

# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=0						)       ->       int:

 """simple docstring"""






 if name is None:
  __lowerCAmelCase:  Optional[int]        = None
 else:
  __lowerCAmelCase:  Dict        = '.' * max(0       ,   spaces - 2						) + '# {:' + str(50 - spaces						) + 's}'
  __lowerCAmelCase:  List[Any]        = fmt.format(SCREAMING_SNAKE_CASE						)

 # Print and recurse (if needed).
 if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
  if msg is not None:
   print(SCREAMING_SNAKE_CASE						)
  for k in val.keys():
   recursive_print(SCREAMING_SNAKE_CASE       ,   val[k]       ,   spaces + 2						)
 elif isinstance(SCREAMING_SNAKE_CASE       ,   torch.Tensor						):
  print(SCREAMING_SNAKE_CASE       ,   ':'       ,   val.size()						)
 else:
  print(SCREAMING_SNAKE_CASE       ,   ':'       ,   SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  Optional[int]        = param.size()
 if checkpoint_version == 1.0:
  # version 1.0 stores [num_heads * hidden_size * num_splits, :]
  __lowerCAmelCase:  Optional[int]        = (num_heads, hidden_size, num_splits) + input_shape[1:]
  __lowerCAmelCase:  str        = param.view(*SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  int        = param.transpose(0       ,   2						)
  __lowerCAmelCase:  List[str]        = param.transpose(1       ,   2						).contiguous()
 elif checkpoint_version >= 2.0:
  # other versions store [num_heads * num_splits * hidden_size, :]
  __lowerCAmelCase:  Tuple        = (num_heads, num_splits, hidden_size) + input_shape[1:]
  __lowerCAmelCase:  Optional[int]        = param.view(*SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Tuple        = param.transpose(0       ,   1						).contiguous()
 __lowerCAmelCase:  str        = param.view(*SCREAMING_SNAKE_CASE						)
 return param







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  Optional[int]        = {}

 # old versions did not store training args
 __lowerCAmelCase:  int        = input_state_dict.get('args'       ,   SCREAMING_SNAKE_CASE						)
 if ds_args is not None:
  # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
  # from pprint import pprint
  # pprint(vars(ds_args))

  __lowerCAmelCase:  Dict        = ds_args.tokenizer_type
  __lowerCAmelCase:  Optional[int]        = ds_args.padded_vocab_size
  __lowerCAmelCase:  List[Any]        = ds_args.max_position_embeddings
  __lowerCAmelCase:  List[Any]        = ds_args.hidden_size
  __lowerCAmelCase:  Union[str, Any]        = ds_args.num_layers
  __lowerCAmelCase:  List[str]        = ds_args.num_attention_heads
  __lowerCAmelCase:  Dict        = ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
  # pprint(config)

 # The number of heads.
 __lowerCAmelCase:  Union[str, Any]        = config.num_attention_heads
 # The hidden_size per head.
 __lowerCAmelCase:  List[str]        = config.hidden_size // heads
 # Megatron-LM checkpoint version
 if "checkpoint_version" in input_state_dict.keys():
  __lowerCAmelCase:  int        = input_state_dict['checkpoint_version']
 else:
  __lowerCAmelCase:  Any        = 0.0

 # The model.
 __lowerCAmelCase:  Dict        = input_state_dict['model']
 # The language model.
 __lowerCAmelCase:  Optional[int]        = model['language_model']
 # The embeddings.
 __lowerCAmelCase:  str        = lm['embedding']

 # The word embeddings.
 __lowerCAmelCase:  Union[str, Any]        = embeddings['word_embeddings']['weight']
 # Truncate the embedding table to vocab_size rows.
 __lowerCAmelCase:  Optional[Any]        = word_embeddings[: config.vocab_size, :]
 # Store the word embeddings.
 __lowerCAmelCase:  Optional[Any]        = word_embeddings

 # The position embeddings.
 __lowerCAmelCase:  int        = embeddings['position_embeddings']['weight']
 assert pos_embeddings.size(0						) == config.max_position_embeddings and pos_embeddings.size(1						) == config.hidden_size
 # Store the position embeddings.
 __lowerCAmelCase:  Dict        = pos_embeddings

