
"""simple docstring"""





from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a		       =							logging.get_logger(__name__)


_a		       =							{
    'nielsr/canine-s': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
_a		       =							1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_a		       =							0

_a		       =							0xe000
_a		       =							0xe001
_a		       =							0xe002
_a		       =							0xe003
_a		       =							0xe004

# Maps special codepoints to human-readable names.
_a		       =							{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_a		       =							{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[str]       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







  def __init__(		self				,   lowercase_=chr(lowercase_  )				,   lowercase_=chr(lowercase_  )				,   lowercase_=chr(lowercase_  )				,   lowercase_=chr(lowercase_  )				,   lowercase_=chr(lowercase_  )				,   lowercase_=chr(lowercase_  )				,   lowercase_=False				,   lowercase_=2048				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_  ) if isinstance(lowercase_				,   lowercase_  ) else bos_token
     UpperCAmelCase_					:					Union[str, Any]			     =    AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_  ) if isinstance(lowercase_				,   lowercase_  ) else eos_token
     UpperCAmelCase_					:					int			     =    AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_  ) if isinstance(lowercase_				,   lowercase_  ) else sep_token
     UpperCAmelCase_					:					Any			     =    AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_  ) if isinstance(lowercase_				,   lowercase_  ) else cls_token
     UpperCAmelCase_					:					Optional[Any]			     =    AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_  ) if isinstance(lowercase_				,   lowercase_  ) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     UpperCAmelCase_					:					Optional[Any]			     =    AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_  ) if isinstance(lowercase_				,   lowercase_  ) else mask_token

     super().__init__(
         bos_token=lowercase_				,   eos_token=lowercase_				,   sep_token=lowercase_				,   cls_token=lowercase_				,   pad_token=lowercase_				,   mask_token=lowercase_				,   add_prefix_space=lowercase_				,   model_max_length=lowercase_				,   **lowercase_				,   )

     # Creates a mapping for looking up the IDs of special symbols.
     UpperCAmelCase_					:					Dict[str, int]			     =    {}
     for codepoint, name in SPECIAL_CODEPOINTS.items():
        UpperCAmelCase_					:					Union[str, Any]			     =    codepoint

     # Creates a mapping for looking up the string forms of special symbol IDs.
     UpperCAmelCase_					:					Dict[int, str]			     =    {
         codepoint: name for name, codepoint in self._special_codepoints.items()
     }

     UpperCAmelCase_					:					str			     =    UNICODE_VOCAB_SIZE
     UpperCAmelCase_					:					Optional[int]			     =    len(self._special_codepoints  )







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return self._unicode_vocab_size







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     return list(lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     try:
        return ord(lowercase_  )
     except TypeError:
        raise ValueError(F"""invalid token: '{token}'"""  )







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     try:
        if index in SPECIAL_CODEPOINTS:
           return SPECIAL_CODEPOINTS[index]
        return chr(lowercase_  )
     except TypeError:
        raise ValueError(F"""invalid id: {index}"""  )







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     return "".join(lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    [self.sep_token_id]
     UpperCAmelCase_					:					int			     =    [self.cls_token_id]

     UpperCAmelCase_					:					List[Any]			     =    cls + token_ids_a + sep
     if token_ids_a is not None:
        result += token_ids_a + sep
     return result







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None				,   lowercase_ = False  ):

     """simple docstring"""
     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowercase_				,   token_ids_a=lowercase_				,   already_has_special_tokens=lowercase_  )

     UpperCAmelCase_					:					Any			     =    [1] + ([0] * len(lowercase_  )) + [1]
     if token_ids_a is not None:
        result += ([0] * len(lowercase_  )) + [1]
     return result







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    [self.sep_token_id]
     UpperCAmelCase_					:					int			     =    [self.cls_token_id]

     UpperCAmelCase_					:					Union[str, Any]			     =    len(cls + token_ids_a + sep  ) * [0]
     if token_ids_a is not None:
        result += len(token_ids_a + sep  ) * [1]
     return result







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     return ()




"""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)


