
import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase_				:							Optional[Any]       =    logging.getLogger()
lowerCamelCase_				:							int       =    torch.cuda.is_available()
lowerCamelCase_				:							int       =    {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-0_8,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def        __lowercase(							__snake_case				:		Path    ,__snake_case				:		list					)       ->   List[str]:
       __snake_case						       =   '\n'.join(lowerCAmelCase__					)
       Path(lowerCAmelCase__					).open('w'					).writelines(lowerCAmelCase__					)


lowerCamelCase_				:							Optional[int]       =    [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCamelCase_				:							Any       =    ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCamelCase_				:							List[str]       =    """patrickvonplaten/t5-tiny-random"""
lowerCamelCase_				:							Optional[Any]       =    """sshleifer/t5-tinier-random"""
lowerCamelCase_				:							Union[str, Any]       =    """sshleifer/bart-tiny-random"""
lowerCamelCase_				:							Any       =    """sshleifer/tiny-mbart"""
lowerCamelCase_				:							List[str]       =    """sshleifer/tiny-marian-en-de"""
lowerCamelCase_				:							Tuple       =    """stas/tiny-wmt19-en-de"""


lowerCamelCase_				:							Optional[Any]       =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def        __lowercase(							__snake_case				:		Dict					)       ->   List[Any]:




       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(lowerCAmelCase__    ,f'''{split}.source'''					)    ,lowerCAmelCase__					)
              _dump_articles(os.path.join(lowerCAmelCase__    ,f'''{split}.target'''					)    ,lowerCAmelCase__					)
       return tmp_dir







class  _lowerCamelCase			(lowercase_					):

     @classmethod
     def 						__lowerCamelCase				(  cls    ):
            logging.disable(logging.CRITICAL    )  # remove noisy download output from tracebacks
            return cls

     @slow
     @require_torch_gpu
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   list_models()
            __snake_case						       =   'sshleifer'
            __snake_case						       =   [x.modelId for x in model_list if x.modelId.startswith(SCREAMING_SNAKE_CASE_    )]
            __snake_case						       =   ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
            __snake_case						       =   []
            for m in model_ids:
                   if m in allowed_to_be_broken:
                          continue
                   try:
                          AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
                   except Exception:
                          failures.append(SCREAMING_SNAKE_CASE_    )
            assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
            self._test_distiller_cli(SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'student_encoder_layers': 2,
                'student_decoder_layers': 1,
                'max_epochs': 4,
                'val_check_interval': 0.2_5,
                'alpha_hid': 2.0,
                'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
            }
            __snake_case						       =   self._test_distiller_cli(SCREAMING_SNAKE_CASE_							,       check_contents=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   list(Path(model.output_dir    ).glob('*.ckpt'    )    )
            self.assertEqual(1							,       len(SCREAMING_SNAKE_CASE_    )    )
            __snake_case						       =   list(Path(model.output_dir    ).glob('**/*.bin'    )    )
            self.assertEqual(len(SCREAMING_SNAKE_CASE_    )							,       2    )
            __snake_case						       =   lmap(str.strip							,       Path(model.hparams.data_dir    ).joinpath('test.source'    ).open().readlines()    )
            __snake_case						       =   tempfile.mktemp()  # XXX: not being cleaned up
            generate_summaries_or_translations(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       str(model.output_dir / 'best_tfmr'    )    )
            self.assertTrue(Path(SCREAMING_SNAKE_CASE_    ).exists()    )

            __snake_case						       =   self.get_auto_remove_tmp_dir()
            convert_pl_to_hf(ckpts[0]							,       transformer_ckpts[0].parent							,       SCREAMING_SNAKE_CASE_    )
            assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_							,       'pytorch_model.bin'    )    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
            __snake_case						       =   torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]							,       dtype=torch.long							,       device=model.device    )
            __snake_case						       =   target_ids[:, :-1].contiguous()  # Why this line?
            __snake_case						       =   target_ids[:, 1:].clone()  # why clone?
            __snake_case						       =   model(
                SCREAMING_SNAKE_CASE_							,       attention_mask=SCREAMING_SNAKE_CASE_							,       decoder_input_ids=SCREAMING_SNAKE_CASE_							,       labels=SCREAMING_SNAKE_CASE_							,       use_cache=SCREAMING_SNAKE_CASE_    ).loss