 # The token-type embeddings.
 __lowerCAmelCase:  List[str]        = embeddings['tokentype_embeddings']['weight']
 # Store the position embeddings.
 __lowerCAmelCase:  Optional[int]        = tokentype_embeddings

 # The transformer.
 __lowerCAmelCase:  Union[str, Any]        = lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

 # The regex to extract layer names.
 __lowerCAmelCase:  str        = re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'						)

 # The simple map of names for "automated" rules.
 __lowerCAmelCase:  List[Any]        = {
     'attention.dense': '.attention.output.dense.',
     'self_attention.dense': '.attention.output.dense.',
     'mlp.dense_h_to_4h': '.intermediate.dense.',
     'mlp.dense_4h_to_h': '.output.dense.',
 }

 # Keep track of the attention/query/value tensor.
 __lowerCAmelCase:  Any        = None

 # Extract the layers.
 for key, val in transformer.items():
  # Match the name.
  __lowerCAmelCase:  Any        = layer_re.match(SCREAMING_SNAKE_CASE						)

  # Stop if that's not a layer
  if m is None:
   break

  # The index of the layer.
  __lowerCAmelCase:  str        = int(m.group(1						)						)
  # The name of the operation.
  __lowerCAmelCase:  Union[str, Any]        = m.group(2						)
  # Is it a weight or a bias?
  __lowerCAmelCase:  Union[str, Any]        = m.group(3						)

  # The name of the layer.
  __lowerCAmelCase:  List[str]        = f'''bert.encoder.layer.{layer_idx}'''

  # For layernorm(s), simply store the layer norm.
  if op_name.endswith('layernorm'						):
   __lowerCAmelCase:  List[str]        = 'attention.ln' if op_name.startswith('input'						) else 'ln'
   __lowerCAmelCase:  List[Any]        = val

  # Transpose the QKV matrix.
  elif (
      op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
  ) and weight_or_bias == "weight":
   # Make sure the QKV pointer is nil.
   assert attention_qkv_weight is None, ""

   __lowerCAmelCase:  Optional[Any]        = fix_query_key_value_ordering(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   3       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
   # Store the tensor as we need the bias as well to interleave QKV and biases.
   __lowerCAmelCase:  List[str]        = out_val

  # Transpose the bias.
  elif (
      op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
  ) and weight_or_bias == "bias":
   # Make sure we read the weight tensor.
   assert attention_qkv_weight is not None, ""

   # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
   __lowerCAmelCase:  Optional[int]        = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
   __lowerCAmelCase:  int        = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
   __lowerCAmelCase:  Dict        = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

   __lowerCAmelCase:  Any        = fix_query_key_value_ordering(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   3       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
   # Split the bias.
   __lowerCAmelCase:  Optional[int]        = out_val[0 * config.hidden_size : 1 * config.hidden_size]
   __lowerCAmelCase:  int        = out_val[1 * config.hidden_size : 2 * config.hidden_size]
   __lowerCAmelCase:  str        = out_val[2 * config.hidden_size : 3 * config.hidden_size]

   # Store.
   __lowerCAmelCase:  Any        = q
   __lowerCAmelCase:  Optional[int]        = q_bias
   __lowerCAmelCase:  List[Any]        = k
   __lowerCAmelCase:  Tuple        = k_bias
   __lowerCAmelCase:  Optional[int]        = v
   __lowerCAmelCase:  Optional[int]        = v_bias

   # Clear the stored tensor.
   __lowerCAmelCase:  Any        = None

  # Copy weights and biases as is.
  elif weight_or_bias in ["weight", "bias"]:
   __lowerCAmelCase:  int        = megatron_to_transformers[op_name]
   __lowerCAmelCase:  int        = val

    # The final layernorm.
 __lowerCAmelCase:  str        = transformer['final_layernorm.weight']
 __lowerCAmelCase:  Dict        = transformer['final_layernorm.bias']

