



'''simple docstring'''


import argparse
import json

import torch

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






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__=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_							(		lowercase__							,						lowercase__=0							):


     '''simple docstring'''







     a_									=[]
     for old_item in old_list:
          a_									=old_item
          a_									=new_item.replace("block."							,						"resnets."							)
          a_									=new_item.replace("conv_shorcut"							,						"conv1"							)
          a_									=new_item.replace("in_shortcut"							,						"conv_shortcut"							)
          a_									=new_item.replace("temb_proj"							,						"time_emb_proj"							)

          a_									=shave_segments(lowercase__							,						n_shave_prefix_segments=lowercase__							)

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

     return mapping






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__=0							,						lowercase__=False							):


     '''simple docstring'''







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

          # In `model.mid`, the layer is called `attn`.
          if not in_mid:
               a_									=new_item.replace("attn"							,						"attentions"							)
          a_									=new_item.replace(".k."							,						".key."							)
          a_									=new_item.replace(".v."							,						".value."							)
          a_									=new_item.replace(".q."							,						".query."							)

          a_									=new_item.replace("proj_out"							,						"proj_attn"							)
          a_									=new_item.replace("norm"							,						"group_norm"							)

          a_									=shave_segments(lowercase__							,						n_shave_prefix_segments=lowercase__							)
          mapping.append({"old": old_item, "new": new_item}							)

     return mapping






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							,						lowercase__=None							,						lowercase__=None							,						lowercase__=None							):


     '''simple docstring'''







     assert isinstance(lowercase__							,						lowercase__							), "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():
               a_									=old_checkpoint[path]
               a_									=old_tensor.shape[0] // 3

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

               a_									=old_tensor.shape[0] // config.get("num_head_channels"							,						1							) // 3

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

               a_									=query.reshape(lowercase__							).squeeze()
               a_									=key.reshape(lowercase__							).squeeze()
               a_									=value.reshape(lowercase__							).squeeze()

     for path in paths:
          a_									=path["new"]

          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          a_									=new_path.replace("down."							,						"down_blocks."							)
          a_									=new_path.replace("up."							,						"up_blocks."							)

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

          if "attentions" in new_path:
               a_									=old_checkpoint[path["old"]].squeeze()
          else:
               a_									=old_checkpoint[path["old"]]






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									={}

     a_									=checkpoint["temb.dense.0.weight"]
     a_									=checkpoint["temb.dense.0.bias"]
     a_									=checkpoint["temb.dense.1.weight"]
     a_									=checkpoint["temb.dense.1.bias"]

     a_									=checkpoint["norm_out.weight"]
     a_									=checkpoint["norm_out.bias"]

     a_									=checkpoint["conv_in.weight"]
     a_									=checkpoint["conv_in.bias"]
     a_									=checkpoint["conv_out.weight"]
     a_									=checkpoint["conv_out.bias"]

     a_									=len({".".join(layer.split("."							)[:2]							) for layer in checkpoint if "down" in layer}							)
     a_									={
         layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(lowercase__							)
     }

     a_									=len({".".join(layer.split("."							)[:2]							) for layer in checkpoint if "up" in layer}							)
     a_									={layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(lowercase__							)}

     for i in range(lowercase__							):
          a_									=(i - 1) // (config["layers_per_block"] + 1)

          if any("downsample" in layer for layer in down_blocks[i]							):
               a_									=checkpoint[
                   F"""down.{i}.downsample.op.weight"""
               ]
               a_									=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]							):
               a_									=len(
                   {".".join(shave_segments(lowercase__							,						2							).split("."							)[:2]							) for layer in down_blocks[i] if "block" in layer}							)
               a_									={
                   layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
                   for layer_id in range(lowercase__							)
               }

               if num_blocks > 0:
                    for j in range(config["layers_per_block"]							):
                         a_									=renew_resnet_paths(blocks[j]							)
                         assign_to_checkpoint(lowercase__							,						lowercase__							,						lowercase__							)

          if any("attn" in layer for layer in down_blocks[i]							):
               a_									=len(
                   {".".join(shave_segments(lowercase__							,						2							).split("."							)[:2]							) for layer in down_blocks[i] if "attn" in layer}							)
               a_									={
                   layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
                   for layer_id in range(lowercase__							)
               }

               if num_attn > 0:
                    for j in range(config["layers_per_block"]							):
                         a_									=renew_attention_paths(attns[j]							)
                         assign_to_checkpoint(lowercase__							,						lowercase__							,						lowercase__							,						config=lowercase__							)

     a_									=[key for key in checkpoint if "mid.block_1" in key]
     a_									=[key for key in checkpoint if "mid.block_2" in key]
     a_									=[key for key in checkpoint if "mid.attn_1" in key]

     # Mid new 2
     a_									=renew_resnet_paths(lowercase__							)
     assign_to_checkpoint(
         lowercase__							,						lowercase__							,						lowercase__							,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]							,						)

     a_									=renew_resnet_paths(lowercase__							)
     assign_to_checkpoint(
         lowercase__							,						lowercase__							,						lowercase__							,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]							,						)

     a_									=renew_attention_paths(lowercase__							,						in_mid=lowercase__							)
     assign_to_checkpoint(
         lowercase__							,						lowercase__							,						lowercase__							,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]							,						)

     for i in range(lowercase__							):
          a_									=num_up_blocks - 1 - i

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

          if any("block" in layer for layer in up_blocks[i]							):
               a_									=len(
                   {".".join(shave_segments(lowercase__							,						2							).split("."							)[:2]							) for layer in up_blocks[i] if "block" in layer}							)
               a_									={
                   layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(lowercase__							)
               }

