

"""simple docstring"""




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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__UpperCAmelCase								=			logging.getLogger(__name__)



@dataclass
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      str										=field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
  lowerCamelCase   :      Optional[str]										=field(
      default=A__       ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
  lowerCamelCase   :      Optional[str]										=field(
      default=A__       ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
  lowerCamelCase   :      Optional[str]										=field(
      default=A__       ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,							)
  lowerCamelCase   :      bool										=field(default=A__       ,							metadata={"help": "Whether tp freeze the encoder."}			)
  lowerCamelCase   :      bool										=field(default=A__       ,							metadata={"help": "Whether  to freeze the embeddings."}			)






@dataclass
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      str										=field(
      metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}			)
  lowerCamelCase   :      Optional[str]										=field(
      default="summarization"       ,							metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}       ,							)
  lowerCamelCase   :      Optional[int]										=field(
      default=1024       ,							metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }       ,							)
  lowerCamelCase   :      Optional[int]										=field(
      default=128       ,							metadata={
          "help": (
              "The maximum total sequence length for target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }       ,							)
  lowerCamelCase   :      Optional[int]										=field(
      default=142       ,							metadata={
          "help": (
              "The maximum total sequence length for validation target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded. "
              "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
              "during ``evaluate`` and ``predict``."
          )
      }       ,							)
  lowerCamelCase   :      Optional[int]										=field(
      default=142       ,							metadata={
          "help": (
              "The maximum total sequence length for test target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }       ,							)
  lowerCamelCase   :      Optional[int]										=field(default=-1       ,							metadata={"help": "# training examples. -1 means use all."}			)
  lowerCamelCase   :      Optional[int]										=field(default=-1       ,							metadata={"help": "# validation examples. -1 means use all."}			)
  lowerCamelCase   :      Optional[int]										=field(default=-1       ,							metadata={"help": "# test examples. -1 means use all."}			)
  lowerCamelCase   :      Optional[str]										=field(default=A__       ,							metadata={"help": "Source language id for translation."}			)
  lowerCamelCase   :      Optional[str]										=field(default=A__       ,							metadata={"help": "Target language id for translation."}			)
  lowerCamelCase   :      Optional[int]										=field(default=A__       ,							metadata={"help": "# num_beams to use for evaluation."}			)
  lowerCamelCase   :      bool										=field(
      default=A__       ,							metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}       ,							)






def      snake_case_				(__A					:						Optional[Any] ,       __A					:						Optional[int] ,       __A					:						Optional[Any]					)      ->     Optional[int]:
   logger.info(f'''***** {split} metrics *****'''					)
   for key in sorted(metrics.keys()					):
      logger.info(f'''  {key} = {metrics[key]}'''					)
   save_json(lowercase__ ,       os.path.join(lowercase__ ,       f'''{split}_results.json'''					)					)





def      snake_case_				()      ->     Tuple:
   __lowerCAmelCase  :      Tuple    =						HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)					)

   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.
      __lowerCAmelCase  :      Optional[int]    =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
   else:
      __lowerCAmelCase  :      Any    =						parser.parse_args_into_dataclasses()

   check_output_dir(lowercase__					)

   # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,       datefmt="""%m/%d/%Y %H:%M:%S""" ,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,       )
   logger.warning(
       """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" ,       training_args.local_rank ,       training_args.device ,       training_args.n_gpu ,       bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED					) ,       training_args.fpaa ,       )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # 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()
   logger.info("""Training/evaluation parameters %s""" ,       lowercase__					)

   # Set seed
   set_seed(training_args.seed					)

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   __lowerCAmelCase  :      Union[str, Any]    =						AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path ,       cache_dir=model_args.cache_dir ,       )

   __lowerCAmelCase  :      Union[str, Any]    =						("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
   for p in extra_model_params:
      if getattr(lowercase__ ,       lowercase__ ,       lowercase__					):
         assert hasattr(lowercase__ ,       lowercase__					), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
         setattr(lowercase__ ,       lowercase__ ,       getattr(lowercase__ ,       lowercase__					)					)

   __lowerCAmelCase  :      Tuple    =						AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,       cache_dir=model_args.cache_dir ,       )
   __lowerCAmelCase  :      Tuple    =						AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path ,       from_tf=""".ckpt""" in model_args.model_name_or_path ,       config=lowercase__ ,       cache_dir=model_args.cache_dir ,       )

