






"""simple docstring"""


import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
		from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
		raise ImportError('You need to install the version of fairseq from the Mega repo!')




class 	__UpperCamelCase    (    nn.Module	):
					def __init__(       self       :  int    ,   UpperCAmelCase       :  Any    ,   UpperCAmelCase       :  Union[str, Any]    ,   UpperCAmelCase       :  Optional[int]      )  ->					str:
											super().__init__()
											lowerCAmelCase      :Any				  = mega_args
											lowerCAmelCase      :List[Any]				  = nn.Embedding(UpperCAmelCase    ,   self.mega_args.encoder_embed_dim      )
											lowerCAmelCase      :Optional[Any]				  = nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(UpperCAmelCase      )]      )
											lowerCAmelCase      :Union[str, Any]				  = depth
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Any    ,   UpperCAmelCase       :  List[str]=True    ,   UpperCAmelCase       :  Union[str, Any]=0      )  ->					int:






											if batch_first:
																	lowerCAmelCase      :int				  = input_ids.T

											# to make things more confusing, Mega expects the attention mask to
											# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
											# which is the opposite of what HF returns
											if ignore_mask_value == 0:
																	lowerCAmelCase      :List[str]				  = 1 - attention_mask

											# get token embeddings from IDs
											lowerCAmelCase      :Tuple				  = self.embedding_layer(UpperCAmelCase      )

											# pass through the Mega layers
											# input is (time, batch, encoder dim) and output is the same
											for encoder in self.encoders:
																	lowerCAmelCase      :Any				  = encoder(UpperCAmelCase    ,   UpperCAmelCase      )

											# return according to the shape specified
											if batch_first:
																	# (T, B, H) --> (B, T, H)
																	return torch.transpose(UpperCAmelCase    ,   0    ,   1      )
											else:
																	return embeds




class 	__UpperCamelCase    (    nn.Module	):
					def __init__(       self       :  Union[str, Any]    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  Dict      )  ->					Optional[Any]:
											super().__init__()
											lowerCAmelCase      :str				  = MegaLM(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :List[str]				  = nn.Linear(mega_args.encoder_embed_dim    ,   UpperCAmelCase      )
											lowerCAmelCase      :List[str]				  = nn.Dropout(p=0.1      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  Union[str, Any]=True    ,   UpperCAmelCase       :  List[str]=0      )  ->					Any:
											lowerCAmelCase      :Any				  = self.mega(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
											return self.mlm_head(self.dropout(UpperCAmelCase      )      )


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__   )     ->   Optional[Any]:





						'''simple docstring'''






						with open(os.path.join(a__    ,			'model_args.pkl'   )    ,			'rb'   ) as f:
												lowerCAmelCase      :List[str]				  = pkl.load(a__   )

						# load the original encoder
						lowerCAmelCase      :int				  = OriginalMegaForMaskedLM(**a__   ).eval()

						# load its weights
						print(
						    'Original Mega encoder:'    ,			original_mlm.mega.load_state_dict(
						        torch.load(os.path.join(a__    ,			'encoder_weights.pt'   )    ,			map_location='cpu'   )   )    ,			)
						print(
						    'Original Mega MLM layer:'    ,			original_mlm.mlm_head.load_state_dict(
						        torch.load(os.path.join(a__    ,			'mlm_head_weights.pt'   )    ,			map_location='cpu'   )   )    ,			)