            __snake_case						       =   model(SCREAMING_SNAKE_CASE_							,       attention_mask=SCREAMING_SNAKE_CASE_							,       decoder_input_ids=SCREAMING_SNAKE_CASE_							,       use_cache=SCREAMING_SNAKE_CASE_    ).logits

            __snake_case						       =   nn.functional.log_softmax(SCREAMING_SNAKE_CASE_							,       dim=-1    )
            __snake_case						       =   label_smoothed_nll_loss(
                SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       0.1							,       ignore_index=model.config.pad_token_id    )
            with self.assertRaises(SCREAMING_SNAKE_CASE_    ):
                   # TODO: understand why this breaks
                   self.assertEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'student_encoder_layers': 2,
                'student_decoder_layers': 1,
                'num_train_epochs': 4,
                'val_check_interval': 0.2_5,
                'alpha_hid': 2.0,
                'task': 'translation',
                'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
                'tokenizer_name': MBART_TINY,
                'teacher': MBART_TINY,
                'src_lang': 'en_XX',
                'tgt_lang': 'ro_RO',
            }
            __snake_case						       =   self._test_distiller_cli(SCREAMING_SNAKE_CASE_							,       check_contents=SCREAMING_SNAKE_CASE_    )
            assert model.model.config.model_type == "mbart"

            __snake_case						       =   list(Path(model.output_dir    ).glob('*.ckpt'    )    )
            self.assertEqual(1							,       len(SCREAMING_SNAKE_CASE_    )    )
            __snake_case						       =   list(Path(model.output_dir    ).glob('**/*.bin'    )    )
            __snake_case						       =   list(Path(model.output_dir    ).glob('best_tfmr/*'    )    )
            assert len(SCREAMING_SNAKE_CASE_    ) > 2
            self.assertEqual(len(SCREAMING_SNAKE_CASE_    )							,       2    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'student_encoder_layers': 1,
                'student_decoder_layers': 1,
                'alpha_hid': 2.0,
                'teacher': T5_TINY,
                'model_name_or_path': T5_TINY,
                'tokenizer_name': T5_TINY,
            }
            self._test_distiller_cli(SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'teacher': T5_TINY,
                'student': T5_TINIER,
                'model_name_or_path': T5_TINIER,
                'tokenizer_name': T5_TINIER,
            }
            self._test_distiller_cli(SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=True    ):
            __snake_case						       =   {
                'label_smoothing': 0.0,
                'early_stopping_patience': -1,
                'train_batch_size': 1,
                'eval_batch_size': 2,
                'max_epochs': 2,
                'alpha_mlm': 0.2,
                'alpha_ce': 0.8,
                'do_predict': True,
                'model_name_or_path': 'sshleifer/tinier_bart',
                'teacher': CHEAP_ARGS['model_name_or_path'],
                'val_check_interval': 0.5,
            }
            default_updates.update(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   CHEAP_ARGS.copy()
            __snake_case						       =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
            __snake_case						       =   self.get_auto_remove_tmp_dir()

            args_d.update(data_dir=SCREAMING_SNAKE_CASE_							,       output_dir=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   distill_main(argparse.Namespace(**SCREAMING_SNAKE_CASE_    )    )
            if not check_contents:
                   return model
            __snake_case						       =   os.listdir(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   {os.path.basename(SCREAMING_SNAKE_CASE_    ) for p in contents}
            __snake_case						       =   [p for p in contents if p.endswith('ckpt'    )]
            assert len(SCREAMING_SNAKE_CASE_    ) > 0

            self.assertIn('test_generations.txt'							,       SCREAMING_SNAKE_CASE_    )
            self.assertIn('test_results.txt'							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   load_json(model.metrics_save_path    )
            __snake_case						       =   metrics['val'][-1]
            self.assertGreaterEqual(last_step_stats['val_avg_gen_time']							,       0.0_1    )
            self.assertGreaterEqual(1.0							,       last_step_stats['val_avg_gen_time']    )
            self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1    )
            self.assertEqual(len(metrics['val']    )							,       SCREAMING_SNAKE_CASE_    )
            self.assertEqual(len(metrics['test']    )							,       1    )
            return model







class  _lowerCamelCase			(lowercase_					):

