
"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger


class A_	(lowercase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    params

     UpperCAmelCase_					:					int			     =    np.array(lowercase_  )
     UpperCAmelCase_					:					Union[str, Any]			     =    np.array([len(lowercase_  ) for t in data]  )

     self.check()
     self.remove_long_sequences()
     self.remove_empty_sequences()
     self.remove_unknown_sequences()
     self.check()
     self.print_statistics()







  def __getitem__(		self				,   lowercase_  ):

     """simple docstring"""
     return (self.token_ids[index], self.lengths[index])







  def __len__(		self  ):

     """simple docstring"""
     return len(self.lengths  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     assert len(self.token_ids  ) == len(self.lengths  )
     assert all(self.lengths[i] == len(self.token_ids[i]  ) for i in range(len(self.lengths  )  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.params.max_model_input_size
     UpperCAmelCase_					:					str			     =    self.lengths > max_len
     logger.info(F"""Splitting {sum(lowercase_  )} too long sequences."""  )

     def divide_chunks(lowercase_				,   lowercase_  ):
        return [l[i : i + n] for i in range(0				,   len(lowercase_  )				,   lowercase_  )]

     UpperCAmelCase_					:					Union[str, Any]			     =    []
     UpperCAmelCase_					:					List[Any]			     =    []
     if self.params.mlm:
        UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
     else:
        UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

     for seq_, len_ in zip(self.token_ids				,   self.lengths  ):
        assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
        if len_ <= max_len:
           new_tok_ids.append(seq_  )
           new_lengths.append(len_  )
        else:
           UpperCAmelCase_					:					Optional[int]			     =    []
           for sub_s in divide_chunks(seq_				,   max_len - 2  ):
              if sub_s[0] != cls_id:
                 UpperCAmelCase_					:					str			     =    np.insert(lowercase_				,   0				,   lowercase_  )
              if sub_s[-1] != sep_id:
                 UpperCAmelCase_					:					List[str]			     =    np.insert(lowercase_				,   len(lowercase_  )				,   lowercase_  )
              assert len(lowercase_  ) <= max_len
              assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
              sub_seqs.append(lowercase_  )

           new_tok_ids.extend(lowercase_  )
           new_lengths.extend([len(lowercase_  ) for l in sub_seqs]  )

     UpperCAmelCase_					:					List[str]			     =    np.array(lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    np.array(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    len(self  )
     UpperCAmelCase_					:					Tuple			     =    self.lengths > 11
     UpperCAmelCase_					:					Any			     =    self.token_ids[indices]
     UpperCAmelCase_					:					List[Any]			     =    self.lengths[indices]
     UpperCAmelCase_					:					List[Any]			     =    len(self  )
     logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if "unk_token" not in self.params.special_tok_ids:
        return
     else:
        UpperCAmelCase_					:					List[str]			     =    self.params.special_tok_ids["unk_token"]
     UpperCAmelCase_					:					Optional[Any]			     =    len(self  )
     UpperCAmelCase_					:					List[str]			     =    np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
     UpperCAmelCase_					:					List[Any]			     =    (unk_occs / self.lengths) < 0.5
     UpperCAmelCase_					:					Tuple			     =    self.token_ids[indices]
     UpperCAmelCase_					:					str			     =    self.lengths[indices]
     UpperCAmelCase_					:					int			     =    len(self  )
     logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if not self.params.is_master:
        return
     logger.info(F"""{len(self  )} sequences"""  )
     # data_len = sum(self.lengths)
     # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
     # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

     # unk_idx = self.params.special_tok_ids['unk_token']
     # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
     # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    [t[0] for t in batch]
     UpperCAmelCase_					:					Tuple			     =    [t[1] for t in batch]
     assert len(lowercase_  ) == len(lowercase_  )

     # Max for paddings
     UpperCAmelCase_					:					int			     =    max(lowercase_  )

     # Pad token ids
     if self.params.mlm:
        UpperCAmelCase_					:					Optional[Any]			     =    self.params.special_tok_ids["pad_token"]
     else:
        UpperCAmelCase_					:					int			     =    self.params.special_tok_ids["unk_token"]
     UpperCAmelCase_					:					int			     =    [list(t.astype(lowercase_  )  ) + [pad_idx] * (max_seq_len_ - len(lowercase_  )) for t in token_ids]
     assert len(tk_  ) == len(lowercase_  )
     assert all(len(lowercase_  ) == max_seq_len_ for t in tk_  )

     UpperCAmelCase_					:					List[Any]			     =    torch.tensor(tk_  )  # (bs, max_seq_len_)
     UpperCAmelCase_					:					str			     =    torch.tensor(lowercase_  )  # (bs)
     return tk_t, lg_t




"""simple docstring"""





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					str			     =    checkpoint

