

"""simple docstring"""






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_lowerCAmelCase						= [
    # (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"""),
]

_lowerCAmelCase						= [
    # (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"""),
]

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

_lowerCAmelCase						= """mid_block.attentions.0."""
_lowerCAmelCase						= """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    {k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        _lowerCAmelCase       :   Any  =    sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                _lowerCAmelCase       :   List[str]  =    v.replace(_lowerCamelCase ,   _lowerCamelCase   )
            _lowerCAmelCase       :   Optional[Any]  =    v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            _lowerCAmelCase       :   Optional[int]  =    v.replace(_lowerCamelCase ,   _lowerCamelCase   )
        _lowerCAmelCase       :   List[Any]  =    v
    _lowerCAmelCase       :   int  =    {v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


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

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

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

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


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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    {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       :   List[str]  =    v.replace(_lowerCamelCase ,   _lowerCamelCase   )
        _lowerCAmelCase       :   Union[str, Any]  =    v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                _lowerCAmelCase       :   Optional[int]  =    v.replace(_lowerCamelCase ,   _lowerCamelCase   )
            _lowerCAmelCase       :   Dict  =    v
    _lowerCAmelCase       :   Dict  =    {v: vae_state_dict[k] for k, v in mapping.items()}
    _lowerCAmelCase       :   Union[str, Any]  =    ['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       :   Union[str, Any]  =    reshape_weight_for_sd(_lowerCamelCase   )
    return new_state_dict


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


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

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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    {}
    _lowerCAmelCase       :   Optional[int]  =    {}
    _lowerCAmelCase       :   Dict  =    {}
    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       :   str  =    k[: -len('.q_proj.weight'   )]
            _lowerCAmelCase       :   Dict  =    k[-len('q_proj.weight'   )]
            if k_pre not in capture_qkv_weight:
                _lowerCAmelCase       :   Dict  =    [None, None, None]
            _lowerCAmelCase       :   List[Any]  =    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       :   Tuple  =    k[: -len('.q_proj.bias'   )]
            _lowerCAmelCase       :   List[Any]  =    k[-len('q_proj.bias'   )]
            if k_pre not in capture_qkv_bias:
                _lowerCAmelCase       :   Union[str, Any]  =    [None, None, None]
            _lowerCAmelCase       :   int  =    v
            continue

        _lowerCAmelCase       :   Union[str, Any]  =    textenc_pattern.sub(lambda _lowerCamelCase   : protected[re.escape(m.group(0   )   )] ,   _lowerCamelCase   )
        _lowerCAmelCase       :   Union[str, Any]  =    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       :   List[Any]  =    textenc_pattern.sub(lambda _lowerCamelCase   : protected[re.escape(m.group(0   )   )] ,   _lowerCamelCase   )
        _lowerCAmelCase       :   Optional[Any]  =    torch.cat(_lowerCamelCase   )

    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       :   Tuple  =    textenc_pattern.sub(lambda _lowerCamelCase   : protected[re.escape(m.group(0   )   )] ,   _lowerCamelCase   )
        _lowerCAmelCase       :   Optional[Any]  =    torch.cat(_lowerCamelCase   )

    return new_state_dict

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return text_enc_dict


if __name__ == "__main__":
 _lowerCAmelCase						= 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."""
 )

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

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

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

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

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

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

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








"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if (
        (cp >= 0X4E00 and cp <= 0X9FFF)
        or (cp >= 0X3400 and cp <= 0X4DBF)  #
        or (cp >= 0X20000 and cp <= 0X2A6DF)  #
        or (cp >= 0X2A700 and cp <= 0X2B73F)  #
        or (cp >= 0X2B740 and cp <= 0X2B81F)  #
        or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
        or (cp >= 0XF900 and cp <= 0XFAFF)
        or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
    ):  #
        return True

    return False

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    for char in word:
        _lowerCAmelCase       :   Dict  =    ord(_lowerCamelCase   )
        if not _is_chinese_char(_lowerCamelCase   ):
            return 0
    return 1

