


'''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


lowercase__				     =		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 __UpperCamelCase   (			__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: float      ,  __lowerCamelCase		: int = 1_6000				)      ->     Optional[int]:







     '''simple docstring'''






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





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







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






     def  a_					(			self		)       ->    List[Any]:
          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`."     ,							__UpperCamelCase     ,							)
          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 __UpperCamelCase   (			)      ->     Dict:







     '''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			,			_a			,			_a						=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          _a			,			_a			,			_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"      ,  __lowerCamelCase      ,  __lowerCamelCase				)

     # 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(__lowerCamelCase				)
     transformers.utils.logging.set_verbosity(__lowerCamelCase				)
     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(__lowerCamelCase		: Dict				):
          _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(__lowerCamelCase				)
          _a						=		feature_extractor(__lowerCamelCase      ,  sampling_rate=feature_extractor.sampling_rate				)
          _a						=		{model_input_name: inputs.get(__lowerCamelCase				)}
          _a						=		list(batch[data_args.label_column_name]				)

          return output_batch

     def val_transforms(__lowerCamelCase		: List[Any]				):
          _a						=		[audio["array"] for audio in batch[data_args.audio_column_name]]
          _a						=		feature_extractor(__lowerCamelCase      ,  sampling_rate=feature_extractor.sampling_rate				)
          _a						=		{model_input_name: inputs.get(__lowerCamelCase				)}
          _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			,			_a						=		{}, {}
     for i, label in enumerate(__lowerCamelCase				):
          _a						=		str(__lowerCamelCase				)
          _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(__lowerCamelCase		: Dict				):
          _a						=		np.argmax(eval_pred.predictions      ,  axis=1				)
          return metric.compute(predictions=__lowerCamelCase      ,  references=eval_pred.label_ids				)

     _a						=		AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path      ,  num_labels=len(__lowerCamelCase				)      ,  labelaid=__lowerCamelCase      ,  idalabel=__lowerCamelCase      ,  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=__lowerCamelCase      ,  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(__lowerCamelCase      ,  output_all_columns=__lowerCamelCase				)

     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(__lowerCamelCase      ,  output_all_columns=__lowerCamelCase				)

     # Initialize our trainer
     _a						=		Trainer(
         model=__lowerCamelCase      ,  args=__lowerCamelCase      ,  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=__lowerCamelCase      ,  tokenizer=__lowerCamelCase      ,  )

     # 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=__lowerCamelCase				)
          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"      ,  __lowerCamelCase				)
          trainer.save_metrics("eval"      ,  __lowerCamelCase				)

     # 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(**__lowerCamelCase				)
     else:
          trainer.create_model_card(**__lowerCamelCase				)


if __name__ == "__main__":
   main()





'''simple docstring'''

import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				jnp.floataa
     UpperCAmelCase 					=				True






     def  a_					(			self		)       ->    int:
          super().setup()
          _a						=		nn.Dense(5     ,							dtype=self.dtype		)






     def __call__(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Optional[Any]:
          _a						=		super().__call__(*__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		self.cls(outputs[2]		)
          return outputs[:2] + (cls_out,)







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				FlaxBigBirdForNaturalQuestionsModule







def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Any      ,  __lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Dict				)      ->     List[Any]:







     '''simple docstring'''






     def cross_entropy(__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: str=None				):
          _a						=		logits.shape[-1]
          _a						=		(labels[..., None] == jnp.arange(__lowerCamelCase				)[None]).astype("f4"				)
          _a						=		jax.nn.log_softmax(__lowerCamelCase      ,  axis=-1				)
          _a						=		-jnp.sum(labels * logits      ,  axis=-1				)
          if reduction is not None:
               _a						=		reduction(__lowerCamelCase				)
          return loss