   UpperCAmelCase_					:					int			     =    {}

   UpperCAmelCase_					:					Optional[int]			     =    vae_state_dict["encoder.conv_in.weight"]
   UpperCAmelCase_					:					List[str]			     =    vae_state_dict["encoder.conv_in.bias"]
   UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict["encoder.conv_out.weight"]
   UpperCAmelCase_					:					Optional[int]			     =    vae_state_dict["encoder.conv_out.bias"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["encoder.norm_out.weight"]
   UpperCAmelCase_					:					Union[str, Any]			     =    vae_state_dict["encoder.norm_out.bias"]

   UpperCAmelCase_					:					Any			     =    vae_state_dict["decoder.conv_in.weight"]
   UpperCAmelCase_					:					int			     =    vae_state_dict["decoder.conv_in.bias"]
   UpperCAmelCase_					:					Any			     =    vae_state_dict["decoder.conv_out.weight"]
   UpperCAmelCase_					:					Tuple			     =    vae_state_dict["decoder.conv_out.bias"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["decoder.norm_out.weight"]
   UpperCAmelCase_					:					Tuple			     =    vae_state_dict["decoder.norm_out.bias"]

   UpperCAmelCase_					:					str			     =    vae_state_dict["quant_conv.weight"]
   UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict["quant_conv.bias"]
   UpperCAmelCase_					:					List[str]			     =    vae_state_dict["post_quant_conv.weight"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["post_quant_conv.bias"]

   # Retrieves the keys for the encoder down blocks only
   UpperCAmelCase_					:					Optional[Any]			     =    len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "encoder.down" in layer}  )
   UpperCAmelCase_					:					Optional[Any]			     =    {
       layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__lowerCamelCase  )
   }

   # Retrieves the keys for the decoder up blocks only
   UpperCAmelCase_					:					Dict			     =    len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "decoder.up" in layer}  )
   UpperCAmelCase_					:					Optional[int]			     =    {
       layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__lowerCamelCase  )
   }

   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					Any			     =    [key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

      if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
         UpperCAmelCase_					:					Dict			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.weight"""  )
         UpperCAmelCase_					:					Dict			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.bias"""  )

      UpperCAmelCase_					:					List[str]			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Optional[Any]			     =    {"old": f"""down.{i}.block""", "new": f"""down_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					List[Any]			     =    [key for key in vae_state_dict if "encoder.mid.block" in key]
   UpperCAmelCase_					:					Tuple			     =    2
   for i in range(1,		num_mid_res_blocks + 1  ):
      UpperCAmelCase_					:					str			     =    [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

      UpperCAmelCase_					:					List[Any]			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Optional[int]			     =    {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[int]			     =    [key for key in vae_state_dict if "encoder.mid.attn" in key]
   UpperCAmelCase_					:					Union[str, Any]			     =    renew_vae_attention_paths(__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )
   conv_attn_to_linear(__lowerCamelCase  )

   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					Optional[Any]			     =    num_up_blocks - 1 - i
      UpperCAmelCase_					:					Any			     =    [
          key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
      ]

      if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
         UpperCAmelCase_					:					str			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.weight"""
         ]
         UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.bias"""
         ]

      UpperCAmelCase_					:					Dict			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					List[str]			     =    {"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[Any]			     =    [key for key in vae_state_dict if "decoder.mid.block" in key]
   UpperCAmelCase_					:					List[Any]			     =    2
   for i in range(1,		num_mid_res_blocks + 1  ):
      UpperCAmelCase_					:					str			     =    [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

      UpperCAmelCase_					:					Tuple			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Tuple			     =    {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[int]			     =    [key for key in vae_state_dict if "decoder.mid.attn" in key]
   UpperCAmelCase_					:					Any			     =    renew_vae_attention_paths(__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )
   conv_attn_to_linear(__lowerCamelCase  )
   return new_checkpoint







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		):
   # Only support V1
   UpperCAmelCase_					:					List[str]			     =    requests.get(
       " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"  )
   UpperCAmelCase_					:					List[Any]			     =    io.BytesIO(r.content  )

   UpperCAmelCase_					:					Any			     =    OmegaConf.load(__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    512
   UpperCAmelCase_					:					Optional[Any]			     =    "cuda" if torch.cuda.is_available() else "cpu"
   if checkpoint_path.endswith("safetensors"  ):
      from safetensors import safe_open

      UpperCAmelCase_					:					int			     =    {}
      with safe_open(__lowerCamelCase,		framework="pt",		device="cpu"  ) as f:
         for key in f.keys():
            UpperCAmelCase_					:					Tuple			     =    f.get_tensor(__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					Any			     =    torch.load(__lowerCamelCase,		map_location=__lowerCamelCase  )["state_dict"]

   # Convert the VAE model.
   UpperCAmelCase_					:					Dict			     =    create_vae_diffusers_config(__lowerCamelCase,		image_size=__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    custom_convert_ldm_vae_checkpoint(__lowerCamelCase,		__lowerCamelCase  )

   UpperCAmelCase_					:					int			     =    AutoencoderKL(**__lowerCamelCase  )
   vae.load_state_dict(__lowerCamelCase  )
   vae.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()

    parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

    _a		       =							parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