   # use task specific params
   use_task_specific_params(lowercase__ ,       data_args.task					)

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      __lowerCAmelCase  :      Dict    =						model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(lowercase__ ,       (MBartTokenizer, MBartTokenizerFast)					):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(lowercase__ ,       lowercase__					):
         __lowerCAmelCase  :      List[str]    =						tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         __lowerCAmelCase  :      List[str]    =						tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

   if model_args.freeze_embeds:
      freeze_embeds(lowercase__					)
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()					)
      assert_all_frozen(model.get_encoder()					)

   __lowerCAmelCase  :      Dict    =						SeqaSeqDataset

   # Get datasets
   __lowerCAmelCase  :      Tuple    =						(
       dataset_class(
           lowercase__ ,       type_path="""train""" ,       data_dir=data_args.data_dir ,       n_obs=data_args.n_train ,       max_target_length=data_args.max_target_length ,       max_source_length=data_args.max_source_length ,       prefix=model.config.prefix or """""" ,       )
       if training_args.do_train
       else None
   )
   __lowerCAmelCase  :      List[str]    =						(
       dataset_class(
           lowercase__ ,       type_path="""val""" ,       data_dir=data_args.data_dir ,       n_obs=data_args.n_val ,       max_target_length=data_args.val_max_target_length ,       max_source_length=data_args.max_source_length ,       prefix=model.config.prefix or """""" ,       )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   __lowerCAmelCase  :      Any    =						(
       dataset_class(
           lowercase__ ,       type_path="""test""" ,       data_dir=data_args.data_dir ,       n_obs=data_args.n_test ,       max_target_length=data_args.test_max_target_length ,       max_source_length=data_args.max_source_length ,       prefix=model.config.prefix or """""" ,       )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   __lowerCAmelCase  :      Any    =						(
       build_compute_metrics_fn(data_args.task ,       lowercase__					) if training_args.predict_with_generate else None
   )
   __lowerCAmelCase  :      Tuple    =						SeqaSeqTrainer(
       model=lowercase__ ,       args=lowercase__ ,       data_args=lowercase__ ,       train_dataset=lowercase__ ,       eval_dataset=lowercase__ ,       data_collator=SeqaSeqDataCollator(
           lowercase__ ,       lowercase__ ,       model.config.decoder_start_token_id ,       training_args.tpu_num_cores					) ,       compute_metrics=lowercase__ ,       tokenizer=lowercase__ ,       )

   __lowerCAmelCase  :      Union[str, Any]    =						{}
   # Training
   if training_args.do_train:
      logger.info("""*** Train ***"""					)

      __lowerCAmelCase  :      Any    =						trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
      __lowerCAmelCase  :      Any    =						train_result.metrics
      __lowerCAmelCase  :      Union[str, Any]    =						data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("""train""" ,       lowercase__ ,       training_args.output_dir					)
         all_metrics.update(lowercase__					)

         # 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"""					)					)

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""					)

      __lowerCAmelCase  :      Any    =						trainer.evaluate(metric_key_prefix="""val"""					)
      __lowerCAmelCase  :      List[Any]    =						data_args.n_val
      __lowerCAmelCase  :      Tuple    =						round(metrics["""val_loss"""] ,       4					)

      if trainer.is_world_process_zero():
         handle_metrics("""val""" ,       lowercase__ ,       training_args.output_dir					)
         all_metrics.update(lowercase__					)

   if training_args.do_predict:
      logger.info("""*** Predict ***"""					)

      __lowerCAmelCase  :      Optional[int]    =						trainer.predict(test_dataset=lowercase__ ,       metric_key_prefix="""test"""					)
      __lowerCAmelCase  :      Union[str, Any]    =						test_output.metrics
      __lowerCAmelCase  :      Dict    =						data_args.n_test

      if trainer.is_world_process_zero():
         __lowerCAmelCase  :      List[str]    =						round(metrics["""test_loss"""] ,       4					)
         handle_metrics("""test""" ,       lowercase__ ,       training_args.output_dir					)
         all_metrics.update(lowercase__					)

         if training_args.predict_with_generate:
            __lowerCAmelCase  :      Union[str, Any]    =						tokenizer.batch_decode(
                test_output.predictions ,       skip_special_tokens=lowercase__ ,       clean_up_tokenization_spaces=lowercase__					)
            __lowerCAmelCase  :      str    =						lmap(str.strip ,       lowercase__					)
            write_txt_file(lowercase__ ,       os.path.join(training_args.output_dir ,       """test_generations.txt"""					)					)

   if trainer.is_world_process_zero():
      save_json(lowercase__ ,       os.path.join(training_args.output_dir ,       """all_results.json"""					)					)

   return all_metrics





def      snake_case_				(__A					:						Tuple					)      ->     Dict:
   main()


if __name__ == "__main__":
   main()





