






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

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

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


def        _a  (     UpperCamelCase_					:				List[Any]   ) ->   Any:
      """simple docstring"""







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


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

a_							=    [
    # (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_							=    F"encoder.down_blocks.{i}.resnets.{j}."
  a_							=    F"encoder.down.{i}.block.{j}."
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

  a_							=    F"up_blocks.{i}.upsamplers.0."
  a_							=    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_							=    F"decoder.up_blocks.{i}.resnets.{j}."
  a_							=    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_							=    F"mid_block.resnets.{i}."
 a_							=    F"mid.block_{i+1}."
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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


def        _a  (     UpperCamelCase_					:				Tuple   ) ->   int:
      """simple docstring"""







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


def        _a  (     UpperCamelCase_					:				Union[str, Any]   ) ->   Optional[int]:
      """simple docstring"""







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


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


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

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


def        _a  (     UpperCamelCase_					:				int   ) ->   Union[str, Any]:
      """simple docstring"""







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

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

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

      return new_state_dict


def        _a  (     UpperCamelCase_					:				Optional[int]   ) ->   Optional[int]:
      """simple docstring"""







      return text_enc_dict


if __name__ == "__main__":
 a_							=    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_							=    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_							=    osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 a_							=    osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 a_							=    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_							=    load_file(unet_path, device='''cpu''')
 else:
  a_							=    osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  a_							=    torch.load(unet_path, map_location='''cpu''')

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

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

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

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

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

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














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ['''PLBartTokenizer''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PLBartForCausalLM''',
	    '''PLBartForConditionalGeneration''',
	    '''PLBartForSequenceClassification''',
	    '''PLBartModel''',
	    '''PLBartPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)






