'''simple docstring'''





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


SCREAMING_SNAKE_CASE_:     str  =logging.getLogger(__name__)
SCREAMING_SNAKE_CASE_:     str  =list(MODEL_FOR_MASKED_LM_MAPPING.keys())
SCREAMING_SNAKE_CASE_:     Tuple  =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class        __A      :
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }		,	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase__	)}		,	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }		,	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,	)
    a__       :						bool          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}		,	)
    a__       :						str          =  field(
        default="""main"""		,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,	)
    a__       :						bool          =  field(
        default=UpperCamelCase__		,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }		,	)



    def 					_lowercase						(self    :    str ):
         if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
              raise ValueError(
                  "--config_overrides can't be used in combination with --config_name or --model_name_or_path" )




@dataclass
class        __A      :
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
    a__       :						Optional[str]          =  field(default=UpperCamelCase__		,	metadata={"""help""": """The input training data file (a text file)."""}	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}		,	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}		,	)
    a__       :						Optional[str]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}		,	)
    a__       :						bool          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}	)
    a__       :						Optional[int]          =  field(
        default=5		,	metadata={
            """help""": """The percentage of the train set used as validation set in case there's no validation split"""
        }		,	)
    a__       :						Optional[int]          =  field(
        default=UpperCamelCase__		,	metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated. Default to the max input length of the model."""
            )
        }		,	)
    a__       :						Optional[int]          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """The number of processes to use for the preprocessing."""}		,	)
    a__       :						float          =  field(
        default=0.1_5		,	metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}	)
    a__       :						bool          =  field(
        default=UpperCamelCase__		,	metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }		,	)



    def 					_lowercase						(self    :    List[str] ):
         if self.train_file is not None:
              UpperCAmelCase_           =   self.train_file.split("." )[-1]
              assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
         if self.validation_file is not None:
              UpperCAmelCase_           =   self.validation_file.split("." )[-1]
              assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : Optional[int]			)    ->      Optional[Any]:


     '''simple docstring'''




     with open(snake_case_					,    "r"					,    encoding="utf-8"			) as f:
          UpperCAmelCase_           =   [json.loads(snake_case_			) for line in f.read().splitlines() if (len(snake_case_			) > 0 and not line.isspace())]
     assert len(snake_case_			) == len(snake_case_			)

     UpperCAmelCase_           =   {c: dataset[c] for c in dataset.column_names}
     UpperCAmelCase_           =   refs
     return Dataset.from_dict(snake_case_			)





def 	lowerCAmelCase_				(					)    ->      Any:


     '''simple docstring'''




     UpperCAmelCase_           =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
     if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     UpperCAmelCase_           =   None
     if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCAmelCase_           =   get_last_checkpoint(training_args.output_dir			)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
               raise ValueError(
                   f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                   "Use --overwrite_output_dir to overcome."			)
          elif last_checkpoint is not None:
               logger.info(
                   f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                   "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."			)

    # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,    datefmt="%m/%d/%Y %H:%M:%S"					,    handlers=[logging.StreamHandler(sys.stdout			)]					,    )
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank			) else logging.WARN			)

     # Log on each process the small summary:
     logger.warning(
         f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
         + f"""distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}"""			)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank			):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info("Training/evaluation parameters %s"					,    snake_case_			)

