

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

a_    :						List[Any]    		=      [
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

a_    :						Optional[Any]    		=      [
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

a_    :						List[Any]    		=      []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
      # loop over downblocks/upblocks

      for j in range(2):
            # loop over resnets/attentions for downblocks
            a_    :						Tuple    		=      F"""down_blocks.{i}.resnets.{j}."""
            a_    :						Dict    		=      F"""input_blocks.{3*i + j + 1}.0."""
            unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

            if i < 3:
                  # no attention layers in down_blocks.3
                  a_    :						Dict    		=      F"""down_blocks.{i}.attentions.{j}."""
                  a_    :						Optional[int]    		=      F"""input_blocks.{3*i + j + 1}.1."""
                  unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

      for j in range(3):
            # loop over resnets/attentions for upblocks
            a_    :						Any    		=      F"""up_blocks.{i}.resnets.{j}."""
            a_    :						List[str]    		=      F"""output_blocks.{3*i + j}.0."""
            unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

            if i > 0:
                  # no attention layers in up_blocks.0
                  a_    :						List[Any]    		=      F"""up_blocks.{i}.attentions.{j}."""
                  a_    :						Union[str, Any]    		=      F"""output_blocks.{3*i + j}.1."""
                  unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

      if i < 3:
            # no downsample in down_blocks.3
            a_    :						int    		=      F"""down_blocks.{i}.downsamplers.0.conv."""
            a_    :						str    		=      F"""input_blocks.{3*(i+1)}.0.op."""
            unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

            # no upsample in up_blocks.3
            a_    :						Dict    		=      F"""up_blocks.{i}.upsamplers.0."""
            a_    :						Optional[Any]    		=      F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
            unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

a_    :						Dict    		=      "mid_block.attentions.0."
a_    :						Any    		=      "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
      a_    :						Union[str, Any]    		=      F"""mid_block.resnets.{j}."""
      a_    :						List[str]    		=      F"""middle_block.{2*j}."""
      unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]	)		->							str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{k: k for k in unet_state_dict.keys()}
 for sd_name, hf_name in unet_conversion_map:
  SCREAMING_SNAKE_CASE								=			sd_name
 for k, v in mapping.items():
  if "resnets" in k:
   for sd_part, hf_part in unet_conversion_map_resnet:
    SCREAMING_SNAKE_CASE								=			v.replace(_UpperCamelCase		,							_UpperCamelCase	)
   SCREAMING_SNAKE_CASE								=			v
 for k, v in mapping.items():
  for sd_part, hf_part in unet_conversion_map_layer:
   SCREAMING_SNAKE_CASE								=			v.replace(_UpperCamelCase		,							_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			v
 SCREAMING_SNAKE_CASE								=			{v: unet_state_dict[k] for k, v in mapping.items()}
 return new_state_dict


# ================#
# VAE Conversion #
# ================#

a_    :						Any    		=      [
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
      # down_blocks have two resnets
      for j in range(2):
            a_    :						str    		=      F"""encoder.down_blocks.{i}.resnets.{j}."""
            a_    :						Optional[Any]    		=      F"""encoder.down.{i}.block.{j}."""
            vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

      if i < 3:
            a_    :						int    		=      F"""down_blocks.{i}.downsamplers.0."""
            a_    :						Tuple    		=      F"""down.{i}.downsample."""
            vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

            a_    :						Optional[int]    		=      F"""up_blocks.{i}.upsamplers.0."""
            a_    :						Optional[Any]    		=      F"""up.{3-i}.upsample."""
            vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

      # up_blocks have three resnets
      # also, up blocks in hf are numbered in reverse from sd
      for j in range(3):
            a_    :						List[Any]    		=      F"""decoder.up_blocks.{i}.resnets.{j}."""
            a_    :						Any    		=      F"""decoder.up.{3-i}.block.{j}."""
            vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
      a_    :						Union[str, Any]    		=      F"""mid_block.resnets.{i}."""
      a_    :						List[Any]    		=      F"""mid.block_{i+1}."""
      vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


a_    :						int    		=      [
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple	)		->							List[Any]:
 '''simple docstring'''


 return w.reshape(*w.shape		,							1		,							1	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{k: k for k in vae_state_dict.keys()}
 for k, v in mapping.items():
  for sd_part, hf_part in vae_conversion_map:
   SCREAMING_SNAKE_CASE								=			v.replace(_UpperCamelCase		,							_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			v
 for k, v in mapping.items():
  if "attentions" in k:
   for sd_part, hf_part in vae_conversion_map_attn:
    SCREAMING_SNAKE_CASE								=			v.replace(_UpperCamelCase		,							_UpperCamelCase	)
   SCREAMING_SNAKE_CASE								=			v
 SCREAMING_SNAKE_CASE								=			{v: vae_state_dict[k] for k, v in mapping.items()}
 SCREAMING_SNAKE_CASE								=			['q', 'k', 'v', 'proj_out']
 for k, v in new_state_dict.items():
  for weight_name in weights_to_convert:
   if f"""mid.attn_1.{weight_name}.weight""" in k:
    print(f"""Reshaping {k} for SD format"""	)
    SCREAMING_SNAKE_CASE								=			reshape_weight_for_sd(_UpperCamelCase	)
 return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


a_    :						Optional[int]    		=      [
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
a_    :						List[Any]    		=      {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a_    :						Any    		=      re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
a_    :						int    		=      {"q": 0, "k": 1, "v": 2}


