
"""simple docstring"""




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			:       Union[str, 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			:       Optional[int]   		=       F"down_blocks.{i}.resnets.{j}."
      _A			:       Optional[Any]   		=       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			:       int   		=       F"down_blocks.{i}.attentions.{j}."
         _A			:       Optional[Any]   		=       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			:       int   		=       F"up_blocks.{i}.resnets.{j}."
      _A			:       Optional[Any]   		=       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			:       Dict   		=       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			:       Dict   		=       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			:       Tuple   		=       F"up_blocks.{i}.upsamplers.0."
      _A			:       Dict   		=       F"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
      unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

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

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


def   __magic_name__	(      __snake_case				:       List[str]  ) ->				Union[str, Any]:
       lowercase    :				List[Any]       		=							{k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              lowercase    :				str       		=							sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            lowercase    :				Union[str, Any]       		=							v.replace(__A     , __A  )
                     lowercase    :				Any       		=							v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     lowercase    :				Union[str, Any]       		=							v.replace(__A     , __A  )
              lowercase    :				Any       		=							v
       lowercase    :				str       		=							{v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


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

_A			:       Union[str, 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			:       Union[str, Any]   		=       F"encoder.down_blocks.{i}.resnets.{j}."
      _A			:       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			:       Dict   		=       F"down.{i}.downsample."
      vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

      _A			:       Union[str, Any]   		=       F"up_blocks.{i}.upsamplers.0."
      _A			:       Union[str, 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			:       Tuple   		=       F"decoder.up_blocks.{i}.resnets.{j}."
      _A			:       int   		=       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			:       Any   		=       F"mid_block.resnets.{i}."
   _A			:       Optional[Any]   		=       F"mid.block_{i+1}."
   vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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


def   __magic_name__	(      __snake_case				:       List[Any]  ) ->				Dict:
       return w.reshape(*w.shape     , 1     , 1  )


def   __magic_name__	(      __snake_case				:       Dict  ) ->				str:
       lowercase    :				Any       		=							{k: k for k in vae_state_dict.keys()}
       for k, v in mapping.items():
              for sd_part, hf_part in vae_conversion_map:
                     lowercase    :				Dict       		=							v.replace(__A     , __A  )
              lowercase    :				Any       		=							v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            lowercase    :				int       		=							v.replace(__A     , __A  )
                     lowercase    :				Optional[int]       		=							v
       lowercase    :				List[str]       		=							{v: vae_state_dict[k] for k, v in mapping.items()}
       lowercase    :				int       		=							["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"""  )
                            lowercase    :				List[Any]       		=							reshape_weight_for_sd(__A  )
       return new_state_dict


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


_A			:       List[Any]   		=       [
    # (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			:       Optional[int]   		=       {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_A			:       List[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   __magic_name__	(      __snake_case				:       int  ) ->				Union[str, Any]:
       lowercase    :				List[Any]       		=							{}
       lowercase    :				str       		=							{}
       lowercase    :				Optional[int]       		=							{}
       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"  )
              ):
                     lowercase    :				int       		=							k[: -len(".q_proj.weight"  )]
                     lowercase    :				Any       		=							k[-len("q_proj.weight"  )]
                     if k_pre not in capture_qkv_weight:
                            lowercase    :				int       		=							[None, None, None]
                     lowercase    :				str       		=							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"  )
              ):
                     lowercase    :				int       		=							k[: -len(".q_proj.bias"  )]
                     lowercase    :				Union[str, Any]       		=							k[-len("q_proj.bias"  )]
                     if k_pre not in capture_qkv_bias:
                            lowercase    :				str       		=							[None, None, None]
                     lowercase    :				Optional[int]       		=							v
                     continue

              lowercase    :				Dict       		=							textenc_pattern.sub(lambda __snake_case  : protected[re.escape(m.group(0  )  )]     , __A  )
              lowercase    :				List[str]       		=							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"  )
              lowercase    :				Optional[Any]       		=							textenc_pattern.sub(lambda __snake_case  : protected[re.escape(m.group(0  )  )]     , __A  )
              lowercase    :				List[str]       		=							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"  )
              lowercase    :				List[str]       		=							textenc_pattern.sub(lambda __snake_case  : protected[re.escape(m.group(0  )  )]     , __A  )
              lowercase    :				Tuple       		=							torch.cat(__A  )

       return new_state_dict


def   __magic_name__	(      __snake_case				:       Tuple  ) ->				Tuple:
       return text_enc_dict


if __name__ == "__main__":
   _A			:       Tuple   		=       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			:       Tuple   		=       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			:       str   		=       osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
   _A			:       Tuple   		=       osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
   _A			:       int   		=       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			:       Any   		=       load_file(unet_path, device="""cpu""")
   else:
      _A			:       int   		=       osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
      _A			:       str   		=       torch.load(unet_path, map_location="""cpu""")

   if osp.exists(vae_path):
      _A			:       Dict   		=       load_file(vae_path, device="""cpu""")
   else:
      _A			:       Any   		=       osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
      _A			:       Optional[Any]   		=       torch.load(vae_path, map_location="""cpu""")

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

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

   # Convert the VAE model
   _A			:       Optional[Any]   		=       convert_vae_state_dict(vae_state_dict)
   _A			:       Union[str, Any]   		=       {"""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			:       Any   		=       {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
      _A			:       List[str]   		=       convert_text_enc_state_dict_vaa(text_enc_dict)
      _A			:       str   		=       {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
   else:
      _A			:       List[Any]   		=       convert_text_enc_state_dict(text_enc_dict)
      _A			:       Tuple   		=       {"""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			:       Any   		=       {"""state_dict""": state_dict}
      torch.save(state_dict, args.checkpoint_path)



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None
   __UpperCAmelCase : torch.FloatTensor      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=0       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[str]="cls"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  project_dim
         UpperCAmelCase__								=  pooler_fn
         UpperCAmelCase__								=  learn_encoder
         UpperCAmelCase__								=  use_attention_mask





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			[r'pooler', r'logit_scale']
   __UpperCAmelCase : int      =			[r'position_ids', r'predictions.decoder.bias']
   __UpperCAmelCase : Any      =			'roberta'
   __UpperCAmelCase : List[str]      =			RobertaSeriesConfig


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  XLMRobertaModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
         UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  "has_pre_transformation"       ,  __UpperCAmelCase		)
         if self.has_pre_transformation:
               UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
               UpperCAmelCase__								=  nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps		)
         self.post_init()





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  self.base_model(
             input_ids=__UpperCAmelCase       ,  attention_mask=__UpperCAmelCase       ,  token_type_ids=__UpperCAmelCase       ,  position_ids=__UpperCAmelCase       ,  head_mask=__UpperCAmelCase       ,  inputs_embeds=__UpperCAmelCase       ,  encoder_hidden_states=__UpperCAmelCase       ,  encoder_attention_mask=__UpperCAmelCase       ,  output_attentions=__UpperCAmelCase       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=__UpperCAmelCase       ,  )

         if self.has_pre_transformation:
               UpperCAmelCase__								=  outputs["hidden_states"][-2]
               UpperCAmelCase__								=  self.pre_LN(__UpperCAmelCase		)
               UpperCAmelCase__								=  self.transformation_pre(__UpperCAmelCase		)

               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
         else:
               UpperCAmelCase__								=  self.transformation(outputs.last_hidden_state		)
               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )



