






"""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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A		:       List[Any]  				=		{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       List[str]  				=		[
						    '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MegaForCausalLM''',
						    '''MegaForMaskedLM''',
						    '''MegaForMultipleChoice''',
						    '''MegaForQuestionAnswering''',
						    '''MegaForSequenceClassification''',
						    '''MegaForTokenClassification''',
						    '''MegaModel''',
						    '''MegaPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mega import (
												    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MegaForCausalLM,
												    MegaForMaskedLM,
												    MegaForMultipleChoice,
												    MegaForQuestionAnswering,
												    MegaForSequenceClassification,
												    MegaForTokenClassification,
												    MegaModel,
												    MegaPreTrainedModel,
												)

else:
						import sys

						__A		:       Any  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