     _a						=		partial(__lowerCamelCase      ,  reduction=jnp.mean				)
     _a						=		cross_entropy(__lowerCamelCase      ,  __lowerCamelCase				)
     _a						=		cross_entropy(__lowerCamelCase      ,  __lowerCamelCase				)
     _a						=		cross_entropy(__lowerCamelCase      ,  __lowerCamelCase				)
     return (start_loss + end_loss + pooled_loss) / 3





@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				"google/bigbird-roberta-base"
     UpperCAmelCase 					=				3_000
     UpperCAmelCase 					=				10_500

     UpperCAmelCase 					=				128
     UpperCAmelCase 					=				3

     UpperCAmelCase 					=				1
     UpperCAmelCase 					=				5

     # tx_args
     UpperCAmelCase 					=				3E-5
     UpperCAmelCase 					=				0.0
     UpperCAmelCase 					=				20_000
     UpperCAmelCase 					=				0.00_95

     UpperCAmelCase 					=				"bigbird-roberta-natural-questions"
     UpperCAmelCase 					=				"training-expt"
     UpperCAmelCase 					=				"data/nq-training.jsonl"
     UpperCAmelCase 					=				"data/nq-validation.jsonl"






     def  a_					(			self		)       ->    Tuple:
          os.makedirs(self.base_dir     ,							exist_ok=__UpperCamelCase		)
          _a						=		os.path.join(self.base_dir     ,							self.save_dir		)
          _a						=		self.batch_size_per_device * jax.device_count()







@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				4_096  # no dynamic padding on TPUs






     def __call__(			self     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		self.collate_fn(__UpperCamelCase		)
          _a						=		jax.tree_util.tree_map(__UpperCamelCase     ,							__UpperCamelCase		)
          return batch






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Tuple:
          _a			,			_a						=		self.fetch_inputs(features["input_ids"]		)
          _a						=		{
              "input_ids": jnp.array(__UpperCamelCase     ,							dtype=jnp.intaa		),
              "attention_mask": jnp.array(__UpperCamelCase     ,							dtype=jnp.intaa		),
              "start_labels": jnp.array(features["start_token"]     ,							dtype=jnp.intaa		),
              "end_labels": jnp.array(features["end_token"]     ,							dtype=jnp.intaa		),
              "pooled_labels": jnp.array(features["category"]     ,							dtype=jnp.intaa		),
          }
          return batch






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		[self._fetch_inputs(__UpperCamelCase		) for ids in input_ids]
          return zip(*__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		[1 for _ in range(len(__UpperCamelCase		)		)]
          while len(__UpperCamelCase		) < self.max_length:
               input_ids.append(self.pad_id		)
               attention_mask.append(0		)
          return input_ids, attention_mask







def __UpperCamelCase   (			__lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: List[Any]=None				)      ->     List[Any]:







     '''simple docstring'''






     if seed is not None:
          _a						=		dataset.shuffle(seed=__lowerCamelCase				)
     for i in range(len(__lowerCamelCase				) // batch_size				):
          _a						=		dataset[i * batch_size : (i + 1) * batch_size]
          yield dict(__lowerCamelCase				)






@partial(jax.pmap      ,  axis_name="batch"				)
def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: int      ,  **__lowerCamelCase		: Optional[int]				)      ->     Any:







     '''simple docstring'''






     def loss_fn(__lowerCamelCase		: str				):
          _a						=		model_inputs.pop("start_labels"				)
          _a						=		model_inputs.pop("end_labels"				)
          _a						=		model_inputs.pop("pooled_labels"				)

          _a						=		state.apply_fn(**__lowerCamelCase      ,  params=__lowerCamelCase      ,  dropout_rng=__lowerCamelCase      ,  train=__lowerCamelCase				)
          _a			,			_a			,			_a						=		outputs

          return state.loss_fn(
              __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  )

     _a			,			_a						=		jax.random.split(__lowerCamelCase				)
     _a						=		jax.value_and_grad(__lowerCamelCase				)
     _a			,			_a						=		grad_fn(state.params				)
     _a						=		jax.lax.pmean({"loss": loss}      ,  axis_name="batch"				)
     _a						=		jax.lax.pmean(__lowerCamelCase      ,  "batch"				)

