code: 
  
  
  
  
  
  '''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)
  
  lowercase_			       =				logging.getLogger()
  lowercase_			       =				torch.cuda.is_available()
  lowercase_			       =				{
      """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 lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					List[str]					)		->Any:
      _SCREAMING_SNAKE_CASE             =    '''\n'''.join(UpperCAmelCase_					)
      Path(UpperCAmelCase_					).open("""w"""					).writelines(UpperCAmelCase_					)
  
  
  lowercase_			       =				[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
  lowercase_			       =				["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
  lowercase_			       =				"""patrickvonplaten/t5-tiny-random"""
  lowercase_			       =				"""sshleifer/t5-tinier-random"""
  lowercase_			       =				"""sshleifer/bart-tiny-random"""
  lowercase_			       =				"""sshleifer/tiny-mbart"""
  lowercase_			       =				"""sshleifer/tiny-marian-en-de"""
  lowercase_			       =				"""stas/tiny-wmt19-en-de"""
  
  
  lowercase_			       =				logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Tuple					)		->Any:
  
  
  
  
  
  
      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       a_  (       A_ ):
  
      '''simple docstring'''
  
  
      @classmethod
      def     snake_case_(	cls )	->     List[Any]:
          logging.disable(logging.CRITICAL )  # remove noisy download output from tracebacks
          return cls
  
  
      @slow
      @require_torch_gpu
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    list_models()
          _SCREAMING_SNAKE_CASE             =    '''sshleifer'''
          _SCREAMING_SNAKE_CASE             =    [x.modelId for x in model_list if x.modelId.startswith(A )]
          _SCREAMING_SNAKE_CASE             =    ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
          _SCREAMING_SNAKE_CASE             =    []
          for m in model_ids:
              if m in allowed_to_be_broken:
                  continue
              try:
                  AutoConfig.from_pretrained(A )
              except Exception:
                  failures.append(A )
          assert not failures, f'The following models could not be loaded through AutoConfig: {failures}'
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
          self._test_distiller_cli(A )
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    {
              '''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''',
          }
          _SCREAMING_SNAKE_CASE             =    self._test_distiller_cli(A				,	check_contents=A )
  
          _SCREAMING_SNAKE_CASE             =    list(Path(model.output_dir ).glob("""*.ckpt""" ) )
          self.assertEqual(1				,	len(A ) )
          _SCREAMING_SNAKE_CASE             =    list(Path(model.output_dir ).glob("""**/*.bin""" ) )
          self.assertEqual(len(A )				,	2 )
          _SCREAMING_SNAKE_CASE             =    lmap(str.strip				,	Path(model.hparams.data_dir ).joinpath("""test.source""" ).open().readlines() )
          _SCREAMING_SNAKE_CASE             =    tempfile.mktemp()  # XXX: not being cleaned up
          generate_summaries_or_translations(A				,	A				,	str(model.output_dir / """best_tfmr""" ) )
          self.assertTrue(Path(A ).exists() )
  
          _SCREAMING_SNAKE_CASE             =    self.get_auto_remove_tmp_dir()
          convert_pl_to_hf(ckpts[0]				,	transformer_ckpts[0].parent				,	A )
          assert os.path.exists(os.path.join(A				,	"""pytorch_model.bin""" ) )
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    AutoModelForSeqaSeqLM.from_pretrained(A )
          _SCREAMING_SNAKE_CASE             =    model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
          _SCREAMING_SNAKE_CASE             =    torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]				,	dtype=torch.long				,	device=model.device )
          _SCREAMING_SNAKE_CASE             =    target_ids[:, :-1].contiguous()  # Why this line?
          _SCREAMING_SNAKE_CASE             =    target_ids[:, 1:].clone()  # why clone?
          _SCREAMING_SNAKE_CASE             =    model(
              A				,	attention_mask=A				,	decoder_input_ids=A				,	labels=A				,	use_cache=A ).loss
  
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A				,	decoder_input_ids=A				,	use_cache=A ).logits
  
          _SCREAMING_SNAKE_CASE             =    nn.functional.log_softmax(A				,	dim=-1 )
          _SCREAMING_SNAKE_CASE             =    label_smoothed_nll_loss(
              A				,	A				,	0.1				,	ignore_index=model.config.pad_token_id )
          with self.assertRaises(A ):
              # TODO: understand why this breaks
              self.assertEqual(A				,	A )
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    {
              '''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''',
          }
          _SCREAMING_SNAKE_CASE             =    self._test_distiller_cli(A				,	check_contents=A )
          assert model.model.config.model_type == "mbart"
  
