'''simple docstring'''

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCamelCase :				str														= logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_lowerCamelCase :				Tuple														= {
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCamelCase :				Dict														= {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCamelCase :				Optional[Any]														= sorted(arg_to_scheduler.keys())
_lowerCamelCase :				str														= """{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class    lowerCamelCase__  (   pl.LightningModule     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="base"					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      **lowerCAmelCase__					,      )				->     str:
        """simple docstring"""





        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =0
        _UpperCamelCase  :Optional[int]       =Path(self.hparams.output_dir     )
        _UpperCamelCase  :Any       =self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
             _UpperCamelCase  :List[Any]       =AutoConfig.from_pretrained(
                 self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,      **({"""num_labels""": num_labels} if num_labels is not None else {})					,      cache_dir=lowerCAmelCase__					,      **lowerCAmelCase__					,      )
        else:
             _UpperCamelCase  :PretrainedConfig       =config

        _UpperCamelCase  :int       =("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
        for p in extra_model_params:
             if getattr(self.hparams					,      lowerCAmelCase__					,      lowerCAmelCase__     ):
                  assert hasattr(self.config					,      lowerCAmelCase__     ), f'''model config doesn\'t have a `{p}` attribute'''
                  setattr(self.config					,      lowerCAmelCase__					,      getattr(self.hparams					,      lowerCAmelCase__     )     )

        if tokenizer is None:
             _UpperCamelCase  :Optional[Any]       =AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,      cache_dir=lowerCAmelCase__					,      )
        else:
             _UpperCamelCase  :PreTrainedTokenizer       =tokenizer
        _UpperCamelCase  :Union[str, Any]       =MODEL_MODES[mode]
        if model is None:
             _UpperCamelCase  :Optional[int]       =self.model_type.from_pretrained(
                 self.hparams.model_name_or_path					,      from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path     )					,      config=self.config					,      cache_dir=lowerCAmelCase__					,      )
        else:
             _UpperCamelCase  :Optional[Any]       =model



   def  _UpperCamelCase		(    self					,      *lowerCAmelCase__					,      **lowerCAmelCase__     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =self.model_type.from_pretrained(*lowerCAmelCase__					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =arg_to_scheduler[self.hparams.lr_scheduler]
        _UpperCamelCase  :int       =get_schedule_func(
            self.opt					,      num_warmup_steps=self.hparams.warmup_steps					,      num_training_steps=self.total_steps()     )
        _UpperCamelCase  :Union[str, Any]       ={"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
        return scheduler



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.model
        _UpperCamelCase  :List[str]       =["""bias""", """LayerNorm.weight"""]
        _UpperCamelCase  :List[Any]       =[
            {
                """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )],
                """weight_decay""": self.hparams.weight_decay,
            },
            {
                """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay     )],
                """weight_decay""": 0.0,
            },
        ]
        if self.hparams.adafactor:
             _UpperCamelCase  :Dict       =Adafactor(
                 lowerCAmelCase__					,      lr=self.hparams.learning_rate					,      scale_parameter=lowerCAmelCase__					,      relative_step=lowerCAmelCase__     )

        else:
             _UpperCamelCase  :List[str]       =AdamW(
                 lowerCAmelCase__					,      lr=self.hparams.learning_rate					,      eps=self.hparams.adam_epsilon     )
        _UpperCamelCase  :str       =optimizer

        _UpperCamelCase  :Tuple       =self.get_lr_scheduler()

        return [optimizer], [scheduler]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        return self.validation_step(lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        return self.validation_end(lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =max(1					,      self.hparams.gpus     )  # TODO: consider num_tpu_cores
        _UpperCamelCase  :Union[str, Any]       =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        if mode == "test":
             _UpperCamelCase  :Union[str, Any]       =len(self.test_dataloader().dataset     )
        else:
             _UpperCamelCase  :Optional[int]       =self.get_dataloader("""train"""					,      self.hparams.train_batch_size					,      shuffle=lowerCAmelCase__     )
             _UpperCamelCase  :List[str]       =len(self.train_dataloader().dataset     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = False     )				->     List[str]:
        """simple docstring"""





        raise NotImplementedError("""You must implement this for your task"""     )



   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        return self.train_loader



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return self.get_dataloader("""dev"""					,      self.hparams.eval_batch_size					,      shuffle=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        return self.get_dataloader("""test"""					,      self.hparams.eval_batch_size					,      shuffle=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     int:
        """simple docstring"""





        return os.path.join(
            self.hparams.data_dir					,      """cached_{}_{}_{}""".format(
                lowerCAmelCase__					,      list(filter(lowerCAmelCase__					,      self.hparams.model_name_or_path.split("""/"""     )     )     ).pop()					,      str(self.hparams.max_seq_length     )					,      )					,      )



