
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      :					List[str]   	=       logging.getLogger()
__lowerCAmelCase      :					List[str]   	=       torch.cuda.is_available()
__lowerCAmelCase      :					Any   	=       {
    '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__,					UpperCAmelCase__ ):






  '''simple docstring'''





  __magic_name__    					=	'\n'.join(UpperCAmelCase__ )
  Path(UpperCAmelCase__ ).open("""w""" ).writelines(UpperCAmelCase__ )


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


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

def a__   (	UpperCAmelCase__ ):






  '''simple docstring'''





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




class 	UpperCAmelCase_      (			snake_case__					):


  '''simple docstring'''







  @classmethod
  def 							_lowercase							(			cls      :			Dict   )      ->     Any:







    """simple docstring"""






    logging.disable(logging.CRITICAL   )  # remove noisy download output from tracebacks
    return cls
  @slow
  @require_torch_gpu
  def 							_lowercase							(			self      :			str   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	list_models()
    __magic_name__    					=	'sshleifer'
    __magic_name__    					=	[x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase__   )]
    __magic_name__    					=	['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
    __magic_name__    					=	[]
    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      :			Union[str, Any]   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
    self._test_distiller_cli(UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[Any]   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	{
        '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',
    }
    __magic_name__    					=	self._test_distiller_cli(UpperCamelCase__      ,							check_contents=UpperCamelCase__   )

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

    __magic_name__    					=	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      :			Dict   )      ->     Optional[Any]:







    """simple docstring"""






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

    __magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							decoder_input_ids=UpperCamelCase__      ,							use_cache=UpperCamelCase__   ).logits

    __magic_name__    					=	nn.functional.log_softmax(UpperCamelCase__      ,							dim=-1   )
    __magic_name__    					=	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      :			int   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	{
        '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',
    }
    __magic_name__    					=	self._test_distiller_cli(UpperCamelCase__      ,							check_contents=UpperCamelCase__   )
    assert model.model.config.model_type == "mbart"

    __magic_name__    					=	list(Path(model.output_dir   ).glob("""*.ckpt"""   )   )
    self.assertEqual(1      ,							len(UpperCamelCase__   )   )
    __magic_name__    					=	list(Path(model.output_dir   ).glob("""**/*.bin"""   )   )
    __magic_name__    					=	list(Path(model.output_dir   ).glob("""best_tfmr/*"""   )   )
    assert len(UpperCamelCase__   ) > 2
    self.assertEqual(len(UpperCamelCase__   )      ,							2   )
  def 							_lowercase							(			self      :			Any   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	{
        '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      :			Optional[int]   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__    					=	{
        'teacher': T5_TINY,
        'student': T5_TINIER,
        'model_name_or_path': T5_TINIER,
        'tokenizer_name': T5_TINIER,
    }
    self._test_distiller_cli(UpperCamelCase__   )





  def 							_lowercase							(			self      :			str      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Optional[int]=True   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	{
        '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__   )
    __magic_name__    					=	CHEAP_ARGS.copy()
    __magic_name__    					=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
    __magic_name__    					=	self.get_auto_remove_tmp_dir()

    args_d.update(data_dir=UpperCamelCase__      ,							output_dir=UpperCamelCase__      ,							**UpperCamelCase__   )
    __magic_name__    					=	distill_main(argparse.Namespace(**UpperCamelCase__   )   )
    if not check_contents:
      return model
    __magic_name__    					=	os.listdir(UpperCamelCase__   )
    __magic_name__    					=	{os.path.basename(UpperCamelCase__   ) for p in contents}
    __magic_name__    					=	[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__   )

    __magic_name__    					=	load_json(model.metrics_save_path   )
    __magic_name__    					=	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__   )
    __magic_name__    					=	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 	UpperCAmelCase_      (			snake_case__					):


  '''simple docstring'''







  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]      ,							)
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	CHEAP_ARGS.copy()
    __magic_name__    					=	'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
    __magic_name__    					=	0.1 if task == 'translation' else 0