						# create a new config from the old one
						lowerCAmelCase      :List[Any]				  = MegaConfig(
						    num_hidden_layers=mega_original_args['depth']    ,			vocab_size=mega_original_args['vocab_size']    ,			hidden_size=mega_original_args['mega_args'].encoder_embed_dim    ,			shared_representation_size=mega_original_args['mega_args'].encoder_z_dim    ,			intermediate_size=mega_original_args['mega_args'].encoder_hidden_dim    ,			ema_projection_size=mega_original_args['mega_args'].encoder_n_dim    ,			dropout_prob=mega_original_args['mega_args'].dropout    ,			attention_probs_dropout_prob=mega_original_args['mega_args'].attention_dropout    ,			hidden_dropout_prob=mega_original_args['mega_args'].hidden_dropout    ,			activation=mega_original_args['mega_args'].activation_fn    ,			attention_activation=mega_original_args['mega_args'].attention_activation_fn    ,			bidirectional=mega_original_args['mega_args'].bidirectional    ,			use_chunking=mega_original_args['mega_args'].encoder_chunk_size > 0    ,			chunk_size=mega_original_args['mega_args'].encoder_chunk_size    ,			truncation=mega_original_args['mega_args'].truncation_length    ,			normalization_type=mega_original_args['mega_args'].normalization_type    ,			normalize_before_mega=a__    ,			norm_affine=a__    ,			use_feature_dropout=mega_original_args['mega_args'].feature_dropout    ,			relative_positional_bias=mega_original_args['mega_args'].rel_pos_bias    ,			max_positions=mega_original_args['mega_args'].max_source_positions    ,			nffn_hidden_size=mega_original_args['mega_args'].encoder_ffn_embed_dim    ,			normalize_before_ffn=mega_original_args['mega_args'].normalize_before    ,			nffn_activation_dropout_prob=0.0    ,			add_token_type_embeddings=a__    ,			add_lm_hidden_dense_layer=a__    ,			)

						lowerCAmelCase      :str				  = MegaForMaskedLM(a__   ).eval()

						# the originl checkpoint just uses nn.Embedding for the word embeddings
						# we use a wrapper module for embeddings to add support for positional embeddings
						lowerCAmelCase      :str				  = original_mlm.mega.embedding_layer.weight

						# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
						# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
						# also renaming previously confusing parameter names
						lowerCAmelCase      :List[str]				  = original_mlm.mega.encoders.state_dict()
						lowerCAmelCase      :Dict				  = {}
						for module_name in original_state_dict.keys():
												lowerCAmelCase      :List[Any]				  = None
												# have to handle gamma, beta, and alpha differently due to their use
												# in multiple modules within the original repository;
												# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
												# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
												if "beta" in module_name:
																		# EMA sub-layers were always called "move" in the original repo
																		if "move.beta" in module_name:
																								lowerCAmelCase      :Union[str, Any]				  = module_name.replace('move.beta'    ,			'ema_gate.ema_expansion_matrix'   )
																		elif "mega_layer.beta" in module_name:
																								lowerCAmelCase      :Dict				  = module_name.replace('beta'    ,			'qk_bias'   )
																		else:
																								lowerCAmelCase      :Optional[int]				  = module_name.replace('beta'    ,			'b_param'   )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
												elif "gamma" in module_name:
																		if "move.gamma" in module_name:
																								lowerCAmelCase      :Optional[int]				  = module_name.replace('move.gamma'    ,			'ema_gate.kernel_projection_matrix'   )
																		elif "mega_layer.gamma" in module_name:
																								lowerCAmelCase      :Union[str, Any]				  = module_name.replace('gamma'    ,			'qk_weight'   )
																		else:
																								lowerCAmelCase      :Tuple				  = module_name.replace('gamma'    ,			'g_param'   )
        # alpha is used in EMA and positional bias; renaming to improve readability
												elif "move.alpha" in module_name:
																		lowerCAmelCase      :Tuple				  = module_name.replace('move.alpha'    ,			'ema_gate.decay_factor'   )
												# delta is only used in EMA; renaming to improve readability
												elif "move.delta" in module_name:
																		lowerCAmelCase      :Union[str, Any]				  = module_name.replace('move.delta'    ,			'ema_gate.damping_factor'   )
												# omega is only used in EMA; renaming to improve readability
												elif "omega" in module_name:
																		lowerCAmelCase      :Optional[Any]				  = module_name.replace('move.omega'    ,			'ema_gate.residual_weight'   )

												if new_module_name:
																		lowerCAmelCase      :List[str]				  = new_module_name

						if len(a__   ) != 0:
												print(F"""Renaming these keys: {updated_keys.keys()}"""   )
						else:
												print('No need to rename state dict entries'   )
						for old, new in updated_keys.items():
												lowerCAmelCase      :Any				  = original_state_dict.pop(a__   )

