





"""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   (      a_    :List[Any] ,							a_    :List[Any])		->		List[str]:
				__a			:    List[str]      							=	checkpoint

				__a			:    Dict      							=	{}

				__a			:    Tuple      							=	vae_state_dict['''encoder.conv_in.weight''']
				__a			:    Tuple      							=	vae_state_dict['''encoder.conv_in.bias''']
				__a			:    List[str]      							=	vae_state_dict['''encoder.conv_out.weight''']
				__a			:    int      							=	vae_state_dict['''encoder.conv_out.bias''']
				__a			:    List[str]      							=	vae_state_dict['''encoder.norm_out.weight''']
				__a			:    str      							=	vae_state_dict['''encoder.norm_out.bias''']

				__a			:    Optional[int]      							=	vae_state_dict['''decoder.conv_in.weight''']
				__a			:    List[Any]      							=	vae_state_dict['''decoder.conv_in.bias''']
				__a			:    str      							=	vae_state_dict['''decoder.conv_out.weight''']
				__a			:    int      							=	vae_state_dict['''decoder.conv_out.bias''']
				__a			:    Dict      							=	vae_state_dict['''decoder.norm_out.weight''']
				__a			:    List[str]      							=	vae_state_dict['''decoder.norm_out.bias''']

				__a			:    List[Any]      							=	vae_state_dict['''quant_conv.weight''']
				__a			:    Tuple      							=	vae_state_dict['''quant_conv.bias''']
				__a			:    int      							=	vae_state_dict['''post_quant_conv.weight''']
				__a			:    Optional[int]      							=	vae_state_dict['''post_quant_conv.bias''']

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

				# Retrieves the keys for the decoder up blocks only
				__a			:    Union[str, Any]      							=	len({'''.'''.join(layer.split('''.''')[:3]) for layer in vae_state_dict if '''decoder.up''' in layer})
				__a			:    List[Any]      							=	{
				    layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(a_)
				}

				for i in range(a_):
								__a			:    Dict      							=	[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:
												__a			:    List[Any]      							=	vae_state_dict.pop(
												    F"""encoder.down.{i}.downsample.conv.weight""")
												__a			:    Any      							=	vae_state_dict.pop(
												    F"""encoder.down.{i}.downsample.conv.bias""")

								__a			:    List[str]      							=	renew_vae_resnet_paths(a_)
								__a			:    Union[str, Any]      							=	{'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""}
								assign_to_checkpoint(a_ ,							a_ ,							a_ ,							additional_replacements=[meta_path] ,							config=a_)

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

								__a			:    Union[str, Any]      							=	renew_vae_resnet_paths(a_)
								__a			:    str      							=	{'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
								assign_to_checkpoint(a_ ,							a_ ,							a_ ,							additional_replacements=[meta_path] ,							config=a_)

				__a			:    List[Any]      							=	[key for key in vae_state_dict if '''encoder.mid.attn''' in key]
				__a			:    List[str]      							=	renew_vae_attention_paths(a_)
				__a			:    Optional[Any]      							=	{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
				assign_to_checkpoint(a_ ,							a_ ,							a_ ,							additional_replacements=[meta_path] ,							config=a_)
				conv_attn_to_linear(a_)

				for i in range(a_):
								__a			:    str      							=	num_up_blocks - 1 - i
								__a			:    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:
												__a			:    Optional[int]      							=	vae_state_dict[
												    F"""decoder.up.{block_id}.upsample.conv.weight"""
												]
												__a			:    List[str]      							=	vae_state_dict[
												    F"""decoder.up.{block_id}.upsample.conv.bias"""
												]

								__a			:    Union[str, Any]      							=	renew_vae_resnet_paths(a_)
								__a			:    Any      							=	{'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""}
								assign_to_checkpoint(a_ ,							a_ ,							a_ ,							additional_replacements=[meta_path] ,							config=a_)

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

								__a			:    Optional[Any]      							=	renew_vae_resnet_paths(a_)
								__a			:    List[str]      							=	{'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
								assign_to_checkpoint(a_ ,							a_ ,							a_ ,							additional_replacements=[meta_path] ,							config=a_)

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

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

				__a			:    Tuple      							=	OmegaConf.load(a_)
				__a			:    Union[str, Any]      							=	5_12
				__a			:    Optional[Any]      							=	'''cuda''' if torch.cuda.is_available() else '''cpu'''
				if checkpoint_path.endswith('''safetensors'''):
								from safetensors import safe_open

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

				# Convert the VAE model.
				__a			:    int      							=	create_vae_diffusers_config(a_ ,							image_size=a_)
				__a			:    Tuple      							=	custom_convert_ldm_vae_checkpoint(a_ ,							a_)

				__a			:    Optional[int]      							=	AutoencoderKL(**a_)
				vae.load_state_dict(a_)
				vae.save_pretrained(a_)


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_rembert import RemBertTokenizer
else:
	lowercase    				=   None

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

lowercase    				=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

lowercase    				=   {
    "google/rembert": 256,
}

lowercase    				=   "▁"




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					VOCAB_FILES_NAMES
	lowerCAmelCase						=					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase						=					RemBertTokenizer




	def __init__(			self			,     a=None			,     a=None			,     a=True			,     a=True			,     a=False			,     a="[CLS]"			,     a="[SEP]"			,     a="<unk>"			,     a="[SEP]"			,     a="<pad>"			,     a="[CLS]"			,     a="[MASK]"			,     **a			,     )			->       Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								snake_case_           =				AddedToken(a			,     lstrip=a			,     rstrip=a   ) if isinstance(a			,     a   ) else mask_token

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

								snake_case_           =				do_lower_case
								snake_case_           =				remove_space
								snake_case_           =				keep_accents
								snake_case_           =				vocab_file
								snake_case_           =				False if not self.vocab_file else True




	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								snake_case_           =				[self.sep_token_id]
								snake_case_           =				[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 				_UpperCamelCase     (			self			,     a			,     a = None			,     a = False   )			->       List[int]:

								if already_has_special_tokens:
															if token_ids_a is not None:
																						raise ValueError(
																						    'You should not supply a second sequence if the provided sequence of '
																						    'ids is already formatted with special tokens for the model.'   )
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
															return [1] + ([0] * len(a   )) + [1] + ([0] * len(a   )) + [1]
								return [1] + ([0] * len(a   )) + [1]




	def 				_UpperCamelCase     (			self			,     a			,     a = 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			,     a			,     a = None   )			->       Tuple[str]:
								if not os.path.isdir(a   ):
															logger.error('Vocabulary path ({}) should be a directory'.format(a   )   )
															return
								snake_case_           =				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,)