     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]							,       )
     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   CHEAP_ARGS.copy()
            __snake_case						       =   'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
            __snake_case						       =   0.1 if task == 'translation' else 0

            __snake_case						       =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
            __snake_case						       =   self.get_auto_remove_tmp_dir()
            args_d.update(
                data_dir=SCREAMING_SNAKE_CASE_							,       model_name_or_path=SCREAMING_SNAKE_CASE_							,       tokenizer_name=SCREAMING_SNAKE_CASE_							,       train_batch_size=2							,       eval_batch_size=2							,       output_dir=SCREAMING_SNAKE_CASE_							,       do_predict=SCREAMING_SNAKE_CASE_							,       task=SCREAMING_SNAKE_CASE_							,       src_lang='en_XX'							,       tgt_lang='ro_RO'							,       freeze_encoder=SCREAMING_SNAKE_CASE_							,       freeze_embeds=SCREAMING_SNAKE_CASE_							,       )
            assert "n_train" in args_d
            __snake_case						       =   argparse.Namespace(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   main(SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   module.model.get_input_embeddings()
            assert not input_embeds.weight.requires_grad
            if model == T5_TINY:
                   __snake_case						       =   module.model.lm_head
                   assert not lm_head.weight.requires_grad
                   assert (lm_head.weight == input_embeds.weight).all().item()
            elif model == FSMT_TINY:
                   __snake_case						       =   module.model.model
                   __snake_case						       =   fsmt.decoder.embed_positions
                   assert not embed_pos.weight.requires_grad
                   assert not fsmt.decoder.embed_tokens.weight.requires_grad
                   # check that embeds are not the same
                   assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
            else:
                   __snake_case						       =   module.model.model
                   __snake_case						       =   bart.decoder.embed_positions
                   assert not embed_pos.weight.requires_grad
                   assert not bart.shared.weight.requires_grad
                   # check that embeds are the same
                   assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
                   assert bart.decoder.embed_tokens == bart.shared

            __snake_case						       =   load_json(module.output_dir / 'text_batch.json'    )
            assert isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            assert len(SCREAMING_SNAKE_CASE_    ) >= 4

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   CHEAP_ARGS.copy()

            __snake_case						       =   'summarization'
            __snake_case						       =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )

            args_d.update(
                data_dir=SCREAMING_SNAKE_CASE_							,       tokenizer_name=SCREAMING_SNAKE_CASE_							,       train_batch_size=2							,       eval_batch_size=2							,       do_predict=SCREAMING_SNAKE_CASE_							,       task=SCREAMING_SNAKE_CASE_							,       src_lang='en_XX'							,       tgt_lang='ro_RO'							,       freeze_encoder=SCREAMING_SNAKE_CASE_							,       freeze_embeds=SCREAMING_SNAKE_CASE_							,       )

            # test models whose config includes the extra_model_args
            __snake_case						       =   BART_TINY
            __snake_case						       =   self.get_auto_remove_tmp_dir()
            __snake_case						       =   args_d.copy()
            args_da.update(
                model_name_or_path=SCREAMING_SNAKE_CASE_							,       output_dir=SCREAMING_SNAKE_CASE_							,       )
            __snake_case						       =   ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
            for p in extra_model_params:
                   __snake_case						       =   0.5
            __snake_case						       =   argparse.Namespace(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   main(SCREAMING_SNAKE_CASE_    )
            for p in extra_model_params:
                   assert getattr(model.config							,       SCREAMING_SNAKE_CASE_    ) == 0.5, f'''failed to override the model config for param {p}'''

            # test models whose config doesn't include the extra_model_args
            __snake_case						       =   T5_TINY
            __snake_case						       =   self.get_auto_remove_tmp_dir()
            __snake_case						       =   args_d.copy()
            args_da.update(
                model_name_or_path=SCREAMING_SNAKE_CASE_							,       output_dir=SCREAMING_SNAKE_CASE_							,       )
            __snake_case						       =   'encoder_layerdrop'
            __snake_case						       =   0.5
            __snake_case						       =   argparse.Namespace(**SCREAMING_SNAKE_CASE_    )
            with pytest.raises(SCREAMING_SNAKE_CASE_    ) as excinfo:
                   __snake_case						       =   main(SCREAMING_SNAKE_CASE_    )
            assert str(excinfo.value    ) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   CHEAP_ARGS.copy()

