"""simple docstring"""
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowercase__					       =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->int:
  if "token" in model_name_or_path:
    return "rag_token"
  if "sequence" in model_name_or_path:
    return "rag_sequence"
  if "bart" in model_name_or_path:
    return "bart"
  return None







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[int]:
  return max(metric_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) for gt in ground_truths   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[int]:
  a__:     Optional[Any]										=	[line.strip() for line in open(_SCREAMING_SNAKE_CASE		,     'r'   ).readlines()]
  a__:     Tuple										=	[]

  if args.gold_data_mode == "qa":
    a__:     Dict										=	pd.read_csv(_SCREAMING_SNAKE_CASE		,     sep='\t'		,     header=_SCREAMING_SNAKE_CASE   )
    for answer_list in data[1]:
      a__:     str										=	ast.literal_eval(_SCREAMING_SNAKE_CASE   )
      answers.append(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Union[str, Any]										=	[line.strip() for line in open(_SCREAMING_SNAKE_CASE		,     'r'   ).readlines()]
    a__:     Any										=	[[reference] for reference in references]

  a__:     str										=	0
  for prediction, ground_truths in zip(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    total += 1
    em += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    fa += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  a__:     List[str]										=	100.0 * em / total
  a__:     Tuple										=	100.0 * fa / total

  logger.info(F'F1: {fa:.2f}'   )
  logger.info(F'EM: {em:.2f}'   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     List[str]										=	args.k
  a__:     List[Any]										=	[line.strip() for line in open(_SCREAMING_SNAKE_CASE		,     'r'   ).readlines()]
  a__:     Optional[int]										=	[line.strip() for line in open(_SCREAMING_SNAKE_CASE		,     'r'   ).readlines()]

  a__:     List[str]										=	0
  for hypo, reference in zip(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     Union[str, Any]										=	set(hypo.split('\t'   )[:k]   )
    a__:     List[Any]										=	set(reference.split('\t'   )   )
    total += 1
    em += len(hypo_provenance & ref_provenance   ) / k

  a__:     List[str]										=	100.0 * em / total
  logger.info(F'Precision@{k}: {em: .2f}'   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Any:
  def strip_title(_SCREAMING_SNAKE_CASE   ):
    if title.startswith('"'   ):
      a__:     Union[str, Any]										=	title[1:]
    if title.endswith('"'   ):
      a__:     Tuple										=	title[:-1]
    return title

  a__:     List[str]										=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      _SCREAMING_SNAKE_CASE		,     return_tensors='pt'		,     padding=_SCREAMING_SNAKE_CASE		,     truncation=_SCREAMING_SNAKE_CASE		,     )['input_ids'].to(args.device   )

  a__:     Dict										=	rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE   )
  a__:     int										=	question_enc_outputs[0]

  a__:     Optional[Any]										=	rag_model.retriever(
      _SCREAMING_SNAKE_CASE		,     question_enc_pool_output.cpu().detach().to(torch.floataa   ).numpy()		,     prefix=rag_model.rag.generator.config.prefix		,     n_docs=rag_model.config.n_docs		,     return_tensors='pt'		,     )
  a__:     Tuple										=	rag_model.retriever.index.get_doc_dicts(result.doc_ids   )
  a__:     str										=	[]
  for docs in all_docs:
    a__:     List[Any]										=	[strip_title(_SCREAMING_SNAKE_CASE   ) for title in docs['title']]
    provenance_strings.append('\t'.join(_SCREAMING_SNAKE_CASE   )   )
  return provenance_strings







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  with torch.no_grad():
    a__:     Union[str, Any]										=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        _SCREAMING_SNAKE_CASE		,     return_tensors='pt'		,     padding=_SCREAMING_SNAKE_CASE		,     truncation=_SCREAMING_SNAKE_CASE   )

    a__:     Union[str, Any]										=	inputs_dict.input_ids.to(args.device   )
    a__:     Tuple										=	inputs_dict.attention_mask.to(args.device   )
    a__:     List[Any]										=	rag_model.generate(  # rag_model overwrites generate
        _SCREAMING_SNAKE_CASE		,     attention_mask=_SCREAMING_SNAKE_CASE		,     num_beams=args.num_beams		,     min_length=args.min_length		,     max_length=args.max_length		,     early_stopping=_SCREAMING_SNAKE_CASE		,     num_return_sequences=1		,     bad_words_ids=[[0, 0]]		,     )
    a__:     List[str]										=	rag_model.retriever.generator_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		,     skip_special_tokens=_SCREAMING_SNAKE_CASE   )