          _SCREAMING_SNAKE_CASE             =    list(Path(model.output_dir ).glob("""*.ckpt""" ) )
          self.assertEqual(1				,	len(A ) )
          _SCREAMING_SNAKE_CASE             =    list(Path(model.output_dir ).glob("""**/*.bin""" ) )
          _SCREAMING_SNAKE_CASE             =    list(Path(model.output_dir ).glob("""best_tfmr/*""" ) )
          assert len(A ) > 2
          self.assertEqual(len(A )				,	2 )
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_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(A )
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    {
              '''teacher''': T5_TINY,
              '''student''': T5_TINIER,
              '''model_name_or_path''': T5_TINIER,
              '''tokenizer_name''': T5_TINIER,
          }
          self._test_distiller_cli(A )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A=True )	->     Any:
          _SCREAMING_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(A )
          _SCREAMING_SNAKE_CASE             =    CHEAP_ARGS.copy()
          _SCREAMING_SNAKE_CASE             =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
          _SCREAMING_SNAKE_CASE             =    self.get_auto_remove_tmp_dir()
  
          args_d.update(data_dir=A				,	output_dir=A				,	**A )
          _SCREAMING_SNAKE_CASE             =    distill_main(argparse.Namespace(**A ) )
          if not check_contents:
              return model
          _SCREAMING_SNAKE_CASE             =    os.listdir(A )
          _SCREAMING_SNAKE_CASE             =    {os.path.basename(A ) for p in contents}
          _SCREAMING_SNAKE_CASE             =    [p for p in contents if p.endswith("""ckpt""" )]
          assert len(A ) > 0
  
          self.assertIn("""test_generations.txt"""				,	A )
          self.assertIn("""test_results.txt"""				,	A )
  
          _SCREAMING_SNAKE_CASE             =    load_json(model.metrics_save_path )
          _SCREAMING_SNAKE_CASE             =    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}']				,	A )
          _SCREAMING_SNAKE_CASE             =    int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1 )
          self.assertEqual(len(metrics["""val"""] )				,	A )
          self.assertEqual(len(metrics["""test"""] )				,	1 )
          return model
  
  
  
  
  class       a_  (       A_ ):
  
      '''simple docstring'''
  
  
      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]				,	)
      def     snake_case_(	self				,	A )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    CHEAP_ARGS.copy()
          _SCREAMING_SNAKE_CASE             =    '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
          _SCREAMING_SNAKE_CASE             =    0.1 if task == '''translation''' else 0
  
          _SCREAMING_SNAKE_CASE             =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
          _SCREAMING_SNAKE_CASE             =    self.get_auto_remove_tmp_dir()
          args_d.update(
              data_dir=A				,	model_name_or_path=A				,	tokenizer_name=A				,	train_batch_size=2				,	eval_batch_size=2				,	output_dir=A				,	do_predict=A				,	task=A				,	src_lang="""en_XX"""				,	tgt_lang="""ro_RO"""				,	freeze_encoder=A				,	freeze_embeds=A				,	)
          assert "n_train" in args_d
          _SCREAMING_SNAKE_CASE             =    argparse.Namespace(**A )
          _SCREAMING_SNAKE_CASE             =    main(A )
  
          _SCREAMING_SNAKE_CASE             =    module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
              _SCREAMING_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:
              _SCREAMING_SNAKE_CASE             =    module.model.model
              _SCREAMING_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:
              _SCREAMING_SNAKE_CASE             =    module.model.model
              _SCREAMING_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
  
          _SCREAMING_SNAKE_CASE             =    load_json(module.output_dir / """text_batch.json""" )
          assert isinstance(A				,	A )
          assert len(A ) >= 4
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    CHEAP_ARGS.copy()
  
          _SCREAMING_SNAKE_CASE             =    '''summarization'''
          _SCREAMING_SNAKE_CASE             =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
  
          args_d.update(
              data_dir=A				,	tokenizer_name=A				,	train_batch_size=2				,	eval_batch_size=2				,	do_predict=A				,	task=A				,	src_lang="""en_XX"""				,	tgt_lang="""ro_RO"""				,	freeze_encoder=A				,	freeze_embeds=A				,	)
  