 # The pooler.
 __lowerCAmelCase:  List[str]        = lm['pooler']

 # Store the matrix and the bias.
 __lowerCAmelCase:  Optional[Any]        = pooler['dense.weight']
 __lowerCAmelCase:  int        = pooler['dense.bias']

 # The LM head from Megatron (for RACE).
 __lowerCAmelCase:  Union[str, Any]        = model['lm_head']

 # The transform matrix.
 __lowerCAmelCase:  int        = lm_head['dense.weight']
 __lowerCAmelCase:  Optional[Any]        = lm_head['dense.bias']

 # The transform LN.
 __lowerCAmelCase:  Tuple        = lm_head['layernorm.weight']
 __lowerCAmelCase:  Dict        = lm_head['layernorm.bias']

 # For the decoder, we replicate the weights.
 __lowerCAmelCase:  str        = word_embeddings
 __lowerCAmelCase:  Optional[int]        = lm_head['bias']

 # The classifier from Megatron (for MLNI).
 __lowerCAmelCase:  str        = model['binary_head']

 # Store the classifier.
 __lowerCAmelCase:  Any        = binary_head['weight']
 __lowerCAmelCase:  Optional[int]        = binary_head['bias']

 # It should be done!
 return output_state_dict







def _a					(			)       ->       Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:  Optional[int]        = argparse.ArgumentParser()
 parser.add_argument('--print-checkpoint-structure'       ,   action='store_true'						)
 parser.add_argument('path_to_checkpoint'       ,   type=SCREAMING_SNAKE_CASE       ,   help='Path to the ZIP file containing the checkpoint'						)
 parser.add_argument(
     '--config_file'       ,   default=''       ,   type=SCREAMING_SNAKE_CASE       ,   help='An optional config json file describing the pre-trained model.'       ,   )
 __lowerCAmelCase:  int        = parser.parse_args()

 # Extract the basename.
 __lowerCAmelCase:  Any        = os.path.dirname(args.path_to_checkpoint						)

 # Load the model.
 # the .zip is very optional, let's keep it for backward compatibility
 print(f'''Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'''						)
 if args.path_to_checkpoint.endswith('.zip'						):
  with zipfile.ZipFile(args.path_to_checkpoint       ,   'r'						) as checkpoint:
   with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'						) as pytorch_dict:
    __lowerCAmelCase:  Optional[Any]        = torch.load(SCREAMING_SNAKE_CASE       ,   map_location='cpu'						)
 else:
  __lowerCAmelCase:  Optional[Any]        = torch.load(args.path_to_checkpoint       ,   map_location='cpu'						)

 if args.config_file == "":
  # Default config of megatron-bert 345m
  __lowerCAmelCase:  Optional[int]        = MegatronBertConfig()

  # different megatron-bert-*-345m models have different vocab sizes, so override the default
  # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
  __lowerCAmelCase:  Any        = input_state_dict['model']['lm_head']['bias'].numel()
 else:
  __lowerCAmelCase:  Any        = MegatronBertConfig.from_json_file(args.config_file						)

 # Convert.
 print('Converting'						)
 __lowerCAmelCase:  Optional[Any]        = convert_megatron_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 # Print the structure of converted state dict.
 if args.print_checkpoint_structure:
  recursive_print(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 # Store the config to file.
 print('Saving config'						)
 config.save_pretrained(SCREAMING_SNAKE_CASE						)

 # Store the state_dict to file.
 __lowerCAmelCase:  List[str]        = os.path.join(SCREAMING_SNAKE_CASE       ,   'pytorch_model.bin'						)
 print(f'''Saving checkpoint to "{output_checkpoint_file}"'''						)
 torch.save(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)


####################################################################################################

if __name__ == "__main__":
  main()

####################################################################################################








import argparse
import json

import torch

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







def _a					(			SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=1						)       ->       int:

 """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 _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     str=0						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = []
 for old_item in old_list:
  __lowerCAmelCase:  Optional[Any]        = old_item
  __lowerCAmelCase:  List[str]        = new_item.replace('block.'       ,   'resnets.'						)
  __lowerCAmelCase:  Dict        = new_item.replace('conv_shorcut'       ,   'conv1'						)
  __lowerCAmelCase:  Optional[Any]        = new_item.replace('in_shortcut'       ,   'conv_shortcut'						)
  __lowerCAmelCase:  str        = new_item.replace('temb_proj'       ,   'time_emb_proj'						)