   @pl.utilities.rank_zero_only
   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     None:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.output_dir.joinpath("""best_tfmr"""     )
        _UpperCamelCase  :Optional[Any]       =self.step_count
        self.model.save_pretrained(lowerCAmelCase__     )
        self.tokenizer.save_pretrained(lowerCAmelCase__     )




   @staticmethod
   def  _UpperCamelCase		(    lowerCAmelCase__					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        parser.add_argument(
            """--model_name_or_path"""					,      default=lowerCAmelCase__					,      type=lowerCAmelCase__					,      required=lowerCAmelCase__					,      help="""Path to pretrained model or model identifier from huggingface.co/models"""					,      )
        parser.add_argument(
            """--config_name"""					,      default=""""""					,      type=lowerCAmelCase__					,      help="""Pretrained config name or path if not the same as model_name"""     )
        parser.add_argument(
            """--tokenizer_name"""					,      default=lowerCAmelCase__					,      type=lowerCAmelCase__					,      help="""Pretrained tokenizer name or path if not the same as model_name"""					,      )
        parser.add_argument(
            """--cache_dir"""					,      default=""""""					,      type=lowerCAmelCase__					,      help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""					,      )
        parser.add_argument(
            """--encoder_layerdrop"""					,      type=lowerCAmelCase__					,      help="""Encoder layer dropout probability (Optional). Goes into model.config"""					,      )
        parser.add_argument(
            """--decoder_layerdrop"""					,      type=lowerCAmelCase__					,      help="""Decoder layer dropout probability (Optional). Goes into model.config"""					,      )
        parser.add_argument(
            """--dropout"""					,      type=lowerCAmelCase__					,      help="""Dropout probability (Optional). Goes into model.config"""					,      )
        parser.add_argument(
            """--attention_dropout"""					,      type=lowerCAmelCase__					,      help="""Attention dropout probability (Optional). Goes into model.config"""					,      )
        parser.add_argument("""--learning_rate"""					,      default=5e-5					,      type=lowerCAmelCase__					,      help="""The initial learning rate for Adam."""     )
        parser.add_argument(
            """--lr_scheduler"""					,      default="""linear"""					,      choices=lowerCAmelCase__					,      metavar=lowerCAmelCase__					,      type=lowerCAmelCase__					,      help="""Learning rate scheduler"""					,      )
        parser.add_argument("""--weight_decay"""					,      default=0.0					,      type=lowerCAmelCase__					,      help="""Weight decay if we apply some."""     )
        parser.add_argument("""--adam_epsilon"""					,      default=1e-8					,      type=lowerCAmelCase__					,      help="""Epsilon for Adam optimizer."""     )
        parser.add_argument("""--warmup_steps"""					,      default=0					,      type=lowerCAmelCase__					,      help="""Linear warmup over warmup_steps."""     )
        parser.add_argument("""--num_workers"""					,      default=4					,      type=lowerCAmelCase__					,      help="""kwarg passed to DataLoader"""     )
        parser.add_argument("""--num_train_epochs"""					,      dest="""max_epochs"""					,      default=3					,      type=lowerCAmelCase__     )
        parser.add_argument("""--train_batch_size"""					,      default=32					,      type=lowerCAmelCase__     )
        parser.add_argument("""--eval_batch_size"""					,      default=32					,      type=lowerCAmelCase__     )
        parser.add_argument("""--adafactor"""					,      action="""store_true"""     )




class    lowerCamelCase__  (   pl.Callback     ):



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =trainer.lr_schedulers[0]["""scheduler"""]
        _UpperCamelCase  :Union[str, Any]       ={f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
        pl_module.logger.log_metrics(lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        rank_zero_info("""***** Validation results *****"""     )
        _UpperCamelCase  :Optional[Any]       =trainer.callback_metrics
        # Log results
        for key in sorted(lowerCAmelCase__     ):
             if key not in ["log", "progress_bar"]:
                  rank_zero_info("""{} = {}\n""".format(lowerCAmelCase__					,      str(metrics[key]     )     )     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        rank_zero_info("""***** Test results *****"""     )
        _UpperCamelCase  :List[str]       =trainer.callback_metrics
        # Log and save results to file
        _UpperCamelCase  :Dict       =os.path.join(pl_module.hparams.output_dir					,      """test_results.txt"""     )
        with open(lowerCAmelCase__					,      """w"""     ) as writer:
             for key in sorted(lowerCAmelCase__     ):
                  if key not in ["log", "progress_bar"]:
                       rank_zero_info("""{} = {}\n""".format(lowerCAmelCase__					,      str(metrics[key]     )     )     )
                       writer.write("""{} = {}\n""".format(lowerCAmelCase__					,      str(metrics[key]     )     )     )