    if args.print_predictions:
      for q, a in zip(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
        logger.info('Q: {} - A: {}'.format(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )   )

    return answers







def 						__a   (	)	->List[Any]:
  a__:     List[Any]										=	argparse.ArgumentParser()
  parser.add_argument(
      '--model_type'		,     choices=['rag_sequence', 'rag_token', 'bart']		,     type=_SCREAMING_SNAKE_CASE		,     help=(
          'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
          ' model_name_or_path'
      )		,     )
  parser.add_argument(
      '--index_name'		,     default=_SCREAMING_SNAKE_CASE		,     choices=['exact', 'compressed', 'legacy']		,     type=_SCREAMING_SNAKE_CASE		,     help='RAG model retriever type'		,     )
  parser.add_argument(
      '--index_path'		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     help='Path to the retrieval index'		,     )
  parser.add_argument('--n_docs'		,     default=5		,     type=_SCREAMING_SNAKE_CASE		,     help='Number of retrieved docs'   )
  parser.add_argument(
      '--model_name_or_path'		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     required=_SCREAMING_SNAKE_CASE		,     help='Path to pretrained checkpoints or model identifier from huggingface.co/models'		,     )
  parser.add_argument(
      '--eval_mode'		,     choices=['e2e', 'retrieval']		,     default='e2e'		,     type=_SCREAMING_SNAKE_CASE		,     help=(
          'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
          ' precision@k.'
      )		,     )
  parser.add_argument('--k'		,     default=1		,     type=_SCREAMING_SNAKE_CASE		,     help='k for the precision@k calculation'   )
  parser.add_argument(
      '--evaluation_set'		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     required=_SCREAMING_SNAKE_CASE		,     help='Path to a file containing evaluation samples'		,     )
  parser.add_argument(
      '--gold_data_path'		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     required=_SCREAMING_SNAKE_CASE		,     help='Path to a tab-separated file with gold samples'		,     )
  parser.add_argument(
      '--gold_data_mode'		,     default='qa'		,     type=_SCREAMING_SNAKE_CASE		,     choices=['qa', 'ans']		,     help=(
          'Format of the gold data file'
          'qa - a single line in the following format: question [tab] answer_list'
          'ans - a single line of the gold file contains the expected answer string'
      )		,     )
  parser.add_argument(
      '--predictions_path'		,     type=_SCREAMING_SNAKE_CASE		,     default='predictions.txt'		,     help='Name of the predictions file, to be stored in the checkpoints directory'		,     )
  parser.add_argument(
      '--eval_all_checkpoints'		,     action='store_true'		,     help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'		,     )
  parser.add_argument(
      '--eval_batch_size'		,     default=8		,     type=_SCREAMING_SNAKE_CASE		,     help='Batch size per GPU/CPU for evaluation.'		,     )
  parser.add_argument(
      '--recalculate'		,     help='Recalculate predictions even if the prediction file exists'		,     action='store_true'		,     )
  parser.add_argument(
      '--num_beams'		,     default=4		,     type=_SCREAMING_SNAKE_CASE		,     help='Number of beams to be used when generating answers'		,     )
  parser.add_argument('--min_length'		,     default=1		,     type=_SCREAMING_SNAKE_CASE		,     help='Min length of the generated answers'   )
  parser.add_argument('--max_length'		,     default=50		,     type=_SCREAMING_SNAKE_CASE		,     help='Max length of the generated answers'   )

  parser.add_argument(
      '--print_predictions'		,     action='store_true'		,     help='If True, prints predictions while evaluating.'		,     )
  parser.add_argument(
      '--print_docs'		,     action='store_true'		,     help='If True, prints docs retried while generating.'		,     )
  a__:     Dict										=	parser.parse_args()
  a__:     List[str]										=	torch.device('cuda' if torch.cuda.is_available() else 'cpu'   )
  return args







