

"""simple docstring"""





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)

_a				   =							logging.getLogger()
_a				   =							torch.cuda.is_available()
_a				   =							{
    "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-08,
    "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 						_A						(						UpperCamelCase_  : List[Any],    UpperCamelCase_  : Optional[Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		'''\n'''.join(snake_case_)
   Path(snake_case_).open("w").writelines(snake_case_)


_a				   =							[" Sam ate lunch today.", "Sams lunch ingredients."]
_a				   =							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_a				   =							"patrickvonplaten/t5-tiny-random"
_a				   =							"sshleifer/t5-tinier-random"
_a				   =							"sshleifer/bart-tiny-random"
_a				   =							"sshleifer/tiny-mbart"
_a				   =							"sshleifer/tiny-marian-en-de"
_a				   =							"stas/tiny-wmt19-en-de"


_a				   =							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




def 						_A						(						UpperCamelCase_  : str)			->		List[str]:
   '''simple docstring'''


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




class       _lowerCAmelCase						(			UpperCamelCase_     ):







 """simple docstring"""







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


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


 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
    self._test_distiller_cli(__UpperCamelCase			)


 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		{
        '''student_encoder_layers''': 2,
        '''student_decoder_layers''': 1,
        '''max_epochs''': 4,
        '''val_check_interval''': 0.25,
        '''alpha_hid''': 2.0,
        '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
    }
    __lowercase			=		self._test_distiller_cli(__UpperCamelCase,      check_contents=__UpperCamelCase			)

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

    __lowercase			=		self.get_auto_remove_tmp_dir()
    convert_pl_to_hf(ckpts[0],      transformer_ckpts[0].parent,      __UpperCamelCase			)
    assert os.path.exists(os.path.join(__UpperCamelCase,      "pytorch_model.bin"			)			)


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

    __lowercase			=		model(__UpperCamelCase,      attention_mask=__UpperCamelCase,      decoder_input_ids=__UpperCamelCase,      use_cache=__UpperCamelCase			).logits

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


 def       _lowercase				(    self							:    str			):
    __lowercase			=		{
        '''student_encoder_layers''': 2,
        '''student_decoder_layers''': 1,
        '''num_train_epochs''': 4,
        '''val_check_interval''': 0.25,
        '''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''',
    }
    __lowercase			=		self._test_distiller_cli(__UpperCamelCase,      check_contents=__UpperCamelCase			)
    assert model.model.config.model_type == "mbart"

    __lowercase			=		list(Path(model.output_dir			).glob("*.ckpt"			)			)
    self.assertEqual(1,      len(__UpperCamelCase			)			)
    __lowercase			=		list(Path(model.output_dir			).glob("**/*.bin"			)			)
    __lowercase			=		list(Path(model.output_dir			).glob("best_tfmr/*"			)			)
    assert len(__UpperCamelCase			) > 2
    self.assertEqual(len(__UpperCamelCase			),      2			)


 def       _lowercase				(    self							:    str			):
    __lowercase			=		{
        '''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(__UpperCamelCase			)


 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		{
        '''teacher''': T5_TINY,
        '''student''': T5_TINIER,
        '''model_name_or_path''': T5_TINIER,
        '''tokenizer_name''': T5_TINIER,
    }
    self._test_distiller_cli(__UpperCamelCase			)



 def       _lowercase				(    self							:    Dict,      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    List[Any]=True			):
    __lowercase			=		{
        '''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(__UpperCamelCase			)
    __lowercase			=		CHEAP_ARGS.copy()
    __lowercase			=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
    __lowercase			=		self.get_auto_remove_tmp_dir()

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

    self.assertIn("test_generations.txt",      __UpperCamelCase			)
    self.assertIn("test_results.txt",      __UpperCamelCase			)

    __lowercase			=		load_json(model.metrics_save_path			)
    __lowercase			=		metrics['''val'''][-1]
    self.assertGreaterEqual(last_step_stats["val_avg_gen_time"],      0.01			)
    self.assertGreaterEqual(1.0,      last_step_stats["val_avg_gen_time"]			)
    self.assertIsInstance(last_step_stats[F"""val_avg_{model.val_metric}"""],      __UpperCamelCase			)
    __lowercase			=		int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1			)
    self.assertEqual(len(metrics["val"]			),      __UpperCamelCase			)
    self.assertEqual(len(metrics["test"]			),      1			)
    return model







class       _lowerCAmelCase						(			UpperCamelCase_     ):







 """simple docstring"""







 @parameterized.expand(
     [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],      )
 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    List[str]			):
    __lowercase			=		CHEAP_ARGS.copy()
    __lowercase			=		'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
    __lowercase			=		0.1 if task == '''translation''' else 0

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

    __lowercase			=		module.model.get_input_embeddings()
    assert not input_embeds.weight.requires_grad
    if model == T5_TINY:
       __lowercase			=		module.model.lm_head
       assert not lm_head.weight.requires_grad
       assert (lm_head.weight == input_embeds.weight).all().item()
    elif model == FSMT_TINY:
       __lowercase			=		module.model.model
       __lowercase			=		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:
       __lowercase			=		module.model.model
       __lowercase			=		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

    __lowercase			=		load_json(module.output_dir / "text_batch.json"			)
    assert isinstance(__UpperCamelCase,      __UpperCamelCase			)
    assert len(__UpperCamelCase			) >= 4


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		CHEAP_ARGS.copy()

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

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

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

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



 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		CHEAP_ARGS.copy()

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

    __lowercase			=		BART_TINY
    __lowercase			=		self.get_auto_remove_tmp_dir()

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

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

    # --help test
    with pytest.raises(__UpperCamelCase			) as excinfo:
       with CaptureStdout() as cs:
          __lowercase			=		parser.parse_args(__UpperCamelCase			)
       assert False, "--help is expected to sys.exit"
    assert excinfo.type == SystemExit
    __lowercase			=		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
    __lowercase			=		'''non_existing_scheduler'''
    __lowercase			=		{F"""--lr_scheduler={unsupported_param}"""}
    with pytest.raises(__UpperCamelCase			) as excinfo:
       with CaptureStderr() as cs:
          __lowercase			=		parser.parse_args(__UpperCamelCase			)
       assert False, "invalid argument is expected to sys.exit"
    assert excinfo.type == SystemExit
    __lowercase			=		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
    __lowercase			=		'''cosine'''
    __lowercase			=		args_d.copy()
    __lowercase			=		supported_param
    __lowercase			=		argparse.Namespace(**__UpperCamelCase			)
    __lowercase			=		main(__UpperCamelCase			)
    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
   _a				   =							Args()
   _a				   =							wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _a				   =							dict(logger.config)
   del wandb_args["batch_size"]
   _a				   =							replace(args, **wandb_args)
   _a				   =							args.base_dir + '-' + wandb.run.id
   _a				   =							replace(args, base_dir=base_dir)
   print(args)

   _a				   =							load_dataset('json', data_files=args.tr_data_path)['train']
   _a				   =							load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _a				   =							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _a				   =							tr_dataset.shuffle().select(indices)
   _a				   =							range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _a				   =							val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   _a				   =							{
       '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,
   }
   _a   ,		_a				   =							build_tx(**tx_args)

   _a				   =							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,
   )

   _a				   =							None
   _a				   =							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`')
   _a				   =							jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)