  __lowerCAmelCase:  Any        = shave_segments(SCREAMING_SNAKE_CASE       ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE						)

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

 return mapping







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Dict=0       ,   SCREAMING_SNAKE_CASE						:     str=False						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  str        = []
 for old_item in old_list:
  __lowerCAmelCase:  Optional[int]        = old_item

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

  __lowerCAmelCase:  List[str]        = new_item.replace('proj_out'       ,   'proj_attn'						)
  __lowerCAmelCase:  Tuple        = new_item.replace('norm'       ,   'group_norm'						)

  __lowerCAmelCase:  int        = shave_segments(SCREAMING_SNAKE_CASE       ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE						)
  mapping.append({'old': old_item, 'new': new_item}						)

 return mapping







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Any=None       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]=None       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=None						)       ->       Union[str, Any]:

 """simple docstring"""






 assert isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						), "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:  List[Any]        = old_checkpoint[path]
   __lowerCAmelCase:  Tuple        = old_tensor.shape[0] // 3

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

   __lowerCAmelCase:  Any        = old_tensor.shape[0] // config.get('num_head_channels'       ,   1						) // 3

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

   __lowerCAmelCase:  Union[str, Any]        = query.reshape(SCREAMING_SNAKE_CASE						).squeeze()
   __lowerCAmelCase:  Dict        = key.reshape(SCREAMING_SNAKE_CASE						).squeeze()
   __lowerCAmelCase:  List[Any]        = value.reshape(SCREAMING_SNAKE_CASE						).squeeze()

 for path in paths:
  __lowerCAmelCase:  Optional[Any]        = path['new']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  __lowerCAmelCase:  Optional[int]        = new_path.replace('down.'       ,   'down_blocks.'						)
  __lowerCAmelCase:  List[Any]        = new_path.replace('up.'       ,   'up_blocks.'						)

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

  if "attentions" in new_path:
   __lowerCAmelCase:  Optional[Any]        = old_checkpoint[path['old']].squeeze()
  else:
   __lowerCAmelCase:  List[Any]        = old_checkpoint[path['old']]







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     Tuple						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Tuple        = {}

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

 __lowerCAmelCase:  Tuple        = checkpoint['norm_out.weight']
 __lowerCAmelCase:  Dict        = checkpoint['norm_out.bias']

 __lowerCAmelCase:  Dict        = checkpoint['conv_in.weight']
 __lowerCAmelCase:  List[str]        = checkpoint['conv_in.bias']
 __lowerCAmelCase:  str        = checkpoint['conv_out.weight']
 __lowerCAmelCase:  Tuple        = checkpoint['conv_out.bias']

 __lowerCAmelCase:  List[Any]        = len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'down' in layer}						)
 __lowerCAmelCase:  Union[str, Any]        = {
     layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
 }

 __lowerCAmelCase:  Optional[int]        = len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'up' in layer}						)
 __lowerCAmelCase:  List[str]        = {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)}

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Any        = (i - 1) // (config['layers_per_block'] + 1)

  if any('downsample' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  List[Any]        = checkpoint[
       f'''down.{i}.downsample.op.weight'''
   ]
   __lowerCAmelCase:  Tuple        = 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:  Optional[Any]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Union[str, Any]        = {
       layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

   if num_blocks > 0:
    for j in range(config['layers_per_block']						):
     __lowerCAmelCase:  int        = renew_resnet_paths(blocks[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  if any('attn' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Optional[int]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Union[str, Any]        = {
       layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

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

 __lowerCAmelCase:  Union[str, Any]        = [key for key in checkpoint if 'mid.block_1' in key]
 __lowerCAmelCase:  List[Any]        = [key for key in checkpoint if 'mid.block_2' in key]
 __lowerCAmelCase:  Optional[Any]        = [key for key in checkpoint if 'mid.attn_1' in key]