     # Set seed before initializing model.
     set_seed(training_args.seed			)

     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
     # 'text' is found. You can easily tweak this behavior (see below).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          UpperCAmelCase_           =   load_dataset(data_args.dataset_name					,    data_args.dataset_config_name			)
          if "validation" not in datasets.keys():
               UpperCAmelCase_           =   load_dataset(
                   data_args.dataset_name					,    data_args.dataset_config_name					,    split=f"""train[:{data_args.validation_split_percentage}%]"""					,    )
               UpperCAmelCase_           =   load_dataset(
                   data_args.dataset_name					,    data_args.dataset_config_name					,    split=f"""train[{data_args.validation_split_percentage}%:]"""					,    )
     else:
          UpperCAmelCase_           =   {}
          if data_args.train_file is not None:
               UpperCAmelCase_           =   data_args.train_file
          if data_args.validation_file is not None:
               UpperCAmelCase_           =   data_args.validation_file
          UpperCAmelCase_           =   data_args.train_file.split("."			)[-1]
          if extension == "txt":
               UpperCAmelCase_           =   "text"
          UpperCAmelCase_           =   load_dataset(snake_case_					,    data_files=snake_case_			)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     UpperCAmelCase_           =   {
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          UpperCAmelCase_           =   AutoConfig.from_pretrained(model_args.config_name					,    **snake_case_			)
     elif model_args.model_name_or_path:
          UpperCAmelCase_           =   AutoConfig.from_pretrained(model_args.model_name_or_path					,    **snake_case_			)
     else:
          UpperCAmelCase_           =   CONFIG_MAPPING[model_args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch."			)
          if model_args.config_overrides is not None:
               logger.info(f"""Overriding config: {model_args.config_overrides}"""			)
               config.update_from_string(model_args.config_overrides			)
               logger.info(f"""New config: {config}"""			)

     UpperCAmelCase_           =   {
         "cache_dir": model_args.cache_dir,
         "use_fast": model_args.use_fast_tokenizer,
         "revision": model_args.model_revision,
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     if model_args.tokenizer_name:
          UpperCAmelCase_           =   AutoTokenizer.from_pretrained(model_args.tokenizer_name					,    **snake_case_			)
     elif model_args.model_name_or_path:
          UpperCAmelCase_           =   AutoTokenizer.from_pretrained(model_args.model_name_or_path					,    **snake_case_			)
     else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "You can do it from another script, save it, and load it from here, using --tokenizer_name."			)

     if model_args.model_name_or_path:
          UpperCAmelCase_           =   AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path					,    from_tf=bool(".ckpt" in model_args.model_name_or_path			)					,    config=snake_case_					,    cache_dir=model_args.cache_dir					,    revision=model_args.model_revision					,    use_auth_token=True if model_args.use_auth_token else None					,    )
     else:
          logger.info("Training new model from scratch"			)
          UpperCAmelCase_           =   AutoModelForMaskedLM.from_config(snake_case_			)

     model.resize_token_embeddings(len(snake_case_			)			)

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          UpperCAmelCase_           =   datasets["train"].column_names
     else:
          UpperCAmelCase_           =   datasets["validation"].column_names
     UpperCAmelCase_           =   "text" if "text" in column_names else column_names[0]

     UpperCAmelCase_           =   "max_length" if data_args.pad_to_max_length else False

     def tokenize_function(snake_case_       : int			):
          # Remove empty lines
          UpperCAmelCase_           =   [line for line in examples["text"] if len(snake_case_			) > 0 and not line.isspace()]
          return tokenizer(examples["text"]					,    padding=snake_case_					,    truncation=snake_case_					,    max_length=data_args.max_seq_length			)

     UpperCAmelCase_           =   datasets.map(
         snake_case_					,    batched=snake_case_					,    num_proc=data_args.preprocessing_num_workers					,    remove_columns=[text_column_name]					,    load_from_cache_file=not data_args.overwrite_cache					,    )

     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
          UpperCAmelCase_           =   add_chinese_references(tokenized_datasets["train"]					,    data_args.train_ref_file			)
     if data_args.validation_ref_file is not None:
          UpperCAmelCase_           =   add_chinese_references(
              tokenized_datasets["validation"]					,    data_args.validation_ref_file			)
     # If we have ref files, need to avoid it removed by trainer
     UpperCAmelCase_           =   data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          UpperCAmelCase_           =   False

     # Data collator
     # This one will take care of randomly masking the tokens.
     UpperCAmelCase_           =   DataCollatorForWholeWordMask(tokenizer=snake_case_					,    mlm_probability=data_args.mlm_probability			)

     # Initialize our Trainer
     UpperCAmelCase_           =   Trainer(
         model=snake_case_					,    args=snake_case_					,    train_dataset=tokenized_datasets["train"] if training_args.do_train else None					,    eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None					,    tokenizer=snake_case_					,    data_collator=snake_case_					,    )