def  _lowerCAmelCase				(				__a			,      __a      )  ->      None:
     '''simple docstring'''







     parser.add_argument(
         """--output_dir"""			,      default=__a			,      type=__a			,      required=__a			,      help="""The output directory where the model predictions and checkpoints will be written."""			,      )
     parser.add_argument(
         """--fp16"""			,      action="""store_true"""			,      help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""			,      )

     parser.add_argument(
         """--fp16_opt_level"""			,      type=__a			,      default="""O2"""			,      help=(
             """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
             """See details at https://nvidia.github.io/apex/amp.html"""
         )			,      )
     parser.add_argument("""--n_tpu_cores"""			,      dest="""tpu_cores"""			,      type=__a      )
     parser.add_argument("""--max_grad_norm"""			,      dest="""gradient_clip_val"""			,      default=1.0			,      type=__a			,      help="""Max gradient norm"""      )
     parser.add_argument("""--do_train"""			,      action="""store_true"""			,      help="""Whether to run training."""      )
     parser.add_argument("""--do_predict"""			,      action="""store_true"""			,      help="""Whether to run predictions on the test set."""      )
     parser.add_argument(
         """--gradient_accumulation_steps"""			,      dest="""accumulate_grad_batches"""			,      type=__a			,      default=1			,      help="""Number of updates steps to accumulate before performing a backward/update pass."""			,      )
     parser.add_argument("""--seed"""			,      type=__a			,      default=42			,      help="""random seed for initialization"""      )
     parser.add_argument(
         """--data_dir"""			,      default=__a			,      type=__a			,      required=__a			,      help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""			,      )



def  _lowerCAmelCase				(				__a			,      __a			,      __a=None			,      __a=True			,      __a=[]			,      __a=None			,      __a=None			,      **__a			,      )  ->      str:
     '''simple docstring'''







     pl.seed_everything(args.seed      )

     # init model
     _UpperCamelCase  :Any       =Path(model.hparams.output_dir      )
     odir.mkdir(exist_ok=__a      )

     # add custom checkpoints
     if checkpoint_callback is None:
          _UpperCamelCase  :Dict       =pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir			,      prefix="""checkpoint"""			,      monitor="""val_loss"""			,      mode="""min"""			,      save_top_k=1      )
     if early_stopping_callback:
          extra_callbacks.append(__a      )
     if logging_callback is None:
          _UpperCamelCase  :List[Any]       =LoggingCallback()

     _UpperCamelCase  :Union[str, Any]       ={}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _UpperCamelCase  :Optional[Any]       =16
          _UpperCamelCase  :int       =args.fpaa_opt_level

     if args.gpus > 1:
          _UpperCamelCase  :int       ="""ddp"""

     _UpperCamelCase  :List[str]       =args.accumulate_grad_batches
     _UpperCamelCase  :Tuple       =extra_train_kwargs.get("""accelerator"""			,      __a      )
     _UpperCamelCase  :Optional[int]       =extra_train_kwargs.get("""profiler"""			,      __a      )

     _UpperCamelCase  :Any       =pl.Trainer.from_argparse_args(
         __a			,      weights_summary=__a			,      callbacks=[logging_callback] + extra_callbacks			,      logger=__a			,      checkpoint_callback=__a			,      **__a			,      )

     if args.do_train:
          trainer.fit(__a      )

     return trainer







import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       Optional[Any]=1		)	-> Optional[int]:

						"""simple docstring"""



						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
						else:
												return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int=0		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item
												lowerCAmelCase__         =    new_item.replace('block.'     ,							'resnets.'		)
												lowerCAmelCase__         =    new_item.replace('conv_shorcut'     ,							'conv1'		)
												lowerCAmelCase__         =    new_item.replace('in_shortcut'     ,							'conv_shortcut'		)
												lowerCAmelCase__         =    new_item.replace('temb_proj'     ,							'time_emb_proj'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)

												mapping.append({'old': old_item, 'new': new_item}		)

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Union[str, Any]=0     ,							UpperCamelCase_							:       Dict=False		)	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		lowerCAmelCase__         =    new_item.replace('attn'     ,							'attentions'		)
												lowerCAmelCase__         =    new_item.replace('.k.'     ,							'.key.'		)
												lowerCAmelCase__         =    new_item.replace('.v.'     ,							'.value.'		)
												lowerCAmelCase__         =    new_item.replace('.q.'     ,							'.query.'		)

												lowerCAmelCase__         =    new_item.replace('proj_out'     ,							'proj_attn'		)
												lowerCAmelCase__         =    new_item.replace('norm'     ,							'group_norm'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)
												mapping.append({'old': old_item, 'new': new_item}		)

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       str=None     ,							UpperCamelCase_							:       str=None		)	-> Dict:

