




"""simple docstring"""

import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


a         =      logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')





def 						_snake_case					( _snake_case							: np.ndarray    ,  _snake_case							: float    ,  _snake_case							: int = 1_60_00							)   -> Union[str, Any]:






      '''simple docstring'''


      _A        =       int(round(sample_rate * max_length							)							)
      if len(_a							) <= sample_length:
            return wav
      _A        =       randint(0    ,  len(_a							) - sample_length - 1							)
      return wav[random_offset : random_offset + sample_length]





@dataclass
class     lowercase_	:



 '''simple docstring'''
 UpperCAmelCase   :					Optional[str]				  =     field(default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''Name of a dataset from the datasets package'''}							)
 UpperCAmelCase   :					Optional[str]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}							)
 UpperCAmelCase   :					Optional[str]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''A file containing the training audio paths and labels.'''}							)
 UpperCAmelCase   :					Optional[str]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''A file containing the validation audio paths and labels.'''}							)
 UpperCAmelCase   :					str				  =     field(
     default='''train'''      ,     metadata={
         '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
     }      ,     )
 UpperCAmelCase   :					str				  =     field(
     default='''validation'''      ,     metadata={
         '''help''': (
             '''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
         )
     }      ,     )
 UpperCAmelCase   :					str				  =     field(
     default='''audio'''      ,     metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''}      ,     )
 UpperCAmelCase   :					str				  =     field(
     default='''label'''      ,     metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''}							)
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     }      ,     )
 UpperCAmelCase   :					float				  =     field(
     default=20      ,     metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''}      ,     )





@dataclass
class     lowercase_	:



 '''simple docstring'''
 UpperCAmelCase   :					str				  =     field(
     default='''facebook/wav2vec2-base'''      ,     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      ,     )
 UpperCAmelCase   :					Optional[str]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
 UpperCAmelCase   :					Optional[str]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''}							)
 UpperCAmelCase   :					str				  =     field(
     default='''main'''      ,     metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}      ,     )
 UpperCAmelCase   :					Optional[str]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''Name or path of preprocessor config.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[bool]				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=SCREAMING_SNAKE_CASE__      ,     metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}      ,     )

 def       lowerCAmelCase_							(			self	:	str		):
       if not self.freeze_feature_extractor and self.freeze_feature_encoder:
             warnings.warn(
                 'The argument `--freeze_feature_extractor` is deprecated and '
                 'will be removed in a future version. Use `--freeze_feature_encoder`'
                 'instead. Setting `freeze_feature_encoder==True`.'   ,							_SCREAMING_SNAKE_CASE   ,							)
       if self.freeze_feature_extractor and not self.freeze_feature_encoder:
             raise ValueError(
                 'The argument `--freeze_feature_extractor` is deprecated and '
                 'should not be used in combination with `--freeze_feature_encoder`.'
                 'Only make use of `--freeze_feature_encoder`.'		)





def 						_snake_case					( )   -> int:






      '''simple docstring'''


      _A        =       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.
            _A        =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
      else:
            _A        =       parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry('run_audio_classification'    ,  _a    ,  _a							)

      # 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							)]    ,  )

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      _A        =       training_args.get_process_log_level()
      logger.setLevel(_a							)
      transformers.utils.logging.set_verbosity(_a							)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # 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}'''							)
      logger.info(F'''Training/evaluation parameters {training_args}'''							)

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

      # Detecting last checkpoint.
      _A        =       None
      if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
            _A        =       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 train from scratch.'							)
            elif last_checkpoint is not None and training_args.resume_from_checkpoint is 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.'							)

    # Initialize our dataset and prepare it for the audio classification task.
      _A        =       DatasetDict()
      _A        =       load_dataset(
          data_args.dataset_name    ,  data_args.dataset_config_name    ,  split=data_args.train_split_name    ,  use_auth_token=True if model_args.use_auth_token else None    ,  )
      _A        =       load_dataset(
          data_args.dataset_name    ,  data_args.dataset_config_name    ,  split=data_args.eval_split_name    ,  use_auth_token=True if model_args.use_auth_token else None    ,  )

      if data_args.audio_column_name not in raw_datasets["train"].column_names:
            raise ValueError(
                F'''--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
                'Make sure to set `--audio_column_name` to the correct audio column - one of '
                F'''{", ".join(raw_datasets["train"].column_names							)}.'''							)

      if data_args.label_column_name not in raw_datasets["train"].column_names:
            raise ValueError(
                F'''--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
                'Make sure to set `--label_column_name` to the correct text column - one of '
                F'''{", ".join(raw_datasets["train"].column_names							)}.'''							)

      # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
      # transformer outputs in the classifier, but it doesn't always lead to better accuracy
      _A        =       AutoFeatureExtractor.from_pretrained(
          model_args.feature_extractor_name or model_args.model_name_or_path    ,  return_attention_mask=model_args.attention_mask    ,  cache_dir=model_args.cache_dir    ,  revision=model_args.model_revision    ,  use_auth_token=True if model_args.use_auth_token else None    ,  )