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple	)		->							Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{}
 SCREAMING_SNAKE_CASE								=			{}
 SCREAMING_SNAKE_CASE								=			{}
 for k, v in text_enc_dict.items():
  if (
      k.endswith('.self_attn.q_proj.weight'	)
      or k.endswith('.self_attn.k_proj.weight'	)
      or k.endswith('.self_attn.v_proj.weight'	)
  ):
   SCREAMING_SNAKE_CASE								=			k[: -len('.q_proj.weight'	)]
   SCREAMING_SNAKE_CASE								=			k[-len('q_proj.weight'	)]
   if k_pre not in capture_qkv_weight:
    SCREAMING_SNAKE_CASE								=			[None, None, None]
   SCREAMING_SNAKE_CASE								=			v
   continue

  if (
      k.endswith('.self_attn.q_proj.bias'	)
      or k.endswith('.self_attn.k_proj.bias'	)
      or k.endswith('.self_attn.v_proj.bias'	)
  ):
   SCREAMING_SNAKE_CASE								=			k[: -len('.q_proj.bias'	)]
   SCREAMING_SNAKE_CASE								=			k[-len('q_proj.bias'	)]
   if k_pre not in capture_qkv_bias:
    SCREAMING_SNAKE_CASE								=			[None, None, None]
   SCREAMING_SNAKE_CASE								=			v
   continue

  SCREAMING_SNAKE_CASE								=			textenc_pattern.sub(lambda _UpperCamelCase	: protected[re.escape(m.group(0	)	)]		,							_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			v

 for k_pre, tensors in capture_qkv_weight.items():
  if None in tensors:
   raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'	)
  SCREAMING_SNAKE_CASE								=			textenc_pattern.sub(lambda _UpperCamelCase	: protected[re.escape(m.group(0	)	)]		,							_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			torch.cat(_UpperCamelCase	)

 for k_pre, tensors in capture_qkv_bias.items():
  if None in tensors:
   raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'	)
  SCREAMING_SNAKE_CASE								=			textenc_pattern.sub(lambda _UpperCamelCase	: protected[re.escape(m.group(0	)	)]		,							_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			torch.cat(_UpperCamelCase	)

 return new_state_dict


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]	)		->							List[Any]:
 '''simple docstring'''


 return text_enc_dict


if __name__ == "__main__":
      a_    :						Any    		=      argparse.ArgumentParser()

      parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
      parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
      parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
      parser.add_argument(
          "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
      )

      a_    :						Any    		=      parser.parse_args()

      assert args.model_path is not None, "Must provide a model path!"

      assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

      # Path for safetensors
      a_    :						List[str]    		=      osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
      a_    :						Dict    		=      osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
      a_    :						Tuple    		=      osp.join(args.model_path, "text_encoder", "model.safetensors")

      # Load models from safetensors if it exists, if it doesn't pytorch
      if osp.exists(unet_path):
            a_    :						Optional[Any]    		=      load_file(unet_path, device="cpu")
      else:
            a_    :						List[Any]    		=      osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
            a_    :						Dict    		=      torch.load(unet_path, map_location="cpu")

      if osp.exists(vae_path):
            a_    :						Tuple    		=      load_file(vae_path, device="cpu")
      else:
            a_    :						Tuple    		=      osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
            a_    :						int    		=      torch.load(vae_path, map_location="cpu")

      if osp.exists(text_enc_path):
            a_    :						Tuple    		=      load_file(text_enc_path, device="cpu")
      else:
            a_    :						str    		=      osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
            a_    :						List[str]    		=      torch.load(text_enc_path, map_location="cpu")

      # Convert the UNet model
      a_    :						str    		=      convert_unet_state_dict(unet_state_dict)
      a_    :						Any    		=      {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

      # Convert the VAE model
      a_    :						Dict    		=      convert_vae_state_dict(vae_state_dict)
      a_    :						str    		=      {"first_stage_model." + k: v for k, v in vae_state_dict.items()}

      # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
      a_    :						Union[str, Any]    		=      "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

      if is_vaa_model:
            # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
            a_    :						Union[str, Any]    		=      {"transformer." + k: v for k, v in text_enc_dict.items()}
            a_    :						Dict    		=      convert_text_enc_state_dict_vaa(text_enc_dict)
            a_    :						List[Any]    		=      {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
      else:
            a_    :						Optional[int]    		=      convert_text_enc_state_dict(text_enc_dict)
            a_    :						Optional[int]    		=      {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

      # Put together new checkpoint
      a_    :						List[str]    		=      {**unet_state_dict, **vae_state_dict, **text_enc_dict}
      if args.half:
            a_    :						Any    		=      {k: v.half() for k, v in state_dict.items()}

      if args.use_safetensors:
            save_file(state_dict, args.checkpoint_path)
      else:
            a_    :						Optional[int]    		=      {"state_dict": state_dict}
            torch.save(state_dict, args.checkpoint_path)




#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist


def        __lowerCAmelCase    (       *_UpperCamelCase		:  Optional[Any]	)		->							Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,							'r'	) as fh:
  fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_EX	)
  try:
   print(*_UpperCamelCase	)
  finally:
   fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_UN	)


a_    :						int    		=      int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
a_    :						str    		=      torch.device("cuda", local_rank)
a_    :						Optional[int]    		=      socket.gethostname()

a_    :						Union[str, Any]    		=      F"""[{hostname}-{local_rank}]"""

try:
      # test distributed
      dist.init_process_group("nccl")
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      a_    :						Dict    		=      dist.get_rank()
      a_    :						Any    		=      dist.get_world_size()

      printflock(F"""{gpu} is OK (global rank: {rank}/{world_size})""")

      dist.barrier()
      if rank == 0:
            printflock(F"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
      printflock(F"""{gpu} is broken""")
      raise