     _a						=		state.apply_gradients(grads=__lowerCamelCase				)
     return state, metrics, new_drp_rng






@partial(jax.pmap      ,  axis_name="batch"				)
def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  **__lowerCamelCase		: Tuple				)      ->     List[Any]:







     '''simple docstring'''






     _a						=		model_inputs.pop("start_labels"				)
     _a						=		model_inputs.pop("end_labels"				)
     _a						=		model_inputs.pop("pooled_labels"				)

     _a						=		state.apply_fn(**__lowerCamelCase      ,  params=state.params      ,  train=__lowerCamelCase				)
     _a			,			_a			,			_a						=		outputs

     _a						=		state.loss_fn(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
     _a						=		jax.lax.pmean({"loss": loss}      ,  axis_name="batch"				)
     return metrics





class 						__SCREAMING_SNAKE_CASE					(      train_state.TrainState ):
     UpperCAmelCase 					=				struct.field(pytree_node=lowerCamelCase__ )







@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				None






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=None		)       ->    Union[str, Any]:
          _a						=		model.params
          _a						=		TrainState.create(
              apply_fn=model.__call__     ,							params=__UpperCamelCase     ,							tx=__UpperCamelCase     ,							loss_fn=__UpperCamelCase     ,							)
          if ckpt_dir is not None:
               _a			,			_a			,			_a			,			_a			,			_a						=		restore_checkpoint(__UpperCamelCase     ,							__UpperCamelCase		)
               _a						=		{
                   "lr": args.lr,
                   "init_lr": args.init_lr,
                   "warmup_steps": args.warmup_steps,
                   "num_train_steps": num_train_steps,
                   "weight_decay": args.weight_decay,
               }
               _a			,			_a						=		build_tx(**__UpperCamelCase		)
               _a						=		train_state.TrainState(
                   step=__UpperCamelCase     ,							apply_fn=model.__call__     ,							params=__UpperCamelCase     ,							tx=__UpperCamelCase     ,							opt_state=__UpperCamelCase     ,							)
               _a						=		args
               _a						=		data_collator
               _a						=		lr
               _a						=		params
          _a						=		jax_utils.replicate(__UpperCamelCase		)
          return state






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    str:
          _a						=		self.args
          _a						=		len(__UpperCamelCase		) // args.batch_size

          _a						=		jax.random.PRNGKey(0		)
          _a						=		jax.random.split(__UpperCamelCase     ,							jax.device_count()		)
          for epoch in range(args.max_epochs		):
               _a						=		jnp.array(0     ,							dtype=jnp.floataa		)
               _a						=		get_batched_dataset(__UpperCamelCase     ,							args.batch_size     ,							seed=__UpperCamelCase		)
               _a						=		0
               for batch in tqdm(__UpperCamelCase     ,							total=__UpperCamelCase     ,							desc=f"Running EPOCH-{epoch}"		):
                    _a						=		self.data_collator(__UpperCamelCase		)
                    _a			,			_a			,			_a						=		self.train_step_fn(__UpperCamelCase     ,							__UpperCamelCase     ,							**__UpperCamelCase		)
                    running_loss += jax_utils.unreplicate(metrics["loss"]		)
                    i += 1
                    if i % args.logging_steps == 0:
                         _a						=		jax_utils.unreplicate(state.step		)
                         _a						=		running_loss.item() / i
                         _a						=		self.scheduler_fn(state_step - 1		)