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
   import transformers





def      snake_case_				(__A					:						int					)      ->     List[Any]:
   random.seed(__A					)
   np.random.seed(__A					)
   torch.manual_seed(__A					)
   torch.cuda.manual_seed_all(__A					)
   # ^^ safe to call this function even if cuda is not available



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]	,							lowerCAmelCase			:   float = 0.9999	,							lowerCAmelCase			:   float = 0.0	,							lowerCAmelCase			:   int = 0	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Union[float, int] = 1.0	,							lowerCAmelCase			:   Union[float, int] = 2 / 3	,							lowerCAmelCase			:   Optional[Any] = None	,							lowerCAmelCase			:   Dict[str, Any] = None	,							**lowerCAmelCase			:   Any	,							)							->     List[str]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      Optional[int]    =						(
            """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
            """Please pass the parameters of the module instead."""
        )
        deprecate(
            """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase	,							)
        __lowerCAmelCase  :      int    =						parameters.parameters()

        # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
        __lowerCAmelCase  :      Optional[Any]    =						True

     if kwargs.get("""max_value"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[Any]    =						"""The `max_value` argument is deprecated. Please use `decay` instead."""
        deprecate("""max_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						kwargs["""max_value"""]

     if kwargs.get("""min_value"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[int]    =						"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
        deprecate("""min_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						kwargs["""min_value"""]

     __lowerCAmelCase  :      Optional[Any]    =						list(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						[p.clone().detach() for p in parameters]

     if kwargs.get("""device"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      str    =						"""The `device` argument is deprecated. Please use `to` instead."""
        deprecate("""device"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        self.to(device=kwargs["""device"""]   )

     __lowerCAmelCase  :      List[Any]    =						None

     __lowerCAmelCase  :      Any    =						decay
     __lowerCAmelCase  :      Any    =						min_decay
     __lowerCAmelCase  :      List[Any]    =						update_after_step
     __lowerCAmelCase  :      Optional[Any]    =						use_ema_warmup
     __lowerCAmelCase  :      Union[str, Any]    =						inv_gamma
     __lowerCAmelCase  :      Union[str, Any]    =						power
     __lowerCAmelCase  :      Optional[Any]    =						0
     __lowerCAmelCase  :      str    =						None  # set in `step()`

     __lowerCAmelCase  :      int    =						model_cls
     __lowerCAmelCase  :      List[Any]    =						model_config







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Optional[int]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict   )							->     "EMAModel":






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[Any]    =						model_cls.load_config(lowerCAmelCase	,							return_unused_kwargs=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						model_cls.from_pretrained(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						cls(model.parameters()	,							model_cls=lowerCAmelCase	,							model_config=model.config   )

     ema_model.load_state_dict(lowerCAmelCase   )
     return ema_model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]   )							->     int:






     """simple docstring"""







     if self.model_cls is None:
        raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""   )

     if self.model_config is None:
        raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""   )

     __lowerCAmelCase  :      Dict    =						self.model_cls.from_config(self.model_config   )
     __lowerCAmelCase  :      Optional[Any]    =						self.state_dict()
     state_dict.pop("""shadow_params"""	,							lowerCAmelCase   )

     model.register_to_config(**lowerCAmelCase   )
     self.copy_to(model.parameters()   )
     model.save_pretrained(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     float:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						max(0	,							optimization_step - self.update_after_step - 1   )

     if step <= 0:
        return 0.0

     if self.use_ema_warmup:
        __lowerCAmelCase  :      str    =						1 - (1 + step / self.inv_gamma) ** -self.power
     else:
        __lowerCAmelCase  :      Optional[int]    =						(1 + step) / (10 + step)

     __lowerCAmelCase  :      Any    =						min(lowerCAmelCase	,							self.decay   )
     # make sure decay is not smaller than min_decay
     __lowerCAmelCase  :      Union[str, Any]    =						max(lowerCAmelCase	,							self.min_decay   )
     return cur_decay_value