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     List[str]										=	{}
  if args.model_type is None:
    a__:     Optional[Any]										=	infer_model_type(args.model_name_or_path   )
    assert args.model_type is not None
  if args.model_type.startswith('rag'   ):
    a__:     Union[str, Any]										=	RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
    a__:     Dict										=	args.n_docs
    if args.index_name is not None:
      a__:     Any										=	args.index_name
    if args.index_path is not None:
      a__:     Any										=	args.index_path
  else:
    a__:     int										=	BartForConditionalGeneration

  a__:     List[Any]										=	(
      [f.path for f in os.scandir(args.model_name_or_path   ) if f.is_dir()]
      if args.eval_all_checkpoints
      else [args.model_name_or_path]
  )

  logger.info('Evaluate the following checkpoints: %s'		,     _SCREAMING_SNAKE_CASE   )

  a__:     Any										=	get_scores if args.eval_mode == 'e2e' else get_precision_at_k
  a__:     str										=	evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

  for checkpoint in checkpoints:
    if os.path.exists(args.predictions_path   ) and (not args.recalculate):
      logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path   )   )
      score_fn(_SCREAMING_SNAKE_CASE		,     args.predictions_path		,     args.gold_data_path   )
      continue

    logger.info('***** Running evaluation for {} *****'.format(_SCREAMING_SNAKE_CASE   )   )
    logger.info('  Batch size = %d'		,     args.eval_batch_size   )
    logger.info('  Predictions will be stored under {}'.format(args.predictions_path   )   )

    if args.model_type.startswith('rag'   ):
      a__:     int										=	RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )
      a__:     int										=	model_class.from_pretrained(_SCREAMING_SNAKE_CASE		,     retriever=_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )
      model.retriever.init_retrieval()
    else:
      a__:     int										=	model_class.from_pretrained(_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )
    model.to(args.device   )

    with open(args.evaluation_set		,     'r'   ) as eval_file, open(args.predictions_path		,     'w'   ) as preds_file:
      a__:     Dict										=	[]
      for line in tqdm(_SCREAMING_SNAKE_CASE   ):
        questions.append(line.strip()   )
        if len(_SCREAMING_SNAKE_CASE   ) == args.eval_batch_size:
          a__:     Optional[int]										=	evaluate_batch_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
          preds_file.write('\n'.join(_SCREAMING_SNAKE_CASE   ) + '\n'   )
          preds_file.flush()
          a__:     List[Any]										=	[]
      if len(_SCREAMING_SNAKE_CASE   ) > 0:
        a__:     Union[str, Any]										=	evaluate_batch_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
        preds_file.write('\n'.join(_SCREAMING_SNAKE_CASE   )   )
        preds_file.flush()

      score_fn(_SCREAMING_SNAKE_CASE		,     args.predictions_path		,     args.gold_data_path   )


if __name__ == "__main__":
 lowercase__					       =	get_args()
 main(args)



"""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    __snake_case       (     __lowerCAmelCase				):
      a__        =		42
      a__        =		jnp.floataa
      a__        =		True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        super().setup()
        a__:     int										=	nn.Dense(5    ,			dtype=self.dtype)



      def __call__(						self    ,			*lowercase    ,			**lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Dict										=	super().__call__(*lowercase    ,			**lowercase)
        a__:     str										=	self.cls(outputs[2])
        return outputs[:2] + (cls_out,)







class    __snake_case       (     __lowerCAmelCase				):
      a__        =		FlaxBigBirdForNaturalQuestionsModule







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

  a__:     Tuple										=	partial(_SCREAMING_SNAKE_CASE		,     reduction=jnp.mean   )
  a__:     List[str]										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Union[str, Any]										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Any										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class    __snake_case       :
      a__        =		"google/bigbird-roberta-base"
      a__        =		3000
      a__        =		1_0500

      a__        =		128
      a__        =		3

      a__        =		1
      a__        =		5

      # tx_args
      a__        =		3e-5
      a__        =		0.0
      a__        =		2_0000
      a__        =		0.0095

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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        os.makedirs(self.base_dir    ,			exist_ok=lowercase)
        a__:     str										=	os.path.join(self.base_dir    ,			self.save_dir)
        a__:     List[str]										=	self.batch_size_per_device * jax.device_count()