      # `datasets` takes care of automatically loading and resampling the audio,
      # so we just need to set the correct target sampling rate.
      _A        =       raw_datasets.cast_column(
          data_args.audio_column_name    ,  datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate							)							)

      _A        =       feature_extractor.model_input_names[0]

      def train_transforms(_snake_case							: Optional[Any]							):
            _A        =       []
            for audio in batch[data_args.audio_column_name]:
                  _A        =       random_subsample(
                      audio['array']    ,  max_length=data_args.max_length_seconds    ,  sample_rate=feature_extractor.sampling_rate							)
                  subsampled_wavs.append(_a							)
            _A        =       feature_extractor(_a    ,  sampling_rate=feature_extractor.sampling_rate							)
            _A        =       {model_input_name: inputs.get(_a							)}
            _A        =       list(batch[data_args.label_column_name]							)

            return output_batch

      def val_transforms(_snake_case							: str							):
            _A        =       [audio["array"] for audio in batch[data_args.audio_column_name]]
            _A        =       feature_extractor(_a    ,  sampling_rate=feature_extractor.sampling_rate							)
            _A        =       {model_input_name: inputs.get(_a							)}
            _A        =       list(batch[data_args.label_column_name]							)

            return output_batch

      # Prepare label mappings.
      # We'll include these in the model's config to get human readable labels in the Inference API.
      _A        =       raw_datasets["train"].features[data_args.label_column_name].names
      _A        =       {}, {}
      for i, label in enumerate(_a							):
            _A        =       str(_a							)
            _A        =       label

      # Load the accuracy metric from the datasets package
      _A        =       evaluate.load('accuracy'							)

      # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
      # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
      def compute_metrics(_snake_case							: List[Any]							):
            _A        =       np.argmax(eval_pred.predictions    ,  axis=1							)
            return metric.compute(predictions=_a    ,  references=eval_pred.label_ids							)

      _A        =       AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path    ,  num_labels=len(_a							)    ,  labelaid=_a    ,  idalabel=_a    ,  finetuning_task='audio-classification'    ,  cache_dir=model_args.cache_dir    ,  revision=model_args.model_revision    ,  use_auth_token=True if model_args.use_auth_token else None    ,  )
      _A        =       AutoModelForAudioClassification.from_pretrained(
          model_args.model_name_or_path    ,  from_tf=bool('.ckpt' in model_args.model_name_or_path							)    ,  config=_a    ,  cache_dir=model_args.cache_dir    ,  revision=model_args.model_revision    ,  use_auth_token=True if model_args.use_auth_token else None    ,  ignore_mismatched_sizes=model_args.ignore_mismatched_sizes    ,  )

      # freeze the convolutional waveform encoder
      if model_args.freeze_feature_encoder:
            model.freeze_feature_encoder()

      if training_args.do_train:
            if data_args.max_train_samples is not None:
                  _A        =       (
                      raw_datasets["train"].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
                  )
            # Set the training transforms
            raw_datasets["train"].set_transform(_a    ,  output_all_columns=_a							)

      if training_args.do_eval:
            if data_args.max_eval_samples is not None:
                  _A        =       (
                      raw_datasets["eval"].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
                  )
            # Set the validation transforms
            raw_datasets["eval"].set_transform(_a    ,  output_all_columns=_a							)

      # Initialize our trainer
      _A        =       Trainer(
          model=_a    ,  args=_a    ,  train_dataset=raw_datasets['train'] if training_args.do_train else None    ,  eval_dataset=raw_datasets['eval'] if training_args.do_eval else None    ,  compute_metrics=_a    ,  tokenizer=_a    ,  )

      # Training
      if training_args.do_train:
            _A        =       None
            if training_args.resume_from_checkpoint is not None:
                  _A        =       training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  _A        =       last_checkpoint
            _A        =       trainer.train(resume_from_checkpoint=_a							)
            trainer.save_model()
            trainer.log_metrics('train'    ,  train_result.metrics							)
            trainer.save_metrics('train'    ,  train_result.metrics							)
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            _A        =       trainer.evaluate()
            trainer.log_metrics('eval'    ,  _a							)
            trainer.save_metrics('eval'    ,  _a							)

      # Write model card and (optionally) push to hub
      _A        =       {
          "finetuned_from": model_args.model_name_or_path,
          "tasks": "audio-classification",
          "dataset": data_args.dataset_name,
          "tags": ["audio-classification"],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**_a							)
      else:
            trainer.create_model_card(**_a							)


if __name__ == "__main__":
       main()







"""simple docstring"""

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					( _snake_case							: int							)   -> Any:






      '''simple docstring'''


      random.seed(_snake_case							)
      np.random.seed(_snake_case							)
      torch.manual_seed(_snake_case							)
      torch.cuda.manual_seed_all(_snake_case							)
      # ^^ safe to call this function even if cuda is not available







class     lowercase_	:



 '''simple docstring'''

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

       if isinstance(_UpperCAmelCase   ,							torch.nn.Module		):
             _A        =       (
                 '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'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
             _A        =       parameters.parameters()

             # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
             _A        =       True

       if kwargs.get('max_value'   ,							_UpperCAmelCase		) is not None:
             _A        =       'The `max_value` argument is deprecated. Please use `decay` instead.'
             deprecate('max_value'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
             _A        =       kwargs['max_value']

       if kwargs.get('min_value'   ,							_UpperCAmelCase		) is not None:
             _A        =       'The `min_value` argument is deprecated. Please use `min_decay` instead.'
             deprecate('min_value'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
             _A        =       kwargs['min_value']

       _A        =       list(_UpperCAmelCase		)
       _A        =       [p.clone().detach() for p in parameters]

       if kwargs.get('device'   ,							_UpperCAmelCase		) is not None:
             _A        =       'The `device` argument is deprecated. Please use `to` instead.'
             deprecate('device'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
             self.to(device=kwargs['device']		)

       _A        =       None

       _A        =       decay
       _A        =       min_decay
       _A        =       update_after_step
       _A        =       use_ema_warmup
       _A        =       inv_gamma
       _A        =       power
       _A        =       0
       _A        =       None  # set in `step()`

       _A        =       model_cls
       _A        =       model_config

 @classmethod
 def       lowerCAmelCase_							(			cls	:	Any   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[int]		):
       _A       ,   _A        =       model_cls.load_config(_UpperCAmelCase   ,							return_unused_kwargs=_UpperCAmelCase		)
       _A        =       model_cls.from_pretrained(_UpperCAmelCase		)

       _A        =       cls(model.parameters()   ,							model_cls=_UpperCAmelCase   ,							model_config=model.config		)

       ema_model.load_state_dict(_UpperCAmelCase		)
       return ema_model

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Tuple		):
       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__.'		)

       _A        =       self.model_cls.from_config(self.model_config		)
       _A        =       self.state_dict()
       state_dict.pop('shadow_params'   ,							_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	int		):
       _A        =       max(0   ,							optimization_step - self.update_after_step - 1		)

       if step <= 0:
             return 0.0

       if self.use_ema_warmup:
             _A        =       1 - (1 + step / self.inv_gamma) ** -self.power
       else:
             _A        =       (1 + step) / (10 + step)

       _A        =       min(_UpperCAmelCase   ,							self.decay		)
       # make sure decay is not smaller than min_decay
       _A        =       max(_UpperCAmelCase   ,							self.min_decay		)
       return cur_decay_value

 @torch.no_grad()
 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       if isinstance(_UpperCAmelCase   ,							torch.nn.Module		):
             _A        =       (
                 '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'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
             _A        =       parameters.parameters()

       _A        =       list(_UpperCAmelCase		)

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       _A        =       self.get_decay(self.optimization_step		)
       _A        =       decay
       _A        =       1 - decay

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

       for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
             if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   _A        =       deepspeed.zero.GatheredParameters(_UpperCAmelCase   ,							modifier_rank=_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       _A        =       list(_UpperCAmelCase		)
       for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
             param.data.copy_(s_param.to(param.device		).data		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	Dict=None		):
       _A        =       [
           p.to(device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		) if p.is_floating_point() else p.to(device=_UpperCAmelCase		)
           for p in self.shadow_params
       ]

 def       lowerCAmelCase_							(			self	:	Dict		):
       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       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       _A        =       [param.detach().cpu().clone() for param in parameters]

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       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   ,							_UpperCAmelCase		):
             param.data.copy_(c_param.data		)

       # Better memory-wise.
       _A        =       None



 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	dict		):
       _A        =       copy.deepcopy(_UpperCAmelCase		)

       _A        =       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'		)

       _A        =       state_dict.get('min_decay'   ,							self.min_decay		)
       if not isinstance(self.min_decay   ,							_UpperCAmelCase		):
             raise ValueError('Invalid min_decay'		)

       _A        =       state_dict.get('optimization_step'   ,							self.optimization_step		)
       if not isinstance(self.optimization_step   ,							_UpperCAmelCase		):
             raise ValueError('Invalid optimization_step'		)

       _A        =       state_dict.get('update_after_step'   ,							self.update_after_step		)
       if not isinstance(self.update_after_step   ,							_UpperCAmelCase		):
             raise ValueError('Invalid update_after_step'		)

       _A        =       state_dict.get('use_ema_warmup'   ,							self.use_ema_warmup		)
       if not isinstance(self.use_ema_warmup   ,							_UpperCAmelCase		):
             raise ValueError('Invalid use_ema_warmup'		)

       _A        =       state_dict.get('inv_gamma'   ,							self.inv_gamma		)
       if not isinstance(self.inv_gamma   ,							(float, int)		):
             raise ValueError('Invalid inv_gamma'		)

       _A        =       state_dict.get('power'   ,							self.power		)
       if not isinstance(self.power   ,							(float, int)		):
             raise ValueError('Invalid power'		)

       _A        =       state_dict.get('shadow_params'   ,							_UpperCAmelCase		)
       if shadow_params is not None:
             _A        =       shadow_params
             if not isinstance(self.shadow_params   ,							_UpperCAmelCase		):
                   raise ValueError('shadow_params must be a list'		)
             if not all(isinstance(_UpperCAmelCase   ,							torch.Tensor		) for p in self.shadow_params		):
                   raise ValueError('shadow_params must all be Tensors'		)

