

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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Union[str, Any]:



  """simple docstring"""






  _snake_case						=							checkpoint

  _snake_case						=							{}

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

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

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

  # Retrieves the keys for the encoder down blocks only
  _snake_case						=							len({'''.'''.join(layer.split('''.'''					)[:3]					) for layer in vae_state_dict if '''encoder.down''' in layer}					)
  _snake_case						=							{
      layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(_UpperCamelCase					)
  }

  # Retrieves the keys for the decoder up blocks only
  _snake_case						=							len({'''.'''.join(layer.split('''.'''					)[:3]					) for layer in vae_state_dict if '''decoder.up''' in layer}					)
  _snake_case						=							{
      layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(_UpperCamelCase					)
  }

  for i in range(_UpperCamelCase					):
    _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:
      _snake_case						=							vae_state_dict.pop(
          F"""encoder.down.{i}.downsample.conv.weight"""					)
      _snake_case						=							vae_state_dict.pop(
          F"""encoder.down.{i}.downsample.conv.bias"""					)

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

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

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

  _snake_case						=							[key for key in vae_state_dict if '''encoder.mid.attn''' in key]
  _snake_case						=							renew_vae_attention_paths(_UpperCamelCase					)
  _snake_case						=							{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
  assign_to_checkpoint(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	additional_replacements=[meta_path]			,	config=_UpperCamelCase					)
  conv_attn_to_linear(_UpperCamelCase					)

  for i in range(_UpperCamelCase					):
    _snake_case						=							num_up_blocks - 1 - i
    _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:
      _snake_case						=							vae_state_dict[
          F"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      _snake_case						=							vae_state_dict[
          F"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

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

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

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

  _snake_case						=							[key for key in vae_state_dict if '''decoder.mid.attn''' in key]
  _snake_case						=							renew_vae_attention_paths(_UpperCamelCase					)
  _snake_case						=							{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
  assign_to_checkpoint(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	additional_replacements=[meta_path]			,	config=_UpperCamelCase					)
  conv_attn_to_linear(_UpperCamelCase					)
  return new_checkpoint



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	)							->     str:



  """simple docstring"""






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

  _snake_case						=							OmegaConf.load(_UpperCamelCase					)
  _snake_case						=							512
  _snake_case						=							'''cuda''' if torch.cuda.is_available() else '''cpu'''
  if checkpoint_path.endswith('''safetensors'''					):
    from safetensors import safe_open

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

  # Convert the VAE model.
  _snake_case						=							create_vae_diffusers_config(_UpperCamelCase			,	image_size=_UpperCamelCase					)
  _snake_case						=							custom_convert_ldm_vae_checkpoint(_UpperCamelCase			,	_UpperCamelCase					)

  _snake_case						=							AutoencoderKL(**_UpperCamelCase					)
  vae.load_state_dict(_UpperCamelCase					)
  vae.save_pretrained(_UpperCamelCase					)


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)




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__A      		=     logging.get_logger(__name__)

__A      		=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A      		=     {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__A      		=     {'''allegro/herbert-base-cased''': 5_14}
__A      		=     {}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Any										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_      :		Dict										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Optional[Any]										=    HerbertTokenizer



      def __init__(      self				:    Tuple      , A__				:    str=None      , A__				:    Optional[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[int]="<s>"      , A__				:    Optional[int]="<unk>"      , A__				:    str="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Dict="</s>"      , **A__				:    Optional[int]      , )   ->					Optional[int]:
        super().__init__(
            A__      , A__      , tokenizer_file=A__      , cls_token=A__      , unk_token=A__      , pad_token=A__      , mask_token=A__      , sep_token=A__      , **A__      , )



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[self.sep_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 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__      , token_ids_a=A__      , already_has_special_tokens=A__		)

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



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _snake_case						=							[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 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							self._tokenizer.model.save(A__      , name=A__		)
        return tuple(A__		)

