






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					    =	logging.getLogger()
lowerCamelCase					    =	torch.cuda.is_available()
lowerCamelCase					    =	{
    '''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 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



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


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


lowerCamelCase					    =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



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

class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   @classmethod
   def       SCREAMING_SNAKE_CASE__					(     cls:						int						)	->		Optional[Any]:


       """simple docstring"""



       logging.disable(logging.CRITICAL						)  # remove noisy download output from tracebacks
       return cls

   @slow
   @require_torch_gpu
   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		Any:


       """simple docstring"""



       _lowercase     			=						list_models()
       _lowercase     			=						'sshleifer'
       _lowercase     			=						[x.modelId for x in model_list if x.modelId.startswith(snake_case__						)]
       _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(snake_case__						)
           except Exception:
               failures.append(snake_case__						)
       assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''

   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
       self._test_distiller_cli(snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		int:


       """simple docstring"""



       _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(snake_case__ ,							check_contents=snake_case__						)

       _lowercase     			=						list(Path(model.output_dir						).glob('*.ckpt'						)						)
       self.assertEqual(1 ,							len(snake_case__						)						)
       _lowercase     			=						list(Path(model.output_dir						).glob('**/*.bin'						)						)
       self.assertEqual(len(snake_case__						) ,							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(snake_case__ ,							snake_case__ ,							str(model.output_dir / 'best_tfmr'						)						)
       self.assertTrue(Path(snake_case__						).exists()						)

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

   def       SCREAMING_SNAKE_CASE__					(     self:						List[Any]						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						AutoModelForSeqaSeqLM.from_pretrained(snake_case__						)
       _lowercase			,     _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(
           snake_case__ ,							attention_mask=snake_case__ ,							decoder_input_ids=snake_case__ ,							labels=snake_case__ ,							use_cache=snake_case__						).loss

       _lowercase     			=						model(snake_case__ ,							attention_mask=snake_case__ ,							decoder_input_ids=snake_case__ ,							use_cache=snake_case__						).logits

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

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		List[str]:


       """simple docstring"""



       _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(snake_case__ ,							check_contents=snake_case__						)
       assert model.model.config.model_type == "mbart"

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

   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		Tuple:


       """simple docstring"""



       _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(snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		int:


       """simple docstring"""



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

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


       """simple docstring"""



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

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

       _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}'''] ,							snake_case__						)
       _lowercase     			=						int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1						)
       self.assertEqual(len(metrics['val']						) ,							snake_case__						)
       self.assertEqual(len(metrics['test']						) ,							1						)
       return model






class        snake_case_				(							lowercase_   ):







   """simple docstring"""



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


       """simple docstring"""



       _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=snake_case__ ,							model_name_or_path=snake_case__ ,							tokenizer_name=snake_case__ ,							train_batch_size=2 ,							eval_batch_size=2 ,							output_dir=snake_case__ ,							do_predict=snake_case__ ,							task=snake_case__ ,							src_lang='en_XX' ,							tgt_lang='ro_RO' ,							freeze_encoder=snake_case__ ,							freeze_embeds=snake_case__ ,							)
       assert "n_train" in args_d
       _lowercase     			=						argparse.Namespace(**snake_case__						)
       _lowercase     			=						main(snake_case__						)

       _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(snake_case__ ,							snake_case__						)
       assert len(snake_case__						) >= 4

   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Any:


       """simple docstring"""



       _lowercase     			=						CHEAP_ARGS.copy()

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

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

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

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Any:


       """simple docstring"""



       _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=snake_case__ ,							model_name_or_path=snake_case__ ,							output_dir=snake_case__ ,							tokenizer_name=snake_case__ ,							train_batch_size=2 ,							eval_batch_size=2 ,							do_predict=snake_case__ ,							task=snake_case__ ,							src_lang='en_XX' ,							tgt_lang='ro_RO' ,							freeze_encoder=snake_case__ ,							freeze_embeds=snake_case__ ,							)

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

       # --help test
       with pytest.raises(snake_case__						) as excinfo:
           with CaptureStdout() as cs:
               _lowercase     			=						parser.parse_args(snake_case__						)
           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(snake_case__						) as excinfo:
           with CaptureStderr() as cs:
               _lowercase     			=						parser.parse_args(snake_case__						)
           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(**snake_case__						)
       _lowercase     			=						main(snake_case__						)
       assert (
           getattr(model.hparams ,							'lr_scheduler'						) == supported_param
       ), f'''lr_scheduler={supported_param} shouldn\'t fail'''








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCamelCase					    =	{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50Tokenizer''']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50TokenizerFast''']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa import MBartaaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
   import sys

   lowerCamelCase					    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
