dir({}		) if is_public(_lowercase		)}
							UpperCAmelCase     :		Any							     =		{name for name in dir(HashMap()		) if is_public(_lowercase		)}

							assert dict_public_names > hash_public_names