						# now attempt to load the state dictionary with updated names
						# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
						print('HF Mega encoder:'    ,			hf_mlm.mega.layers.load_state_dict(a__   )   )

						# load the MLM head weights directly
						print(
						    'HF Mega MLM layer:'    ,			hf_mlm.mlm_head.load_state_dict(
						        torch.load(os.path.join(a__    ,			'mlm_head_weights.pt'   )    ,			map_location='cpu'   )   )    ,			)

						# test on a randomly generated input sequence
						lowerCAmelCase      :Union[str, Any]				  = torch.randint(0    ,			hf_config.vocab_size    ,			size=(4, 2_56)   )
						lowerCAmelCase      :Union[str, Any]				  = torch.ones_like(a__   )
						# mask a few tokens to make sure masking is applied appropriately :)
						lowerCAmelCase      :int				  = 0

						# run forward passes
						lowerCAmelCase      :Optional[int]				  = original_mlm(a__    ,			a__    ,			batch_first=a__    ,			ignore_mask_value=0   )
						lowerCAmelCase      :str				  = hf_mlm(a__    ,			a__   )[0]

						# print shapes and diff
						print(F"""original output {original_output.shape}"""   )
						print(F"""hf output {hf_output.shape}"""   )
						print(F"""max diff: {(original_output - hf_output).max()}"""   )  # 0.0
						lowerCAmelCase      :str				  = torch.allclose(a__    ,			a__    ,			atol=1e-3   )

						if success:
												print('Yay!'   )
												hf_mlm.save_pretrained(a__   )
						else:
												raise RuntimeError(F"""Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"""   )

						if includes_tokenizer:
												print('Transferring tokenizer'   )
												lowerCAmelCase      :Union[str, Any]				  = AutoTokenizer.from_pretrained(a__   )
												tokenizer.save_pretrained(a__   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE      =      argparse.ArgumentParser()

		parser.add_argument(
		    '--pretrained_checkpoint_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Point to the directory containing your model weights using the official Mega repo',
		)

		parser.add_argument(
		    '--output_path', default=None, type=str, required=True, help='Location to save the Hugging Face version'
		)

		parser.add_argument(
		    '--includes_tokenizer',
		    action='store_true',
		    help='Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo',
		)

		__SCREAMING_SNAKE_CASE      =      parser.parse_args()

		convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)







"""simple docstring"""


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def 					UpperCAmelCase			(				a__    ,			a__=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 					UpperCAmelCase			(				a__    ,			a__=0   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = []
						for old_item in old_list:
												lowerCAmelCase      :Union[str, Any]				  = old_item
												lowerCAmelCase      :str				  = new_item.replace('block.'    ,			'resnets.'   )
												lowerCAmelCase      :int				  = new_item.replace('conv_shorcut'    ,			'conv1'   )
												lowerCAmelCase      :int				  = new_item.replace('in_shortcut'    ,			'conv_shortcut'   )
												lowerCAmelCase      :Union[str, Any]				  = new_item.replace('temb_proj'    ,			'time_emb_proj'   )

												lowerCAmelCase      :List[str]				  = shave_segments(a__    ,			n_shave_prefix_segments=a__   )

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

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__=0    ,			a__=False   ):





						'''simple docstring'''






						lowerCAmelCase      :Tuple				  = []
						for old_item in old_list:
												lowerCAmelCase      :int				  = old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		lowerCAmelCase      :int				  = new_item.replace('attn'    ,			'attentions'   )
												lowerCAmelCase      :List[str]				  = new_item.replace('.k.'    ,			'.key.'   )
												lowerCAmelCase      :Tuple				  = new_item.replace('.v.'    ,			'.value.'   )
												lowerCAmelCase      :int				  = new_item.replace('.q.'    ,			'.query.'   )

												lowerCAmelCase      :Optional[Any]				  = new_item.replace('proj_out'    ,			'proj_attn'   )
												lowerCAmelCase      :List[str]				  = new_item.replace('norm'    ,			'group_norm'   )

												lowerCAmelCase      :Any				  = shave_segments(a__    ,			n_shave_prefix_segments=a__   )
												mapping.append({'old': old_item, 'new': new_item}   )

