





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)

snake_case__      :							str									=  logging.getLogger()
snake_case__      :							List[str]									=  torch.cuda.is_available()
snake_case__      :							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 _a       (			lowerCamelCase:     Path      ,       lowerCamelCase:     list				)			->		Any:







      '''simple docstring'''






      __A      =       '''\n'''.join(lowerCamelCase				)
      Path(lowerCamelCase				).open('''w'''				).writelines(lowerCamelCase				)


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


snake_case__      :							Optional[Any]									=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



def _a       (			lowerCamelCase:     Optional[Any]				)			->		Optional[Any]:







      '''simple docstring'''






      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 A_      (    _lowerCamelCase     ):




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




   @slow
   @require_torch_gpu
   def _lowerCAmelCase   (self			:List[str]				)->      List[str]:
         __A      =       list_models()
         __A      =       '''sshleifer'''
         __A      =       [x.modelId for x in model_list if x.modelId.startswith(_UpperCamelCase				)]
         __A      =       ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
         __A      =       []
         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 _lowerCAmelCase   (self			:Optional[int]				)->      List[Any]:
         __A      =       {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
         self._test_distiller_cli(_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Tuple				)->      Optional[int]:
         __A      =       {
             '''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''',
         }
         __A      =       self._test_distiller_cli(_UpperCamelCase					,     check_contents=_UpperCamelCase				)

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

         __A      =       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 _lowerCAmelCase   (self			:Union[str, Any]				)->      Any:
         __A      =       AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase				)
         __A     ,		__A      =       model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
         __A      =       torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]					,     dtype=torch.long					,     device=model.device				)
         __A      =       target_ids[:, :-1].contiguous()  # Why this line?
         __A      =       target_ids[:, 1:].clone()  # why clone?
         __A      =       model(
             _UpperCamelCase					,     attention_mask=_UpperCamelCase					,     decoder_input_ids=_UpperCamelCase					,     labels=_UpperCamelCase					,     use_cache=_UpperCamelCase				).loss

         __A      =       model(_UpperCamelCase					,     attention_mask=_UpperCamelCase					,     decoder_input_ids=_UpperCamelCase					,     use_cache=_UpperCamelCase				).logits

         __A      =       nn.functional.log_softmax(_UpperCamelCase					,     dim=-1				)
         __A     ,		__A      =       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 _lowerCAmelCase   (self			:List[str]				)->      Optional[int]:
         __A      =       {
             '''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''',
         }
         __A      =       self._test_distiller_cli(_UpperCamelCase					,     check_contents=_UpperCamelCase				)
         assert model.model.config.model_type == "mbart"

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




   def _lowerCAmelCase   (self			:Optional[int]				)->      Tuple:
         __A      =       {
             '''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 _lowerCAmelCase   (self			:Optional[Any]				)->      Optional[int]:
         __A      =       {
             '''teacher''': T5_TINY,
             '''student''': T5_TINIER,
             '''model_name_or_path''': T5_TINIER,
             '''tokenizer_name''': T5_TINIER,
         }
         self._test_distiller_cli(_UpperCamelCase				)






   def _lowerCAmelCase   (self			:List[Any]					,     _UpperCamelCase			:Tuple					,     _UpperCamelCase			:Optional[int]=True				)->      int:
         __A      =       {
             '''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				)
         __A      =       CHEAP_ARGS.copy()
         __A      =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
         __A      =       self.get_auto_remove_tmp_dir()

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

         __A      =       load_json(model.metrics_save_path				)
         __A      =       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}"""]					,     _UpperCamelCase				)
         __A      =       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 A_      (    _lowerCamelCase     ):




   @parameterized.expand(
       [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					,     )
   def _lowerCAmelCase   (self			:Dict					,     _UpperCamelCase			:List[str]				)->      str:
         __A      =       CHEAP_ARGS.copy()
         __A      =       '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
         __A      =       0.1 if task == '''translation''' else 0

         __A      =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
         __A      =       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
         __A      =       argparse.Namespace(**_UpperCamelCase				)
         __A      =       main(_UpperCamelCase				)

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

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




   def _lowerCAmelCase   (self			:Optional[int]				)->      Union[str, Any]:
         __A      =       CHEAP_ARGS.copy()

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






   def _lowerCAmelCase   (self			:Optional[Any]				)->      List[Any]:
         __A      =       CHEAP_ARGS.copy()

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

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

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












import unittest
from timeit import timeit



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      __A      =       first_num if first_num >= second_num else second_num
      __A      =       max_num
      while (common_mult % first_num > 0) or (common_mult % second_num > 0):
            common_mult += max_num
      return common_mult



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      return b if a == 0 else greatest_common_divisor(b % a      ,       lowerCamelCase				)



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      return first_num // greatest_common_divisor(lowerCamelCase      ,       lowerCamelCase				) * second_num



def _a       (			)			->		Tuple:







      '''simple docstring'''






      __A      =       (
          '''from __main__ import least_common_multiple_slow, least_common_multiple_fast'''
      )
      print(
          '''least_common_multiple_slow():'''      ,       timeit('''least_common_multiple_slow(1000, 999)'''      ,       setup=lowerCamelCase				)      ,       )
      print(
          '''least_common_multiple_fast():'''      ,       timeit('''least_common_multiple_fast(1000, 999)'''      ,       setup=lowerCamelCase				)      ,       )





class A_      (    unittest.TestCase     ):
   lowerCAmelCase__       =				(
       (10, 20),
       (13, 15),
       (4, 31),
       (10, 42),
       (43, 34),
       (5, 12),
       (12, 25),
       (10, 25),
       (6, 9),
   )
   lowerCAmelCase__       =				(20, 195, 124, 210, 1462, 60, 300, 50, 18)




   def _lowerCAmelCase   (self			:List[str]				)->      Union[str, Any]:
         for i, (first_num, second_num) in enumerate(self.test_inputs				):
               __A      =       least_common_multiple_slow(_UpperCamelCase					,     _UpperCamelCase				)
               __A      =       least_common_multiple_fast(_UpperCamelCase					,     _UpperCamelCase				)
               with self.subTest(i=_UpperCamelCase				):
                     self.assertEqual(_UpperCamelCase					,     self.expected_results[i]				)
                     self.assertEqual(_UpperCamelCase					,     self.expected_results[i]				)


if __name__ == "__main__":
       benchmark()
       unittest.main()