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    set()

    for token in tokens:
        _lowerCAmelCase       :   Optional[int]  =    len(_lowerCamelCase   ) > 1 and is_chinese(_lowerCamelCase   )
        if chinese_word:
            word_set.add(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    list(_lowerCamelCase   )
    return word_list

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if not chinese_word_set:
        return bert_tokens
    _lowerCAmelCase       :   Optional[Any]  =    max([len(_lowerCamelCase   ) for w in chinese_word_set]   )

    _lowerCAmelCase       :   str  =    bert_tokens
    _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    0, len(_lowerCamelCase   )
    while start < end:
        _lowerCAmelCase       :   Dict  =    True
        if is_chinese(bert_word[start]   ):
            _lowerCAmelCase       :   str  =    min(end - start ,   _lowerCamelCase   )
            for i in range(_lowerCamelCase ,   1 ,   -1   ):
                _lowerCAmelCase       :   List[Any]  =    ''.join(bert_word[start : start + i]   )
                if whole_word in chinese_word_set:
                    for j in range(start + 1 ,   start + i   ):
                        _lowerCAmelCase       :   Tuple  =    '##' + bert_word[j]
                    _lowerCAmelCase       :   Optional[int]  =    start + i
                    _lowerCAmelCase       :   Any  =    False
                    break
        if single_word:
            start += 1
    return bert_word

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Tuple  =    ltp_tokenizer.seg(lines[i : i + 100]   )[0]
        _lowerCAmelCase       :   List[Any]  =    [get_chinese_word(_lowerCamelCase   ) for r in res]
        ltp_res.extend(_lowerCamelCase   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    _lowerCAmelCase       :   int  =    []
    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Dict  =    bert_tokenizer(lines[i : i + 100] ,   add_special_tokens=_lowerCamelCase ,   truncation=_lowerCamelCase ,   max_length=512   )
        bert_res.extend(res['input_ids']   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    _lowerCAmelCase       :   Union[str, Any]  =    []
    for input_ids, chinese_word in zip(_lowerCamelCase ,   _lowerCamelCase   ):
        _lowerCAmelCase       :   Optional[int]  =    []
        for id in input_ids:
            _lowerCAmelCase       :   List[Any]  =    bert_tokenizer._convert_id_to_token(_lowerCamelCase   )
            input_tokens.append(_lowerCamelCase   )
        _lowerCAmelCase       :   Any  =    add_sub_symbol(_lowerCamelCase ,   _lowerCamelCase   )
        _lowerCAmelCase       :   List[str]  =    []
        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
        for i, token in enumerate(_lowerCamelCase   ):
            if token[:2] == "##":
                _lowerCAmelCase       :   List[Any]  =    token[2:]
                # save chinese tokens' pos
                if len(_lowerCamelCase   ) == 1 and _is_chinese_char(ord(_lowerCamelCase   )   ):
                    ref_id.append(_lowerCamelCase   )
        ref_ids.append(_lowerCamelCase   )

    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    return ref_ids

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    with open(args.file_name ,   'r' ,   encoding='utf-8'   ) as f:
        _lowerCAmelCase       :   int  =    f.readlines()
    _lowerCAmelCase       :   int  =    [line.strip() for line in data if len(_lowerCamelCase   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    _lowerCAmelCase       :   Dict  =    LTP(args.ltp   )  # faster in GPU device
    _lowerCAmelCase       :   Optional[int]  =    BertTokenizer.from_pretrained(args.bert   )

    _lowerCAmelCase       :   Optional[Any]  =    prepare_ref(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

    with open(args.save_path ,   'w' ,   encoding='utf-8'   ) as f:
        _lowerCAmelCase       :   Any  =    [json.dumps(_lowerCamelCase   ) + '\n' for ref in ref_ids]
        f.writelines(_lowerCamelCase   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""", type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path"""
 )
 parser.add_argument("""--bert""", type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""")
 parser.add_argument("""--save_path""", type=str, default="""./resources/ref.txt""", help="""path to save res""")

 _lowerCAmelCase						=		parser.parse_args()
 main(args)





