
"""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		:					str    	=					logging.getLogger()
a		:					Optional[int]    	=					torch.cuda.is_available()
a		:					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 						__magic_name__		(				UpperCamelCase    :		Path       ,		UpperCamelCase    :		list    )    ->	Optional[int]:
    a__    = '\n'.join(UpperCamelCase    )
    Path(UpperCamelCase    ).open('w'    ).writelines(UpperCamelCase    )


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


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



def 						__magic_name__		(				UpperCamelCase    :		Tuple    )    ->	Optional[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 						lowercase(_lowercase					):



     @classmethod
     def  lowercase__     (       cls      )		->    Union[str, Any]:







         """simple docstring"""



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



     @slow
     @require_torch_gpu
     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



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



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



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



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







         """simple docstring"""



         a__    = {
             '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',
         }
         a__    = self._test_distiller_cli(__SCREAMING_SNAKE_CASE    ,							check_contents=__SCREAMING_SNAKE_CASE      )

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

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



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



         a__    = AutoModelForSeqaSeqLM.from_pretrained(__SCREAMING_SNAKE_CASE      )
         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(
             __SCREAMING_SNAKE_CASE    ,							attention_mask=__SCREAMING_SNAKE_CASE    ,							decoder_input_ids=__SCREAMING_SNAKE_CASE    ,							labels=__SCREAMING_SNAKE_CASE    ,							use_cache=__SCREAMING_SNAKE_CASE      ).loss

         a__    = model(__SCREAMING_SNAKE_CASE    ,							attention_mask=__SCREAMING_SNAKE_CASE    ,							decoder_input_ids=__SCREAMING_SNAKE_CASE    ,							use_cache=__SCREAMING_SNAKE_CASE      ).logits

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



     def  lowercase__     (       self      )		->    Tuple:







         """simple docstring"""



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

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



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







         """simple docstring"""



         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(__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self      )		->    Optional[Any]:







         """simple docstring"""



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




     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=True      )		->    Union[str, Any]:







         """simple docstring"""



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

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

         a__    = load_json(model.metrics_save_path      )
         a__    = 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}']    ,							__SCREAMING_SNAKE_CASE      )
         a__    = int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1      )
         self.assertEqual(len(metrics['val']      )    ,							__SCREAMING_SNAKE_CASE      )
         self.assertEqual(len(metrics['test']      )    ,							1      )
         return model







class 						lowercase(_lowercase					):



     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,							)
     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



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

         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(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         assert len(__SCREAMING_SNAKE_CASE      ) >= 4



     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



         a__    = CHEAP_ARGS.copy()

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

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

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




     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



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

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

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




"""simple docstring"""




from itertools import combinations, product



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int    )    ->	bool:
    if xa == ya == 0 or xa == ya == 0:
        return False
    a__    = xa * xa + ya * ya
    a__    = xa * xa + ya * ya
    a__    = (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
    return (
        a_square + b_square == c_square
        or a_square + c_square == b_square
        or b_square + c_square == a_square
    )



def 						__magic_name__		(				UpperCamelCase    :		int = 50    )    ->	int:
    return sum(
        1
        for pta, pta in combinations(product(range(limit + 1    )       ,		repeat=2    )       ,		2    )
        if is_right(*UpperCamelCase       ,		*UpperCamelCase    )    )


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