                         _a						=		self.evaluate(__UpperCamelCase     ,							__UpperCamelCase		)
                         _a						=		{
                             "step": state_step.item(),
                             "eval_loss": eval_loss.item(),
                             "tr_loss": tr_loss,
                             "lr": lr.item(),
                         }
                         tqdm.write(str(__UpperCamelCase		)		)
                         self.logger.log(__UpperCamelCase     ,							commit=__UpperCamelCase		)

                    if i % args.save_steps == 0:
                         self.save_checkpoint(args.save_dir + f"-e{epoch}-s{i}"     ,							state=__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[Any]:
          _a						=		get_batched_dataset(__UpperCamelCase     ,							self.args.batch_size		)
          _a						=		len(__UpperCamelCase		) // self.args.batch_size
          _a						=		jnp.array(0     ,							dtype=jnp.floataa		)
          _a						=		0
          for batch in tqdm(__UpperCamelCase     ,							total=__UpperCamelCase     ,							desc="Evaluating ... "		):
               _a						=		self.data_collator(__UpperCamelCase		)
               _a						=		self.val_step_fn(__UpperCamelCase     ,							**__UpperCamelCase		)
               running_loss += jax_utils.unreplicate(metrics["loss"]		)
               i += 1
          return running_loss / i






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		jax_utils.unreplicate(__UpperCamelCase		)
          print(f"SAVING CHECKPOINT IN {save_dir}"     ,							end=" ... "		)
          self.model_save_fn(__UpperCamelCase     ,							params=state.params		)
          with open(os.path.join(__UpperCamelCase     ,							"opt_state.msgpack"		)     ,							"wb"		) as f:
               f.write(to_bytes(state.opt_state		)		)
          joblib.dump(self.args     ,							os.path.join(__UpperCamelCase     ,							"args.joblib"		)		)
          joblib.dump(self.data_collator     ,							os.path.join(__UpperCamelCase     ,							"data_collator.joblib"		)		)
          with open(os.path.join(__UpperCamelCase     ,							"training_state.json"		)     ,							"w"		) as f:
               json.dump({"step": state.step.item()}     ,							__UpperCamelCase		)
          print("DONE"		)







def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Dict				)      ->     Optional[int]:







     '''simple docstring'''






     print(F"RESTORING CHECKPOINT FROM {save_dir}"      ,  end=" ... "				)
     with open(os.path.join(__lowerCamelCase      ,  "flax_model.msgpack"				)      ,  "rb"				) as f:
          _a						=		from_bytes(state.params      ,  f.read()				)

     with open(os.path.join(__lowerCamelCase      ,  "opt_state.msgpack"				)      ,  "rb"				) as f:
          _a						=		from_bytes(state.opt_state      ,  f.read()				)

     _a						=		joblib.load(os.path.join(__lowerCamelCase      ,  "args.joblib"				)				)
     _a						=		joblib.load(os.path.join(__lowerCamelCase      ,  "data_collator.joblib"				)				)

     with open(os.path.join(__lowerCamelCase      ,  "training_state.json"				)      ,  "r"				) as f:
          _a						=		json.load(__lowerCamelCase				)
     _a						=		training_state["step"]

     print("DONE"				)
     return params, opt_state, step, args, data_collator






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: List[str]				)      ->     List[str]:







     '''simple docstring'''






     _a						=		num_train_steps - warmup_steps
     _a						=		optax.linear_schedule(init_value=__lowerCamelCase      ,  end_value=__lowerCamelCase      ,  transition_steps=__lowerCamelCase				)
     _a						=		optax.linear_schedule(init_value=__lowerCamelCase      ,  end_value=1E-7      ,  transition_steps=__lowerCamelCase				)
     _a						=		optax.join_schedules(schedules=[warmup_fn, decay_fn]      ,  boundaries=[warmup_steps]				)
     return lr






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Tuple				)      ->     Any:







     '''simple docstring'''






     def weight_decay_mask(__lowerCamelCase		: Any				):
          _a						=		traverse_util.flatten_dict(__lowerCamelCase				)
          _a						=		{k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
          return traverse_util.unflatten_dict(__lowerCamelCase				)

     _a						=		scheduler_fn(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)

     _a						=		optax.adamw(learning_rate=__lowerCamelCase      ,  weight_decay=__lowerCamelCase      ,  mask=__lowerCamelCase				)
     return tx, lr

