import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

UpperCamelCase__											=					[
    # (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'),
]

UpperCamelCase__											=					[
    # (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'),
]

UpperCamelCase__											=					[]
# 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
            UpperCamelCase__											=					f'''down_blocks.{i}.resnets.{j}.'''
            UpperCamelCase__											=					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
                  UpperCamelCase__											=					f'''down_blocks.{i}.attentions.{j}.'''
                  UpperCamelCase__											=					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
            UpperCamelCase__											=					f'''up_blocks.{i}.resnets.{j}.'''
            UpperCamelCase__											=					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
                  UpperCamelCase__											=					f'''up_blocks.{i}.attentions.{j}.'''
                  UpperCamelCase__											=					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
            UpperCamelCase__											=					f'''down_blocks.{i}.downsamplers.0.conv.'''
            UpperCamelCase__											=					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
            UpperCamelCase__											=					f'''up_blocks.{i}.upsamplers.0.'''
            UpperCamelCase__											=					f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
            unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCamelCase__											=					'mid_block.attentions.0.'
UpperCamelCase__											=					'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
      UpperCamelCase__											=					f'''mid_block.resnets.{j}.'''
      UpperCamelCase__											=					f'''middle_block.{2*j}.'''
      unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))




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







      '''simple docstring'''


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


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

UpperCamelCase__											=					[
    # (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):
            UpperCamelCase__											=					f'''encoder.down_blocks.{i}.resnets.{j}.'''
            UpperCamelCase__											=					f'''encoder.down.{i}.block.{j}.'''
            vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

      if i < 3:
            UpperCamelCase__											=					f'''down_blocks.{i}.downsamplers.0.'''
            UpperCamelCase__											=					f'''down.{i}.downsample.'''
            vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

            UpperCamelCase__											=					f'''up_blocks.{i}.upsamplers.0.'''
            UpperCamelCase__											=					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):
            UpperCamelCase__											=					f'''decoder.up_blocks.{i}.resnets.{j}.'''
            UpperCamelCase__											=					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):
      UpperCamelCase__											=					f'''mid_block.resnets.{i}.'''
      UpperCamelCase__											=					f'''mid.block_{i+1}.'''
      vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCamelCase__											=					[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


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




def    lowerCAmelCase_ (     __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  {k: k for k in vae_state_dict.keys()}
      for k, v in mapping.items():
            for sd_part, hf_part in vae_conversion_map:
                  UpperCAmelCase__								=  v.replace(__A,  __A							)
            UpperCAmelCase__								=  v
      for k, v in mapping.items():
            if "attentions" in k:
                  for sd_part, hf_part in vae_conversion_map_attn:
                        UpperCAmelCase__								=  v.replace(__A,  __A							)
                  UpperCAmelCase__								=  v
      UpperCAmelCase__								=  {v: vae_state_dict[k] for k, v in mapping.items()}
      UpperCAmelCase__								=  ["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"""							)
                        UpperCAmelCase__								=  reshape_weight_for_sd(__A							)
      return new_state_dict


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


UpperCamelCase__											=					[
    # (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'),
]
UpperCamelCase__											=					{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCamelCase__											=					re.compile('|'.join(protected.keys()))

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




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







      '''simple docstring'''


      UpperCAmelCase__								=  {}
      UpperCAmelCase__								=  {}
      UpperCAmelCase__								=  {}
      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"							)
            ):
                  UpperCAmelCase__								=  k[: -len(".q_proj.weight"							)]
                  UpperCAmelCase__								=  k[-len("q_proj.weight"							)]
                  if k_pre not in capture_qkv_weight:
                        UpperCAmelCase__								=  [None, None, None]
                  UpperCAmelCase__								=  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"							)
            ):
                  UpperCAmelCase__								=  k[: -len(".q_proj.bias"							)]
                  UpperCAmelCase__								=  k[-len("q_proj.bias"							)]
                  if k_pre not in capture_qkv_bias:
                        UpperCAmelCase__								=  [None, None, None]
                  UpperCAmelCase__								=  v
                  continue

            UpperCAmelCase__								=  textenc_pattern.sub(lambda __A							: protected[re.escape(m.group(0							)							)],  __A							)
            UpperCAmelCase__								=  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"							)
            UpperCAmelCase__								=  textenc_pattern.sub(lambda __A							: protected[re.escape(m.group(0							)							)],  __A							)
            UpperCAmelCase__								=  torch.cat(__A							)

      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"							)
            UpperCAmelCase__								=  textenc_pattern.sub(lambda __A							: protected[re.escape(m.group(0							)							)],  __A							)
            UpperCAmelCase__								=  torch.cat(__A							)

      return new_state_dict




def    lowerCAmelCase_ (     __A							)     ->				Tuple:







      '''simple docstring'''


      return text_enc_dict


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

      UpperCamelCase__											=					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
      UpperCamelCase__											=					osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
      UpperCamelCase__											=					osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
      UpperCamelCase__											=					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):
            UpperCamelCase__											=					load_file(unet_path, device='cpu')
      else:
            UpperCamelCase__											=					osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
            UpperCamelCase__											=					torch.load(unet_path, map_location='cpu')

      if osp.exists(vae_path):
            UpperCamelCase__											=					load_file(vae_path, device='cpu')
      else:
            UpperCamelCase__											=					osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
            UpperCamelCase__											=					torch.load(vae_path, map_location='cpu')

      if osp.exists(text_enc_path):
            UpperCamelCase__											=					load_file(text_enc_path, device='cpu')
      else:
            UpperCamelCase__											=					osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
            UpperCamelCase__											=					torch.load(text_enc_path, map_location='cpu')

      # Convert the UNet model
      UpperCamelCase__											=					convert_unet_state_dict(unet_state_dict)
      UpperCamelCase__											=					{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

      # Convert the VAE model
      UpperCamelCase__											=					convert_vae_state_dict(vae_state_dict)
      UpperCamelCase__											=					{'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
      UpperCamelCase__											=					'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
            UpperCamelCase__											=					{'transformer.' + k: v for k, v in text_enc_dict.items()}
            UpperCamelCase__											=					convert_text_enc_state_dict_vaa(text_enc_dict)
            UpperCamelCase__											=					{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
      else:
            UpperCamelCase__											=					convert_text_enc_state_dict(text_enc_dict)
            UpperCamelCase__											=					{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

      # Put together new checkpoint
      UpperCamelCase__											=					{**unet_state_dict, **vae_state_dict, **text_enc_dict}
      if args.half:
            UpperCamelCase__											=					{k: v.half() for k, v in state_dict.items()}

      if args.use_safetensors:
            save_file(state_dict, args.checkpoint_path)
      else:
            UpperCamelCase__											=					{'state_dict': state_dict}
            torch.save(state_dict, args.checkpoint_path)






import operator as op


a__				=		'''scaler.pt'''
a__				=		'''pytorch_model'''
a__				=		'''random_states'''
a__				=		'''optimizer'''
a__				=		'''scheduler'''
a__				=		'''pytorch_model.bin'''
a__				=		'''pytorch_model.bin.index.json'''
a__				=		'''model.safetensors'''
a__				=		'''model.safetensors.index.json'''
a__				=		'''1.10.2'''
a__				=		'''py38'''
a__				=		'''4.17.0'''
a__				=		['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
a__				=		['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
a__				=		['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
a__				=		['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
a__				=		['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
a__				=		'''2.0.1'''
a__				=		['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
a__				=		['''default''', '''reduce-overhead''', '''max-autotune''']

a__				=		{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
a__				=		[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

a__				=		['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
a__				=		['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']