@dataclass
class    __snake_case       :
      a__        =		42
      a__        =		4096  # no dynamic padding on TPUs



      def __call__(						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	self.collate_fn(lowercase)
        a__:     Optional[int]										=	jax.tree_util.tree_map(lowercase    ,			lowercase)
        return batch



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__							,    a__:     Dict										=	self.fetch_inputs(features['input_ids'])
        a__:     List[Any]										=	{
            'input_ids': jnp.array(lowercase    ,			dtype=jnp.intaa),
            'attention_mask': jnp.array(lowercase    ,			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   lowerCamelCase_   (						self    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        a__:     List[Any]										=	[self._fetch_inputs(lowercase) for ids in input_ids]
        return zip(*lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Union[str, Any]										=	[1 for _ in range(len(lowercase))]
        while len(lowercase) < self.max_length:
          input_ids.append(self.pad_id)
          attention_mask.append(0)
        return input_ids, attention_mask







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->List[Any]:
  if seed is not None:
    a__:     int										=	dataset.shuffle(seed=_SCREAMING_SNAKE_CASE   )
  for i in range(len(_SCREAMING_SNAKE_CASE   ) // batch_size   ):
    a__:     Union[str, Any]										=	dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(_SCREAMING_SNAKE_CASE   )







@partial(jax.pmap		,     axis_name='batch'   )
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )	->Any:
  def loss_fn(_SCREAMING_SNAKE_CASE   ):
    a__:     str										=	model_inputs.pop('start_labels'   )
    a__:     Dict										=	model_inputs.pop('end_labels'   )
    a__:     Optional[int]										=	model_inputs.pop('pooled_labels'   )

    a__:     Optional[Any]										=	state.apply_fn(**_SCREAMING_SNAKE_CASE		,     params=_SCREAMING_SNAKE_CASE		,     dropout_rng=_SCREAMING_SNAKE_CASE		,     train=_SCREAMING_SNAKE_CASE   )
    a__							,    a__							,    a__:     Optional[int]										=	outputs

    return state.loss_fn(
        _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     )

  a__							,    a__:     Union[str, Any]										=	jax.random.split(_SCREAMING_SNAKE_CASE   )
  a__:     List[Any]										=	jax.value_and_grad(_SCREAMING_SNAKE_CASE   )
  a__							,    a__:     str										=	grad_fn(state.params   )
  a__:     Optional[int]										=	jax.lax.pmean({'loss': loss}		,     axis_name='batch'   )
  a__:     int										=	jax.lax.pmean(_SCREAMING_SNAKE_CASE		,     'batch'   )

  a__:     Union[str, Any]										=	state.apply_gradients(grads=_SCREAMING_SNAKE_CASE   )
  return state, metrics, new_drp_rng







@partial(jax.pmap		,     axis_name='batch'   )
def 						__a   (	_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  a__:     Optional[int]										=	model_inputs.pop('start_labels'   )
  a__:     int										=	model_inputs.pop('end_labels'   )
  a__:     Dict										=	model_inputs.pop('pooled_labels'   )

  a__:     Union[str, Any]										=	state.apply_fn(**_SCREAMING_SNAKE_CASE		,     params=state.params		,     train=_SCREAMING_SNAKE_CASE   )
  a__							,    a__							,    a__:     int										=	outputs

  a__:     Optional[int]										=	state.loss_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Tuple										=	jax.lax.pmean({'loss': loss}		,     axis_name='batch'   )
  return metrics




class    __snake_case       (     train_state.TrainState				):
      a__        =		struct.field(pytree_node=__lowerCAmelCase				)







@dataclass
class    __snake_case       :
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		None



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			Optional[int]:





        '''simple docstring'''





        a__:     Dict										=	model.params
        a__:     Any										=	TrainState.create(
            apply_fn=model.__call__    ,			params=lowercase    ,			tx=lowercase    ,			loss_fn=lowercase    ,			)
        if ckpt_dir is not None:
          a__							,    a__							,    a__							,    a__							,    a__:     Any										=	restore_checkpoint(lowercase    ,			lowercase)
          a__:     Any										=	{
              '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__:     str										=	build_tx(**lowercase)
          a__:     Optional[Any]										=	train_state.TrainState(
              step=lowercase    ,			apply_fn=model.__call__    ,			params=lowercase    ,			tx=lowercase    ,			opt_state=lowercase    ,			)
          a__:     int										=	args
          a__:     Union[str, Any]										=	data_collator
          a__:     Any										=	lr
          a__:     Dict										=	params
        a__:     Tuple										=	jax_utils.replicate(lowercase)
        return state



