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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       Union[str, Any]	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      checkpoint

  SCREAMING_SNAKE_CASE__										=      {}

  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''encoder.conv_in.weight''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''encoder.conv_in.bias''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''encoder.conv_out.weight''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''encoder.conv_out.bias''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''encoder.norm_out.weight''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''encoder.norm_out.bias''']

  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''decoder.conv_in.weight''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''decoder.conv_in.bias''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''decoder.conv_out.weight''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''decoder.conv_out.bias''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''decoder.norm_out.weight''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''decoder.norm_out.bias''']

  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''quant_conv.weight''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''quant_conv.bias''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''post_quant_conv.weight''']
  SCREAMING_SNAKE_CASE__										=      vae_state_dict['''post_quant_conv.bias''']

  # Retrieves the keys for the encoder down blocks only
  SCREAMING_SNAKE_CASE__										=      len({""".""".join(layer.split("""."""	)[:3]	) for layer in vae_state_dict if """encoder.down""" in layer}	)
  SCREAMING_SNAKE_CASE__										=      {
      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
  SCREAMING_SNAKE_CASE__										=      len({""".""".join(layer.split("""."""	)[:3]	) for layer in vae_state_dict if """decoder.up""" in layer}	)
  SCREAMING_SNAKE_CASE__										=      {
      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	):
    SCREAMING_SNAKE_CASE__										=      [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:
      SCREAMING_SNAKE_CASE__										=      vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""	)
      SCREAMING_SNAKE_CASE__										=      vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""	)

    SCREAMING_SNAKE_CASE__										=      renew_vae_resnet_paths(_lowerCAmelCase	)
    SCREAMING_SNAKE_CASE__										=      {'''old''': f"""down.{i}.block""", '''new''': f"""down_blocks.{i}.resnets"""}
    assign_to_checkpoint(_lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase ,     additional_replacements=[meta_path] ,     config=_lowerCAmelCase	)

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

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

  SCREAMING_SNAKE_CASE__										=      [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
  SCREAMING_SNAKE_CASE__										=      renew_vae_attention_paths(_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      {'''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	):
    SCREAMING_SNAKE_CASE__										=      num_up_blocks - 1 - i
    SCREAMING_SNAKE_CASE__										=      [
        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:
      SCREAMING_SNAKE_CASE__										=      vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      SCREAMING_SNAKE_CASE__										=      vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

    SCREAMING_SNAKE_CASE__										=      renew_vae_resnet_paths(_lowerCAmelCase	)
    SCREAMING_SNAKE_CASE__										=      {'''old''': f"""up.{block_id}.block""", '''new''': f"""up_blocks.{i}.resnets"""}
    assign_to_checkpoint(_lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase ,     additional_replacements=[meta_path] ,     config=_lowerCAmelCase	)

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

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

  SCREAMING_SNAKE_CASE__										=      [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
  SCREAMING_SNAKE_CASE__										=      renew_vae_attention_paths(_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      {'''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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       str ,     )							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      requests.get(
      """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""	)
  SCREAMING_SNAKE_CASE__										=      io.BytesIO(r.content	)

  SCREAMING_SNAKE_CASE__										=      OmegaConf.load(_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      5_12
  SCREAMING_SNAKE_CASE__										=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if checkpoint_path.endswith("""safetensors"""	):
    from safetensors import safe_open

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

  # Convert the VAE model.
  SCREAMING_SNAKE_CASE__										=      create_vae_diffusers_config(_lowerCAmelCase ,     image_size=_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      custom_convert_ldm_vae_checkpoint(_lowerCAmelCase ,     _lowerCAmelCase	)

  SCREAMING_SNAKE_CASE__										=      AutoencoderKL(**_lowerCAmelCase	)
  vae.load_state_dict(_lowerCAmelCase	)
  vae.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: Dict							   =		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.''')

   __lowerCamelCase			: str							   =		parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)





'''simple docstring'''






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

import sentencepiece as spm

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


UpperCamelCase__:   Tuple								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__:   Optional[int]								=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

UpperCamelCase__:   Dict								=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCamelCase__:   Tuple								=			"▁"

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__			 =       ["""input_ids""", """attention_mask"""]







	def __init__(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple="<s>"		,			__snake_case			:     List[Any]="</s>"		,			__snake_case			:     int="</s>"		,			__snake_case			:     Any="<s>"		,			__snake_case			:     Optional[int]="<unk>"		,			__snake_case			:     Union[str, Any]="<pad>"		,			__snake_case			:     Union[str, Any]="<mask>"		,			__snake_case			:     Optional[Dict[str, Any]] = None		,			**__snake_case			:     Dict		,			)			->		None:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase :       int 							=		AddedToken(__snake_case		,			lstrip=__snake_case		,			rstrip=__snake_case		) if isinstance(__snake_case		,			__snake_case		) else mask_token

								UpperCAmelCase :       Union[str, Any] 							=		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__snake_case		,			eos_token=__snake_case		,			unk_token=__snake_case		,			sep_token=__snake_case		,			cls_token=__snake_case		,			pad_token=__snake_case		,			mask_token=__snake_case		,			sp_model_kwargs=self.sp_model_kwargs		,			**__snake_case		,			)

								UpperCAmelCase :       Optional[int] 							=		vocab_file
								UpperCAmelCase :       int 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(__snake_case		)		)

								UpperCAmelCase :       int 							=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								UpperCAmelCase :       Optional[Any] 							=		len(self.sp_model		) - 1
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def      A	(						self			:     Tuple		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase :       Union[str, Any] 							=		[self.cls_token_id]
								UpperCAmelCase :       Optional[Any] 							=		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







	def      A	(						self			:     int		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__snake_case		,			token_ids_a=__snake_case		,			already_has_special_tokens=__snake_case		)

								if token_ids_a is None:
															return [1] + ([0] * len(__snake_case		)) + [1]
								return [1] + ([0] * len(__snake_case		)) + [1, 1] + ([0] * len(__snake_case		)) + [1]







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Tuple 							=		[self.sep_token_id]
								UpperCAmelCase :       Tuple 							=		[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 + sep + token_ids_a + sep		) * [0]







	@property
	def      A	(						self			:     Dict		)			->		Optional[int]:
								return len(self.sp_model		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Optional[Any] 							=		{self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		List[str]:
								return self.sp_model.encode(__snake_case		,			out_type=__snake_case		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase :       Optional[Any] 							=		self.sp_model.PieceToId(__snake_case		)

								return spm_id if spm_id else self.unk_token_id







	def      A	(						self			:     int		,			__snake_case			:     Any		)			->		List[Any]:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		[]
								UpperCAmelCase :       int 							=		''''''
								UpperCAmelCase :       Union[str, Any] 							=		False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(__snake_case		) + token
																						UpperCAmelCase :       str 							=		True
																						UpperCAmelCase :       List[str] 							=		[]
															else:
																						current_sub_tokens.append(__snake_case		)
																						UpperCAmelCase :       Optional[int] 							=		False
								out_string += self.sp_model.decode(__snake_case		)
								return out_string.strip()







	def __getstate__(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.__dict__.copy()
								UpperCAmelCase :       Any 							=		None
								return state







	def __setstate__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Dict 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Optional[Any] 							=		{}

								UpperCAmelCase :       Any 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Union[str, Any] 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file		,			__snake_case		)
								elif not os.path.isfile(self.vocab_file		):
															with open(__snake_case		,			'''wb'''		) as fi:
																						UpperCAmelCase :       Any 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)