  @torch.no_grad()
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     Optional[int]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      int    =						(
            """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
            """Please pass the parameters of the module instead."""
        )
        deprecate(
            """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase	,							)
        __lowerCAmelCase  :      Any    =						parameters.parameters()

     __lowerCAmelCase  :      Any    =						list(lowerCAmelCase   )

     self.optimization_step += 1

     # Compute the decay factor for the exponential moving average.
     __lowerCAmelCase  :      Optional[Any]    =						self.get_decay(self.optimization_step   )
     __lowerCAmelCase  :      Dict    =						decay
     __lowerCAmelCase  :      Tuple    =						1 - decay

     __lowerCAmelCase  :      str    =						contextlib.nullcontext
     if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
        import deepspeed

     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
           __lowerCAmelCase  :      List[Any]    =						deepspeed.zero.GatheredParameters(lowerCAmelCase	,							modifier_rank=lowerCAmelCase   )

        with context_manager():
           if param.requires_grad:
              s_param.sub_(one_minus_decay * (s_param - param)   )
           else:
              s_param.copy_(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						list(lowerCAmelCase   )
     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        param.data.copy_(s_param.to(param.device   ).data   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   Optional[Any]=None   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[
         p.to(device=lowerCAmelCase	,							dtype=lowerCAmelCase   ) if p.is_floating_point() else p.to(device=lowerCAmelCase   )
         for p in self.shadow_params
     ]







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     dict:






     """simple docstring"""







     return {
         "decay": self.decay,
         "min_decay": self.min_decay,
         "optimization_step": self.optimization_step,
         "update_after_step": self.update_after_step,
         "use_ema_warmup": self.use_ema_warmup,
         "inv_gamma": self.inv_gamma,
         "power": self.power,
         "shadow_params": self.shadow_params,
     }







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						[param.detach().cpu().clone() for param in parameters]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     if self.temp_stored_params is None:
        raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""   )
     for c_param, param in zip(self.temp_stored_params	,							lowerCAmelCase   ):
        param.data.copy_(c_param.data   )

     # Better memory-wise.
     __lowerCAmelCase  :      Union[str, Any]    =						None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   dict   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						copy.deepcopy(lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						state_dict.get("""decay"""	,							self.decay   )
     if self.decay < 0.0 or self.decay > 1.0:
        raise ValueError("""Decay must be between 0 and 1"""   )

     __lowerCAmelCase  :      Tuple    =						state_dict.get("""min_decay"""	,							self.min_decay   )
     if not isinstance(self.min_decay	,							lowerCAmelCase   ):
        raise ValueError("""Invalid min_decay"""   )

     __lowerCAmelCase  :      List[Any]    =						state_dict.get("""optimization_step"""	,							self.optimization_step   )
     if not isinstance(self.optimization_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid optimization_step"""   )

     __lowerCAmelCase  :      Union[str, Any]    =						state_dict.get("""update_after_step"""	,							self.update_after_step   )
     if not isinstance(self.update_after_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid update_after_step"""   )

     __lowerCAmelCase  :      List[str]    =						state_dict.get("""use_ema_warmup"""	,							self.use_ema_warmup   )
     if not isinstance(self.use_ema_warmup	,							lowerCAmelCase   ):
        raise ValueError("""Invalid use_ema_warmup"""   )

     __lowerCAmelCase  :      Optional[int]    =						state_dict.get("""inv_gamma"""	,							self.inv_gamma   )
     if not isinstance(self.inv_gamma	,							(float, int)   ):
        raise ValueError("""Invalid inv_gamma"""   )

     __lowerCAmelCase  :      Any    =						state_dict.get("""power"""	,							self.power   )
     if not isinstance(self.power	,							(float, int)   ):
        raise ValueError("""Invalid power"""   )

     __lowerCAmelCase  :      Optional[Any]    =						state_dict.get("""shadow_params"""	,							lowerCAmelCase   )
     if shadow_params is not None:
        __lowerCAmelCase  :      int    =						shadow_params
        if not isinstance(self.shadow_params	,							lowerCAmelCase   ):
           raise ValueError("""shadow_params must be a list"""   )
        if not all(isinstance(lowerCAmelCase	,							torch.Tensor   ) for p in self.shadow_params   ):
           raise ValueError("""shadow_params must all be Tensors"""   )