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

 __lowerCAmelCase:  int        = renew_resnet_paths(SCREAMING_SNAKE_CASE						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,   )

 __lowerCAmelCase:  Tuple        = renew_attention_paths(SCREAMING_SNAKE_CASE       ,   in_mid=SCREAMING_SNAKE_CASE						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,   )

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Dict        = num_up_blocks - 1 - i

  if any('upsample' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Tuple        = 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[int]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Tuple        = {
       layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

   if num_blocks > 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_resnet_paths(blocks[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[replace_indices]						)

  if any('attn' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  str        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Optional[Any]        = {
       layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

   if num_attn > 0:
    for j in range(config['layers_per_block'] + 1						):
     __lowerCAmelCase:  str        = {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
     __lowerCAmelCase:  Dict        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[replace_indices]						)

 __lowerCAmelCase:  Union[str, Any]        = {k.replace('mid_new_2'       ,   'mid_block'						): v for k, v in new_checkpoint.items()}
 return new_checkpoint







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  int        = {}

 __lowerCAmelCase:  List[Any]        = checkpoint['encoder.norm_out.weight']
 __lowerCAmelCase:  str        = checkpoint['encoder.norm_out.bias']

 __lowerCAmelCase:  Any        = checkpoint['encoder.conv_in.weight']
 __lowerCAmelCase:  str        = checkpoint['encoder.conv_in.bias']
 __lowerCAmelCase:  str        = checkpoint['encoder.conv_out.weight']
 __lowerCAmelCase:  List[str]        = checkpoint['encoder.conv_out.bias']

 __lowerCAmelCase:  Tuple        = checkpoint['decoder.norm_out.weight']
 __lowerCAmelCase:  int        = checkpoint['decoder.norm_out.bias']

 __lowerCAmelCase:  Optional[int]        = checkpoint['decoder.conv_in.weight']
 __lowerCAmelCase:  List[Any]        = checkpoint['decoder.conv_in.bias']
 __lowerCAmelCase:  int        = checkpoint['decoder.conv_out.weight']
 __lowerCAmelCase:  Tuple        = checkpoint['decoder.conv_out.bias']

 __lowerCAmelCase:  Optional[int]        = 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(SCREAMING_SNAKE_CASE						)
 }

 __lowerCAmelCase:  str        = len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'up' in layer}						)
 __lowerCAmelCase:  Optional[int]        = {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)}

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  List[str]        = (i - 1) // (config['layers_per_block'] + 1)

  if any('downsample' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Tuple        = checkpoint[
       f'''encoder.down.{i}.downsample.conv.weight'''
   ]
   __lowerCAmelCase:  Any        = checkpoint[
       f'''encoder.down.{i}.downsample.conv.bias'''
   ]

  if any('block' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Tuple        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  List[str]        = {
       layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

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

  if any('attn' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  List[str]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Optional[int]        = {
       layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

   if num_attn > 0:
    for j in range(config['layers_per_block']						):
     __lowerCAmelCase:  int        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE						)

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

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

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

 __lowerCAmelCase:  Any        = renew_attention_paths(SCREAMING_SNAKE_CASE       ,   in_mid=SCREAMING_SNAKE_CASE						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,   )

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Dict        = num_up_blocks - 1 - i

  if any('upsample' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  List[Any]        = checkpoint[
       f'''decoder.up.{i}.upsample.conv.weight'''
   ]
   __lowerCAmelCase:  Dict        = checkpoint[
       f'''decoder.up.{i}.upsample.conv.bias'''
   ]

  if any('block' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Tuple        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Optional[int]        = {
       layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

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

  if any('attn' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Any        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Dict        = {
       layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

   if num_attn > 0:
    for j in range(config['layers_per_block'] + 1						):
     __lowerCAmelCase:  int        = {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
     __lowerCAmelCase:  Dict        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[replace_indices]						)

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

 return new_checkpoint


if __name__ == "__main__":
  _a									=  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									=  parser.parse_args()
  _a									=  torch.load(args.checkpoint_path)

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

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

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

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

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

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