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

          if any("attn" in layer for layer in up_blocks[i]							):
               a_									=len(
                   {".".join(shave_segments(lowercase__							,						2							).split("."							)[:2]							) for layer in up_blocks[i] if "attn" in layer}							)
               a_									={
                   layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(lowercase__							)
               }

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

     a_									={k.replace("mid_new_2"							,						"mid_block"							): v for k, v in new_checkpoint.items()}
     return new_checkpoint






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									={}

     a_									=checkpoint["encoder.norm_out.weight"]
     a_									=checkpoint["encoder.norm_out.bias"]

     a_									=checkpoint["encoder.conv_in.weight"]
     a_									=checkpoint["encoder.conv_in.bias"]
     a_									=checkpoint["encoder.conv_out.weight"]
     a_									=checkpoint["encoder.conv_out.bias"]

     a_									=checkpoint["decoder.norm_out.weight"]
     a_									=checkpoint["decoder.norm_out.bias"]

     a_									=checkpoint["decoder.conv_in.weight"]
     a_									=checkpoint["decoder.conv_in.bias"]
     a_									=checkpoint["decoder.conv_out.weight"]
     a_									=checkpoint["decoder.conv_out.bias"]

     a_									=len({".".join(layer.split("."							)[:3]							) for layer in checkpoint if "down" in layer}							)
     a_									={
         layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(lowercase__							)
     }

     a_									=len({".".join(layer.split("."							)[:3]							) for layer in checkpoint if "up" in layer}							)
     a_									={layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(lowercase__							)}

     for i in range(lowercase__							):
          a_									=(i - 1) // (config["layers_per_block"] + 1)

          if any("downsample" in layer for layer in down_blocks[i]							):
               a_									=checkpoint[
                   F"""encoder.down.{i}.downsample.conv.weight"""
               ]
               a_									=checkpoint[
                   F"""encoder.down.{i}.downsample.conv.bias"""
               ]

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

               if num_blocks > 0:
                    for j in range(config["layers_per_block"]							):
                         a_									=renew_resnet_paths(blocks[j]							)
                         assign_to_checkpoint(lowercase__							,						lowercase__							,						lowercase__							)

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

               if num_attn > 0:
                    for j in range(config["layers_per_block"]							):
                         a_									=renew_attention_paths(attns[j]							)
                         assign_to_checkpoint(lowercase__							,						lowercase__							,						lowercase__							,						config=lowercase__							)

     a_									=[key for key in checkpoint if "mid.block_1" in key]
     a_									=[key for key in checkpoint if "mid.block_2" in key]
     a_									=[key for key in checkpoint if "mid.attn_1" in key]

     # Mid new 2
     a_									=renew_resnet_paths(lowercase__							)
     assign_to_checkpoint(
         lowercase__							,						lowercase__							,						lowercase__							,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]							,						)

     a_									=renew_resnet_paths(lowercase__							)
     assign_to_checkpoint(
         lowercase__							,						lowercase__							,						lowercase__							,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]							,						)

     a_									=renew_attention_paths(lowercase__							,						in_mid=lowercase__							)
     assign_to_checkpoint(
         lowercase__							,						lowercase__							,						lowercase__							,						additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]							,						)

     for i in range(lowercase__							):
          a_									=num_up_blocks - 1 - i

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

          if any("block" in layer for layer in up_blocks[i]							):
               a_									=len(
                   {".".join(shave_segments(lowercase__							,						3							).split("."							)[:3]							) for layer in up_blocks[i] if "block" in layer}							)
               a_									={
                   layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(lowercase__							)
               }

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

          if any("attn" in layer for layer in up_blocks[i]							):
               a_									=len(
                   {".".join(shave_segments(lowercase__							,						3							).split("."							)[:3]							) for layer in up_blocks[i] if "attn" in layer}							)
               a_									={
                   layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(lowercase__							)
               }

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

     a_									={k.replace("mid_new_2"							,						"mid_block"							): v for k, v in new_checkpoint.items()}
     a_									=checkpoint["quant_conv.weight"]
     a_									=checkpoint["quant_conv.bias"]
     if "quantize.embedding.weight" in checkpoint:
          a_									=checkpoint["quantize.embedding.weight"]
     a_									=checkpoint["post_quant_conv.weight"]
     a_									=checkpoint["post_quant_conv.bias"]

     return new_checkpoint


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

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

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

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

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

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

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

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







'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=0  # Number of params in the encoder

     print("name".ljust(6_0							,						" "							)							,						"Remaining Weights %"							,						"Remaining Weight"							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


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

  parser.add_argument(
      '''--pruning_method''',
      choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
      type=str,
      required=True,
      help=(
          '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
          ''' pruning)'''
      ),
  )
  parser.add_argument(
      '''--threshold''',
      type=float,
      required=False,
      help=(
          '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
          '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
          '''Not needed for `l0`'''
      ),
  )
  parser.add_argument(
      '''--serialization_dir''',
      type=str,
      required=True,
      help='''Folder containing the model that was previously fine-pruned''',
  )

  lowercase											=      parser.parse_args()

  main(args)