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               UpperCAmelCase_           =   last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path			):
               UpperCAmelCase_           =   model_args.model_name_or_path
          else:
               UpperCAmelCase_           =   None
          UpperCAmelCase_           =   trainer.train(resume_from_checkpoint=snake_case_			)
          trainer.save_model()  # Saves the tokenizer too for easy upload

          UpperCAmelCase_           =   os.path.join(training_args.output_dir					,    "train_results.txt"			)
          if trainer.is_world_process_zero():
               with open(snake_case_					,    "w"			) as writer:
                    logger.info("***** Train results *****"			)
                    for key, value in sorted(train_result.metrics.items()			):
                         logger.info(f"""  {key} = {value}"""			)
                         writer.write(f"""{key} = {value}\n"""			)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir					,    "trainer_state.json"			)			)

    # Evaluation
     UpperCAmelCase_           =   {}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"			)

          UpperCAmelCase_           =   trainer.evaluate()

          UpperCAmelCase_           =   math.exp(eval_output["eval_loss"]			)
          UpperCAmelCase_           =   perplexity

          UpperCAmelCase_           =   os.path.join(training_args.output_dir					,    "eval_results_mlm_wwm.txt"			)
          if trainer.is_world_process_zero():
               with open(snake_case_					,    "w"			) as writer:
                    logger.info("***** Eval results *****"			)
                    for key, value in sorted(results.items()			):
                         logger.info(f"""  {key} = {value}"""			)
                         writer.write(f"""{key} = {value}\n"""			)

     return results





def 	lowerCAmelCase_				(					snake_case_       : List[str]			)    ->      Union[str, Any]:


     '''simple docstring'''




     main()


if __name__ == "__main__":
    main()





'''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 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : int			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   checkpoint

     UpperCAmelCase_           =   {}

     UpperCAmelCase_           =   vae_state_dict["encoder.conv_in.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_in.bias"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_out.bias"]
     UpperCAmelCase_           =   vae_state_dict["encoder.norm_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.norm_out.bias"]

     UpperCAmelCase_           =   vae_state_dict["decoder.conv_in.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_in.bias"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_out.bias"]
     UpperCAmelCase_           =   vae_state_dict["decoder.norm_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.norm_out.bias"]

     UpperCAmelCase_           =   vae_state_dict["quant_conv.weight"]
     UpperCAmelCase_           =   vae_state_dict["quant_conv.bias"]
     UpperCAmelCase_           =   vae_state_dict["post_quant_conv.weight"]
     UpperCAmelCase_           =   vae_state_dict["post_quant_conv.bias"]

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

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

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"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_           =   [key for key in vae_state_dict if "encoder.mid.attn" in key]
     UpperCAmelCase_           =   renew_vae_attention_paths(snake_case_			)
     UpperCAmelCase_           =   {"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_           =   num_up_blocks - 1 - i
          UpperCAmelCase_           =   [
              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_           =   vae_state_dict[
                   f"""decoder.up.{block_id}.upsample.conv.weight"""
               ]
               UpperCAmelCase_           =   vae_state_dict[
                   f"""decoder.up.{block_id}.upsample.conv.bias"""
               ]

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

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"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_           =   [key for key in vae_state_dict if "decoder.mid.attn" in key]
     UpperCAmelCase_           =   renew_vae_attention_paths(snake_case_			)
     UpperCAmelCase_           =   {"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 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : str					,    )    ->      Dict:


     '''simple docstring'''




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

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

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

     # Convert the VAE model.
     UpperCAmelCase_           =   create_vae_diffusers_config(snake_case_					,    image_size=snake_case_			)
     UpperCAmelCase_           =   custom_convert_ldm_vae_checkpoint(snake_case_					,    snake_case_			)

     UpperCAmelCase_           =   AutoencoderKL(**snake_case_			)
     vae.load_state_dict(snake_case_			)
     vae.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Optional[int]  =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.')

    SCREAMING_SNAKE_CASE_:     str  =parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