    __magic_name__    					=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
    __magic_name__    					=	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
    __magic_name__    					=	argparse.Namespace(**UpperCamelCase__   )
    __magic_name__    					=	main(UpperCamelCase__   )

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

    __magic_name__    					=	load_json(module.output_dir / """text_batch.json"""   )
    assert isinstance(UpperCamelCase__      ,							UpperCamelCase__   )
    assert len(UpperCamelCase__   ) >= 4
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






    __magic_name__    					=	CHEAP_ARGS.copy()

    __magic_name__    					=	'summarization'
    __magic_name__    					=	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
    __magic_name__    					=	BART_TINY
    __magic_name__    					=	self.get_auto_remove_tmp_dir()
    __magic_name__    					=	args_d.copy()
    args_da.update(
        model_name_or_path=UpperCamelCase__      ,							output_dir=UpperCamelCase__      ,							)
    __magic_name__    					=	('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
    for p in extra_model_params:
      __magic_name__    					=	0.5
    __magic_name__    					=	argparse.Namespace(**UpperCamelCase__   )
    __magic_name__    					=	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
    __magic_name__    					=	T5_TINY
    __magic_name__    					=	self.get_auto_remove_tmp_dir()
    __magic_name__    					=	args_d.copy()
    args_da.update(
        model_name_or_path=UpperCamelCase__      ,							output_dir=UpperCamelCase__      ,							)
    __magic_name__    					=	'encoder_layerdrop'
    __magic_name__    					=	0.5
    __magic_name__    					=	argparse.Namespace(**UpperCamelCase__   )
    with pytest.raises(UpperCamelCase__   ) as excinfo:
      __magic_name__    					=	main(UpperCamelCase__   )
    assert str(excinfo.value   ) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''





  def 							_lowercase							(			self      :			int   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	CHEAP_ARGS.copy()

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

    __magic_name__    					=	BART_TINY
    __magic_name__    					=	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
    __magic_name__    					=	argparse.ArgumentParser()
    __magic_name__    					=	pl.Trainer.add_argparse_args(UpperCamelCase__   )
    __magic_name__    					=	SummarizationModule.add_model_specific_args(UpperCamelCase__      ,							os.getcwd()   )
    __magic_name__    					=	{'--help': True}

    # --help test
    with pytest.raises(UpperCamelCase__   ) as excinfo:
      with CaptureStdout() as cs:
        __magic_name__    					=	parser.parse_args(UpperCamelCase__   )
      assert False, "--help is expected to sys.exit"
    assert excinfo.type == SystemExit
    __magic_name__    					=	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
    __magic_name__    					=	'non_existing_scheduler'
    __magic_name__    					=	{F'''--lr_scheduler={unsupported_param}'''}
    with pytest.raises(UpperCamelCase__   ) as excinfo:
      with CaptureStderr() as cs:
        __magic_name__    					=	parser.parse_args(UpperCamelCase__   )
      assert False, "invalid argument is expected to sys.exit"
    assert excinfo.type == SystemExit
    __magic_name__    					=	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
    __magic_name__    					=	'cosine'
    __magic_name__    					=	args_d.copy()
    __magic_name__    					=	supported_param
    __magic_name__    					=	argparse.Namespace(**UpperCamelCase__   )
    __magic_name__    					=	main(UpperCamelCase__   )
    assert (
        getattr(model.hparams      ,							"""lr_scheduler"""   ) == supported_param
    ), F'''lr_scheduler={supported_param} shouldn\'t fail'''



from itertools import compress, repeat
from math import ceil, sqrt

def a__   (	A_ ):






  '''simple docstring'''





  if num <= 2:
    return []
  if num == 3:
    return [2]

  # Odd sieve for numbers in range [3, num - 1]
  __magic_name__    					=	bytearray(b"""\x01""" ) * ((num >> 1) - 1)

  for i in range(3,					int(sqrt(A_ ) ) + 1,					2 ):
    if sieve[(i >> 1) - 1]:
      __magic_name__    					=	i**2
      __magic_name__    					=	repeat(
          0,					ceil((num - i_squared) / (i << 1) ) )

  return [2] + list(compress(range(3,					A_,					2 ),					A_ ) )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