						"""simple docstring"""



						assert isinstance(UpperCamelCase_     ,							UpperCamelCase_		), "Paths should be a list of dicts containing 'old' and 'new' keys."

						if attention_paths_to_split is not None:
												if config is None:
																		raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'		)

												for path, path_map in attention_paths_to_split.items():
																		lowerCAmelCase__         =    old_checkpoint[path]
																		lowerCAmelCase__         =    old_tensor.shape[0] // 3

																		lowerCAmelCase__         =    (-1, channels) if len(old_tensor.shape		) == 3 else (-1)

																		lowerCAmelCase__         =    old_tensor.shape[0] // config.get('num_head_channels'     ,							1		) // 3

																		lowerCAmelCase__         =    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
																		lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    old_tensor.split(channels // num_heads     ,							dim=1		)

																		lowerCAmelCase__         =    query.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    key.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    value.reshape(UpperCamelCase_		).squeeze()

						for path in paths:
												lowerCAmelCase__         =    path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase__         =    new_path.replace('down.'     ,							'down_blocks.'		)
												lowerCAmelCase__         =    new_path.replace('up.'     ,							'up_blocks.'		)

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								lowerCAmelCase__         =    new_path.replace(replacement['old']     ,							replacement['new']		)

												if "attentions" in new_path:
																		lowerCAmelCase__         =    old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase__         =    old_checkpoint[path['old']]







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Dict		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['temb.dense.0.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.0.bias']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.bias']

						lowerCAmelCase__         =    checkpoint['norm_out.weight']
						lowerCAmelCase__         =    checkpoint['norm_out.bias']

						lowerCAmelCase__         =    checkpoint['conv_in.weight']
						lowerCAmelCase__         =    checkpoint['conv_in.bias']
						lowerCAmelCase__         =    checkpoint['conv_out.weight']
						lowerCAmelCase__         =    checkpoint['conv_out.bias']

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"down.{i}.downsample.op.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[F"down.{i}.downsample.op.bias"]
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

												if any('block' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							config=UpperCamelCase_		)

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]     ,							)

						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,							)

						lowerCAmelCase__         =    renew_attention_paths(UpperCamelCase_     ,							in_mid=UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,							)

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"up.{i}.upsample.conv.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[F"up.{i}.upsample.conv.bias"]

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

						lowerCAmelCase__         =    {k.replace('mid_new_2'     ,							'mid_block'		): v for k, v in new_checkpoint.items()}
						return new_checkpoint







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       int		)	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['encoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['encoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.bias']

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"encoder.down.{i}.downsample.conv.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[
																		    F"encoder.down.{i}.downsample.conv.bias"
																		]

												if any('block' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							config=UpperCamelCase_		)

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]     ,							)

						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,							)

						lowerCAmelCase__         =    renew_attention_paths(UpperCamelCase_     ,							in_mid=UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,							)

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"decoder.up.{i}.upsample.conv.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[
																		    F"decoder.up.{i}.upsample.conv.bias"
																		]

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

						lowerCAmelCase__         =    {k.replace('mid_new_2'     ,							'mid_block'		): v for k, v in new_checkpoint.items()}
						lowerCAmelCase__         =    checkpoint['quant_conv.weight']
						lowerCAmelCase__         =    checkpoint['quant_conv.bias']
						if "quantize.embedding.weight" in checkpoint:
												lowerCAmelCase__         =    checkpoint['quantize.embedding.weight']
						lowerCAmelCase__         =    checkpoint['post_quant_conv.weight']
						lowerCAmelCase__         =    checkpoint['post_quant_conv.bias']

						return new_checkpoint


if __name__ == "__main__":
					__snake_case     :     Tuple       =  argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)

					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the architecture.""",
					)

					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

					__snake_case     :     str       =  parser.parse_args()
					__snake_case     :     Any       =  torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__snake_case     :     Optional[int]       =  json.loads(f.read())

					# unet case
					__snake_case     :     Any       =  {key.split(""".""")[0] for key in checkpoint.keys()}
					if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
										__snake_case     :     Optional[int]       =  convert_vq_autoenc_checkpoint(checkpoint, config)
					else:
										__snake_case     :     List[str]       =  convert_ddpm_checkpoint(checkpoint, config)

					if "ddpm" in config:
										del config["ddpm"]

					if config["_class_name"] == "VQModel":
										__snake_case     :     List[str]       =  VQModel(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					elif config["_class_name"] == "AutoencoderKL":
										__snake_case     :     List[Any]       =  AutoencoderKL(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					else:
										__snake_case     :     Any       =  UNetaDModel(**config)
										model.load_state_dict(converted_checkpoint)

										__snake_case     :     List[str]       =  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										__snake_case     :     Tuple       =  DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)





