
"""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_					:					List[Any]			     =    checkpoint

   UpperCAmelCase_					:					Optional[Any]			     =    {}

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

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

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

   # Retrieves the keys for the encoder down blocks only
   UpperCAmelCase_					:					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(snake_case_  )
   }

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

   for i in range(snake_case_  ):
      UpperCAmelCase_					:					str			     =    [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_					:					Any			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.weight"""  )
         UpperCAmelCase_					:					List[str]			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.bias"""  )

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

   UpperCAmelCase_					:					List[str]			     =    [key for key in vae_state_dict if """encoder.mid.block""" in key]
   UpperCAmelCase_					:					Union[str, Any]			     =    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_					:					str			     =    renew_vae_resnet_paths(snake_case_  )
      UpperCAmelCase_					:					str			     =    {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(snake_case_,		snake_case_,		snake_case_,		additional_replacements=[meta_path],		config=snake_case_  )

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

   for i in range(snake_case_  ):
      UpperCAmelCase_					:					List[str]			     =    num_up_blocks - 1 - i
      UpperCAmelCase_					:					List[str]			     =    [
          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_					:					Union[str, Any]			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.weight"""
         ]
         UpperCAmelCase_					:					List[str]			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.bias"""
         ]

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

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

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

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







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

   UpperCAmelCase_					:					List[Any]			     =    OmegaConf.load(snake_case_  )
   UpperCAmelCase_					:					Optional[int]			     =    512
   UpperCAmelCase_					:					Dict			     =    """cuda""" if torch.cuda.is_available() else """cpu"""
   if checkpoint_path.endswith("safetensors"  ):
      from safetensors import safe_open

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

   # Convert the VAE model.
   UpperCAmelCase_					:					List[str]			     =    create_vae_diffusers_config(snake_case_,		image_size=snake_case_  )
   UpperCAmelCase_					:					Any			     =    custom_convert_ldm_vae_checkpoint(snake_case_,		snake_case_  )

   UpperCAmelCase_					:					List[Any]			     =    AutoencoderKL(**snake_case_  )
   vae.load_state_dict(snake_case_  )
   vae.save_pretrained(snake_case_  )


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)








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_fnet import FNetTokenizer
else:
  _snake_case										=       None

_snake_case										=       logging.get_logger(__name__)
_snake_case										=       {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_snake_case										=       {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

_snake_case										=       {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_snake_case										=       "▁"







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						VOCAB_FILES_NAMES
     _a							 =						PRETRAINED_VOCAB_FILES_MAP
     _a							 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a							 =						["input_ids", "token_type_ids"]
     _a							 =						FNetTokenizer

     def __init__(  self		,     _a=None		,     _a=None		,     _a=False		,     _a=True		,     _a=True		,     _a="<unk>"		,     _a="[SEP]"		,     _a="<pad>"		,     _a="[CLS]"		,     _a="[MASK]"		,     **_a		,     )						->							Optional[int]:
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.
           _A							:     int			=						(
               AddedToken(_a		,     lstrip=_a		,     rstrip=_a		,     normalized=_a				)
               if isinstance(_a		,     _a				)
               else mask_token
           )

           super().__init__(
               _a		,     tokenizer_file=_a		,     do_lower_case=_a		,     remove_space=_a		,     keep_accents=_a		,     unk_token=_a		,     sep_token=_a		,     pad_token=_a		,     cls_token=_a		,     mask_token=_a		,     **_a		,     )

           _A							:     Optional[int]			=						do_lower_case
           _A							:     List[Any]			=						remove_space
           _A							:     str			=						keep_accents
           _A							:     int			=						vocab_file
           _A							:     int			=						False if not self.vocab_file else True

     def 					a__	(  self		,     _a		,     _a = None				)						->							List[int]:
           _A							:     str			=						[self.sep_token_id]
           _A							:     Dict			=						[self.cls_token_id]
           if token_ids_a is None:
                 return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep

     def 					a__	(  self		,     _a		,     _a = None				)						->							List[int]:
           _A							:     Any			=						[self.sep_token_id]
           _A							:     Any			=						[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep				) * [0]
           return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]



     def 					a__	(  self		,     _a		,     _a = None				)						->							Tuple[str]:
           if not os.path.isdir(_a				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           _A							:     List[str]			=						os.path.join(
               _a		,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

           if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
                 copyfile(self.vocab_file		,     _a				)

           return (out_vocab_file,)