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     int										=	self.args
        a__:     str										=	len(lowercase) // args.batch_size

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

              a__:     List[Any]										=	self.evaluate(lowercase    ,			lowercase)
              a__:     List[str]										=	{
                  'step': state_step.item(),
                  'eval_loss': eval_loss.item(),
                  'tr_loss': tr_loss,
                  'lr': lr.item(),
              }
              tqdm.write(str(lowercase))
              self.logger.log(lowercase    ,			commit=lowercase)

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     Tuple										=	get_batched_dataset(lowercase    ,			self.args.batch_size)
        a__:     Dict										=	len(lowercase) // self.args.batch_size
        a__:     Tuple										=	jnp.array(0    ,			dtype=jnp.floataa)
        a__:     List[Any]										=	0
        for batch in tqdm(lowercase    ,			total=lowercase    ,			desc='Evaluating ... '):
          a__:     str										=	self.data_collator(lowercase)
          a__:     List[str]										=	self.val_step_fn(lowercase    ,			**lowercase)
          running_loss += jax_utils.unreplicate(metrics['loss'])
          i += 1
        return running_loss / i



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	jax_utils.unreplicate(lowercase)
        print(f'SAVING CHECKPOINT IN {save_dir}'    ,			end=' ... ')
        self.model_save_fn(lowercase    ,			params=state.params)
        with open(os.path.join(lowercase    ,			'opt_state.msgpack')    ,			'wb') as f:
          f.write(to_bytes(state.opt_state))
        joblib.dump(self.args    ,			os.path.join(lowercase    ,			'args.joblib'))
        joblib.dump(self.data_collator    ,			os.path.join(lowercase    ,			'data_collator.joblib'))
        with open(os.path.join(lowercase    ,			'training_state.json')    ,			'w') as f:
          json.dump({'step': state.step.item()}    ,			lowercase)
        print('DONE')







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  print(F'RESTORING CHECKPOINT FROM {save_dir}'		,     end=' ... '   )
  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'flax_model.msgpack'   )		,     'rb'   ) as f:
    a__:     int										=	from_bytes(state.params		,     f.read()   )

  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'opt_state.msgpack'   )		,     'rb'   ) as f:
    a__:     Optional[Any]										=	from_bytes(state.opt_state		,     f.read()   )

  a__:     Optional[Any]										=	joblib.load(os.path.join(_SCREAMING_SNAKE_CASE		,     'args.joblib'   )   )
  a__:     int										=	joblib.load(os.path.join(_SCREAMING_SNAKE_CASE		,     'data_collator.joblib'   )   )

  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'training_state.json'   )		,     'r'   ) as f:
    a__:     Any										=	json.load(_SCREAMING_SNAKE_CASE   )
  a__:     Optional[Any]										=	training_state['step']

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[int]:
  a__:     str										=	num_train_steps - warmup_steps
  a__:     str										=	optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE		,     end_value=_SCREAMING_SNAKE_CASE		,     transition_steps=_SCREAMING_SNAKE_CASE   )
  a__:     List[Any]										=	optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE		,     end_value=1e-7		,     transition_steps=_SCREAMING_SNAKE_CASE   )
  a__:     int										=	optax.join_schedules(schedules=[warmup_fn, decay_fn]		,     boundaries=[warmup_steps]   )
  return lr







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Tuple:
  def weight_decay_mask(_SCREAMING_SNAKE_CASE   ):
    a__:     List[Any]										=	traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE   )
    a__:     List[str]										=	{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
    return traverse_util.unflatten_dict(_SCREAMING_SNAKE_CASE   )

  a__:     List[str]										=	scheduler_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  a__:     Any										=	optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE		,     weight_decay=_SCREAMING_SNAKE_CASE		,     mask=_SCREAMING_SNAKE_CASE   )
  return tx, lr