            __snake_case						       =   'summarization'
            __snake_case						       =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )

            __snake_case						       =   BART_TINY
            __snake_case						       =   self.get_auto_remove_tmp_dir()

            args_d.update(
                data_dir=SCREAMING_SNAKE_CASE_							,       model_name_or_path=SCREAMING_SNAKE_CASE_							,       output_dir=SCREAMING_SNAKE_CASE_							,       tokenizer_name=SCREAMING_SNAKE_CASE_							,       train_batch_size=2							,       eval_batch_size=2							,       do_predict=SCREAMING_SNAKE_CASE_							,       task=SCREAMING_SNAKE_CASE_							,       src_lang='en_XX'							,       tgt_lang='ro_RO'							,       freeze_encoder=SCREAMING_SNAKE_CASE_							,       freeze_embeds=SCREAMING_SNAKE_CASE_							,       )

            # emulate finetune.py
            __snake_case						       =   argparse.ArgumentParser()
            __snake_case						       =   pl.Trainer.add_argparse_args(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE_							,       os.getcwd()    )
            __snake_case						       =   {'--help': True}

            # --help test
            with pytest.raises(SCREAMING_SNAKE_CASE_    ) as excinfo:
                   with CaptureStdout() as cs:
                          __snake_case						       =   parser.parse_args(SCREAMING_SNAKE_CASE_    )
                   assert False, "--help is expected to sys.exit"
            assert excinfo.type == SystemExit
            __snake_case						       =   lightning_base.arg_to_scheduler_metavar
            assert expected in cs.out, "--help is expected to list the supported schedulers"

            # --lr_scheduler=non_existing_scheduler test
            __snake_case						       =   'non_existing_scheduler'
            __snake_case						       =   {f'''--lr_scheduler={unsupported_param}'''}
            with pytest.raises(SCREAMING_SNAKE_CASE_    ) as excinfo:
                   with CaptureStderr() as cs:
                          __snake_case						       =   parser.parse_args(SCREAMING_SNAKE_CASE_    )
                   assert False, "invalid argument is expected to sys.exit"
            assert excinfo.type == SystemExit
            __snake_case						       =   f'''invalid choice: \'{unsupported_param}\''''
            assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

            # --lr_scheduler=existing_scheduler test
            __snake_case						       =   'cosine'
            __snake_case						       =   args_d.copy()
            __snake_case						       =   supported_param
            __snake_case						       =   argparse.Namespace(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   main(SCREAMING_SNAKE_CASE_    )
            assert (
                getattr(model.hparams							,       'lr_scheduler'    ) == supported_param
            ), f'''lr_scheduler={supported_param} shouldn\'t fail'''





"""simple docstring"""







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
     print("""#################### AVAILABLE DEVICES ####################""")
     print(jax.devices())
     print("""###########################################################""")

     # setup for wandb sweep
     lowercase__						:							Any        =      Args()
     lowercase__						:							List[Any]        =      wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
     lowercase__						:							List[Any]        =      dict(logger.config)
     del wandb_args["batch_size"]
     lowercase__						:							List[str]        =      replace(args, **wandb_args)
     lowercase__						:							Any        =      args.base_dir + """-""" + wandb.run.id
     lowercase__						:							Tuple        =      replace(args, base_dir=base_dir)
     print(args)

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """lr""": args.lr,
         """init_lr""": args.init_lr,
         """warmup_steps""": args.warmup_steps,
         """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
         """weight_decay""": args.weight_decay,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[Any]        =      Trainer(
         args=args,
         data_collator=data_collator,
         model_save_fn=model.save_pretrained,
         train_step_fn=train_step,
         val_step_fn=val_step,
         logger=logger,
         scheduler_fn=lr,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
     try:
          trainer.train(state, tr_dataset, val_dataset)
     except KeyboardInterrupt:
          print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

     print("""SAVING WEIGHTS IN `final-weights`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