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__=None    ,			a__=None    ,			a__=None   ):





						'''simple docstring'''






						assert isinstance(a__    ,			a__   ), "Paths should be a list of dicts containing 'old' and 'new' keys."

						if attention_paths_to_split is not None:
												if config is None:
																		raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'   )

												for path, path_map in attention_paths_to_split.items():
																		lowerCAmelCase      :Union[str, Any]				  = old_checkpoint[path]
																		lowerCAmelCase      :List[Any]				  = old_tensor.shape[0] // 3

																		lowerCAmelCase      :int				  = (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

																		lowerCAmelCase      :Optional[int]				  = old_tensor.shape[0] // config.get('num_head_channels'    ,			1   ) // 3

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

																		lowerCAmelCase      :str				  = query.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Dict				  = key.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Union[str, Any]				  = value.reshape(a__   ).squeeze()

						for path in paths:
												lowerCAmelCase      :Union[str, Any]				  = path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase      :Dict				  = new_path.replace('down.'    ,			'down_blocks.'   )
												lowerCAmelCase      :int				  = new_path.replace('up.'    ,			'up_blocks.'   )

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

												if "attentions" in new_path:
																		lowerCAmelCase      :Tuple				  = old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase      :Optional[int]				  = old_checkpoint[path['old']]


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['temb.dense.0.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['temb.dense.0.bias']
						lowerCAmelCase      :str				  = checkpoint['temb.dense.1.weight']
						lowerCAmelCase      :int				  = checkpoint['temb.dense.1.bias']

						lowerCAmelCase      :Dict				  = checkpoint['norm_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['norm_out.bias']

						lowerCAmelCase      :Optional[int]				  = checkpoint['conv_in.weight']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_in.bias']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['conv_out.bias']

						lowerCAmelCase      :Tuple				  = len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :Dict				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

						lowerCAmelCase      :Dict				  = len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'up' in layer}   )
						lowerCAmelCase      :List[Any]				  = {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(a__   )}

						for i in range(a__   ):
												lowerCAmelCase      :Union[str, Any]				  = (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :Tuple				  = checkpoint[
																		    F"""down.{i}.downsample.op.weight"""
																		]
																		lowerCAmelCase      :Optional[Any]				  = checkpoint[F"""down.{i}.downsample.op.bias"""]
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

												if any('block' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :List[Any]				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in down_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Optional[Any]				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Any				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__   )

												if any('attn' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :str				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in down_blocks[i] if 'attn' in layer}   )
																		lowerCAmelCase      :List[Any]				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Optional[Any]				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :str				  = [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase      :Union[str, Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]    ,			)

						lowerCAmelCase      :Union[str, Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_attention_paths(a__    ,			in_mid=a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,			)

						for i in range(a__   ):
												lowerCAmelCase      :Optional[Any]				  = num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :int				  = checkpoint[
																		    F"""up.{i}.upsample.conv.weight"""
																		]
																		lowerCAmelCase      :Any				  = checkpoint[F"""up.{i}.upsample.conv.bias"""]

												if any('block' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :Optional[Any]				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in up_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Dict				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(a__   )
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1   ):
																														lowerCAmelCase      :Tuple				  = {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																														lowerCAmelCase      :str				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			additional_replacements=[replace_indices]   )

												if any('attn' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :Dict				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in up_blocks[i] if 'attn' in layer}   )
																		lowerCAmelCase      :Optional[int]				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(a__   )
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1   ):
																														lowerCAmelCase      :List[str]				  = {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																														lowerCAmelCase      :Tuple				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			additional_replacements=[replace_indices]   )

						lowerCAmelCase      :Tuple				  = {k.replace('mid_new_2'    ,			'mid_block'   ): v for k, v in new_checkpoint.items()}
						return new_checkpoint


