





'''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__							:							Any			=logging.getLogger()
a__							:							Union[str, Any]			=torch.cuda.is_available()
a__							:							str			={
    '''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     lowercase__       (       __lowercase						:  Path    ,  __lowercase						:  list					)			->				List[str]:



       """simple docstring"""






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


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


a__							:							Dict			=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def     lowercase__       (       __lowercase						:  Dict					)			->				Union[str, Any]:



       """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					(   __lowerCamelCase	):




 """simple docstring"""



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






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






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        __UpperCamelCase	=  {
            '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',
        }
        __UpperCamelCase	=  self._test_distiller_cli(__A							,     check_contents=__A     )

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

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

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

        __UpperCamelCase	=  nn.functional.log_softmax(__A							,     dim=-1     )
        __UpperCamelCase	,						__UpperCamelCase	=  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 	_lowerCamelCase (	self	:							Dict     ):
        __UpperCamelCase	=  {
            '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',
        }
        __UpperCamelCase	=  self._test_distiller_cli(__A							,     check_contents=__A     )
        assert model.model.config.model_type == "mbart"

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






 def 	_lowerCamelCase (	self	:							List[Any]     ):
        __UpperCamelCase	=  {
            '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 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  {
            'teacher': T5_TINY,
            'student': T5_TINIER,
            'model_name_or_path': T5_TINIER,
            'tokenizer_name': T5_TINIER,
        }
        self._test_distiller_cli(__A     )






 def 	_lowerCamelCase (	self	:							List[str]							,     __A	:							List[str]							,     __A	:							Union[str, Any]=True     ):
        __UpperCamelCase	=  {
            '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     )
        __UpperCamelCase	=  CHEAP_ARGS.copy()
        __UpperCamelCase	=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
        __UpperCamelCase	=  self.get_auto_remove_tmp_dir()

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

        __UpperCamelCase	=  load_json(model.metrics_save_path     )
        __UpperCamelCase	=  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     )
        __UpperCamelCase	=  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 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



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

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

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

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






 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  CHEAP_ARGS.copy()

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






 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  CHEAP_ARGS.copy()

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

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

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







'''simple docstring'''
def     lowercase__       (       __lowercase						:  int = 20					)			->				int:



       """simple docstring"""






       try:
              __UpperCamelCase	=  int(__lowercase					)
       except (TypeError, ValueError):
              raise TypeError('Parameter n must be int or castable to int.'					)
       if n <= 0:
              raise ValueError('Parameter n must be greater than or equal to one.'					)
       __UpperCamelCase	=  0
       while 1:
              i += n * (n - 1)
              __UpperCamelCase	=  0
              for j in range(2    ,  __lowercase					):
                     if i % j != 0:
                            __UpperCamelCase	=  1
                            break
              if nfound == 0:
                     if i == 0:
                            __UpperCamelCase	=  1
                     return i
       return None


if __name__ == "__main__":
    print(f'{solution() = }')