          # test models whose config includes the extra_model_args
          _SCREAMING_SNAKE_CASE             =    BART_TINY
          _SCREAMING_SNAKE_CASE             =    self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE             =    args_d.copy()
          args_da.update(
              model_name_or_path=A				,	output_dir=A				,	)
          _SCREAMING_SNAKE_CASE             =    ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
          for p in extra_model_params:
              _SCREAMING_SNAKE_CASE             =    0.5
          _SCREAMING_SNAKE_CASE             =    argparse.Namespace(**A )
          _SCREAMING_SNAKE_CASE             =    main(A )
          for p in extra_model_params:
              assert getattr(model.config				,	A ) == 0.5, f'failed to override the model config for param {p}'
  
          # test models whose config doesn't include the extra_model_args
          _SCREAMING_SNAKE_CASE             =    T5_TINY
          _SCREAMING_SNAKE_CASE             =    self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE             =    args_d.copy()
          args_da.update(
              model_name_or_path=A				,	output_dir=A				,	)
          _SCREAMING_SNAKE_CASE             =    '''encoder_layerdrop'''
          _SCREAMING_SNAKE_CASE             =    0.5
          _SCREAMING_SNAKE_CASE             =    argparse.Namespace(**A )
          with pytest.raises(A ) as excinfo:
              _SCREAMING_SNAKE_CASE             =    main(A )
          assert str(excinfo.value ) == f'model config doesn\'t have a `{unsupported_param}` attribute'
  
  
  
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    CHEAP_ARGS.copy()
  
          _SCREAMING_SNAKE_CASE             =    '''summarization'''
          _SCREAMING_SNAKE_CASE             =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
  
          _SCREAMING_SNAKE_CASE             =    BART_TINY
          _SCREAMING_SNAKE_CASE             =    self.get_auto_remove_tmp_dir()
  
          args_d.update(
              data_dir=A				,	model_name_or_path=A				,	output_dir=A				,	tokenizer_name=A				,	train_batch_size=2				,	eval_batch_size=2				,	do_predict=A				,	task=A				,	src_lang="""en_XX"""				,	tgt_lang="""ro_RO"""				,	freeze_encoder=A				,	freeze_embeds=A				,	)
  
          # emulate finetune.py
          _SCREAMING_SNAKE_CASE             =    argparse.ArgumentParser()
          _SCREAMING_SNAKE_CASE             =    pl.Trainer.add_argparse_args(A )
          _SCREAMING_SNAKE_CASE             =    SummarizationModule.add_model_specific_args(A				,	os.getcwd() )
          _SCREAMING_SNAKE_CASE             =    {'''--help''': True}
  
          # --help test
          with pytest.raises(A ) as excinfo:
              with CaptureStdout() as cs:
                  _SCREAMING_SNAKE_CASE             =    parser.parse_args(A )
              assert False, "--help is expected to sys.exit"
          assert excinfo.type == SystemExit
          _SCREAMING_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
          _SCREAMING_SNAKE_CASE             =    '''non_existing_scheduler'''
          _SCREAMING_SNAKE_CASE             =    {f'--lr_scheduler={unsupported_param}'}
          with pytest.raises(A ) as excinfo:
              with CaptureStderr() as cs:
                  _SCREAMING_SNAKE_CASE             =    parser.parse_args(A )
              assert False, "invalid argument is expected to sys.exit"
          assert excinfo.type == SystemExit
          _SCREAMING_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
          _SCREAMING_SNAKE_CASE             =    '''cosine'''
          _SCREAMING_SNAKE_CASE             =    args_d.copy()
          _SCREAMING_SNAKE_CASE             =    supported_param
          _SCREAMING_SNAKE_CASE             =    argparse.Namespace(**A )
          _SCREAMING_SNAKE_CASE             =    main(A )
          assert (
              getattr(model.hparams				,	"""lr_scheduler""" ) == supported_param
          ), f'lr_scheduler={supported_param} shouldn\'t fail'
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					int = 10**6					)		->int:
  
      if n <= 0:
          raise ValueError("""Please enter an integer greater than 0"""					)
  
      _SCREAMING_SNAKE_CASE             =    list(range(n + 1					)					)
      for number in range(2      , n + 1					):
          if phi[number] == number:
              phi[number] -= 1
              for multiple in range(number * 2      , n + 1      , __lowerCamelCase					):
                  _SCREAMING_SNAKE_CASE             =    (phi[multiple] // number) * (number - 1)
  
      _SCREAMING_SNAKE_CASE             =    1
      for number in range(1      , n + 1					):
          if (answer / phi[answer]) < (number / phi[number]):
              _SCREAMING_SNAKE_CASE             =    number
  
      return answer
  
  
  if __name__ == "__main__":
        print(solution())
  
  
  
  