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['encoder.norm_out.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['encoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['encoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['encoder.conv_in.bias']
						lowerCAmelCase      :Dict				  = checkpoint['encoder.conv_out.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['encoder.conv_out.bias']

						lowerCAmelCase      :Dict				  = checkpoint['decoder.norm_out.weight']
						lowerCAmelCase      :List[str]				  = checkpoint['decoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['decoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['decoder.conv_in.bias']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['decoder.conv_out.weight']
						lowerCAmelCase      :int				  = checkpoint['decoder.conv_out.bias']

						lowerCAmelCase      :List[str]				  = len({'.'.join(layer.split('.'   )[:3]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :int				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

						lowerCAmelCase      :Any				  = len({'.'.join(layer.split('.'   )[:3]   ) for layer in checkpoint if 'up' in layer}   )
						lowerCAmelCase      :int				  = {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(a__   )}

						for i in range(a__   ):
												lowerCAmelCase      :Tuple				  = (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :List[Any]				  = checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.weight"""
																		]
																		lowerCAmelCase      :Optional[int]				  = checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.bias"""
																		]

												if any('block' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :int				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in down_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :str				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :List[Any]				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__   )

												if any('attn' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :Dict				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in down_blocks[i] if 'attn' in layer}   )
																		lowerCAmelCase      :Any				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Any				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Tuple				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase      :Optional[Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_attention_paths(a__    ,			in_mid=a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,			)

						for i in range(a__   ):
												lowerCAmelCase      :Optional[int]				  = num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :str				  = checkpoint[
																		    F"""decoder.up.{i}.upsample.conv.weight"""
																		]
																		lowerCAmelCase      :List[Any]				  = checkpoint[
																		    F"""decoder.up.{i}.upsample.conv.bias"""
																		]

												if any('block' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :Dict				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in up_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Optional[Any]				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(a__   )
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1   ):
																														lowerCAmelCase      :List[Any]				  = {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																														lowerCAmelCase      :List[Any]				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			additional_replacements=[replace_indices]   )

												if any('attn' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :str				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in up_blocks[i] if 'attn' in layer}   )
																		lowerCAmelCase      :Optional[int]				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(a__   )
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1   ):
																														lowerCAmelCase      :List[str]				  = {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																														lowerCAmelCase      :int				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			additional_replacements=[replace_indices]   )

						lowerCAmelCase      :Optional[Any]				  = {k.replace('mid_new_2'    ,			'mid_block'   ): v for k, v in new_checkpoint.items()}
						lowerCAmelCase      :Optional[int]				  = checkpoint['quant_conv.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['quant_conv.bias']
						if "quantize.embedding.weight" in checkpoint:
												lowerCAmelCase      :Optional[Any]				  = checkpoint['quantize.embedding.weight']
						lowerCAmelCase      :Any				  = checkpoint['post_quant_conv.weight']
						lowerCAmelCase      :str				  = checkpoint['post_quant_conv.bias']

						return new_checkpoint


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

		__SCREAMING_SNAKE_CASE      =      parser.parse_args()
		__SCREAMING_SNAKE_CASE      =      torch.load(args.checkpoint_path)

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

		# unet case
		__SCREAMING_SNAKE_CASE      =      {key.split('.')[0] for key in checkpoint.keys()}
		if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
				__SCREAMING_SNAKE_CASE      =      convert_vq_autoenc_checkpoint(checkpoint, config)
		else:
				__SCREAMING_SNAKE_CASE      =      convert_ddpm_checkpoint(checkpoint, config)

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

		if config["_class_name"] == "VQModel":
				__SCREAMING_SNAKE_CASE      =      VQModel(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		elif config["_class_name"] == "AutoencoderKL":
				__SCREAMING_SNAKE_CASE      =      AutoencoderKL(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		else:
				__SCREAMING_SNAKE_CASE      =      UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				__SCREAMING_SNAKE_CASE      =      DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

				__SCREAMING_SNAKE_CASE      =      DDPMPipeline(unet=model, scheduler=scheduler)
				pipe.save_pretrained(args.dump_path)