



'''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)

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 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Path ,       snake_case_    :     list  )		->str:






							'''simple docstring'''
							_lowercase							:		Tuple				=   '''\n'''.join(snake_case_  )
							Path(snake_case_  ).open('''w'''  ).writelines(snake_case_  )


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 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[str]  )		->Any:






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

class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		@classmethod
		def 						__lowercase      (						cls							:       List[str]		)      ->			Dict:







									'''simple docstring'''





									logging.disable(logging.CRITICAL		)  # remove noisy download output from tracebacks
									return cls
		@slow
		@require_torch_gpu
		def 						__lowercase      (						self							:       str		)      ->			Dict:







									'''simple docstring'''





									_lowercase							:		int				=   list_models()
									_lowercase							:		Optional[int]				=   '''sshleifer'''
									_lowercase							:		List[str]				=   [x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase_		)]
									_lowercase							:		Optional[int]				=   ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
									_lowercase							:		Optional[Any]				=   []
									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 						__lowercase      (						self							:       Optional[Any]		)      ->			int:







									'''simple docstring'''





									_lowercase							:		List[str]				=   {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
									self._test_distiller_cli(UpperCamelCase_		)
		def 						__lowercase      (						self							:       List[str]		)      ->			Optional[Any]:







									'''simple docstring'''





									_lowercase							:		List[str]				=   {
									    '''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							:		Union[str, Any]				=   self._test_distiller_cli(UpperCamelCase_      ,		check_contents=UpperCamelCase_		)

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

									_lowercase							:		List[str]				=   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 						__lowercase      (						self							:       List[Any]		)      ->			List[Any]:







									'''simple docstring'''





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

									_lowercase							:		Tuple				=   model(UpperCamelCase_      ,		attention_mask=UpperCamelCase_      ,		decoder_input_ids=UpperCamelCase_      ,		use_cache=UpperCamelCase_		).logits

									_lowercase							:		List[Any]				=   nn.functional.log_softmax(UpperCamelCase_      ,		dim=-1		)
									_lowercase			, _lowercase							:		Any				=   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 						__lowercase      (						self							:       Union[str, Any]		)      ->			Optional[Any]:







									'''simple docstring'''





									_lowercase							:		Tuple				=   {
									    '''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							:		Tuple				=   self._test_distiller_cli(UpperCamelCase_      ,		check_contents=UpperCamelCase_		)
									assert model.model.config.model_type == "mbart"

									_lowercase							:		Tuple				=   list(Path(model.output_dir		).glob('''*.ckpt'''		)		)
									self.assertEqual(1      ,		len(UpperCamelCase_		)		)
									_lowercase							:		int				=   list(Path(model.output_dir		).glob('''**/*.bin'''		)		)
									_lowercase							:		Dict				=   list(Path(model.output_dir		).glob('''best_tfmr/*'''		)		)
									assert len(UpperCamelCase_		) > 2
									self.assertEqual(len(UpperCamelCase_		)      ,		2		)
		def 						__lowercase      (						self							:       Tuple		)      ->			Optional[Any]:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   {
									    '''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 						__lowercase      (						self							:       Dict		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   {
									    '''teacher''': T5_TINY,
									    '''student''': T5_TINIER,
									    '''model_name_or_path''': T5_TINIER,
									    '''tokenizer_name''': T5_TINIER,
									}
									self._test_distiller_cli(UpperCamelCase_		)





		def 						__lowercase      (						self							:       str      ,		UpperCamelCase_							:       Union[str, Any]      ,		UpperCamelCase_							:       str=True		)      ->			List[str]:







									'''simple docstring'''





									_lowercase							:		List[str]				=   {
									    '''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_		)
									_lowercase							:		dict				=   CHEAP_ARGS.copy()
									_lowercase							:		Tuple				=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									_lowercase							:		Tuple				=   self.get_auto_remove_tmp_dir()

									args_d.update(data_dir=UpperCamelCase_      ,		output_dir=UpperCamelCase_      ,		**UpperCamelCase_		)
									_lowercase							:		Optional[Any]				=   distill_main(argparse.Namespace(**UpperCamelCase_		)		)
									if not check_contents:
																return model
									_lowercase							:		Optional[Any]				=   os.listdir(UpperCamelCase_		)
									_lowercase							:		Dict				=   {os.path.basename(UpperCamelCase_		) for p in contents}
									_lowercase							:		Union[str, Any]				=   [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_		)

									_lowercase							:		Tuple				=   load_json(model.metrics_save_path		)
									_lowercase							:		Optional[Any]				=   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}"]      ,		UpperCamelCase_		)
									_lowercase							:		str				=   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 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]      ,		)
		def 						__lowercase      (						self							:       Dict      ,		UpperCamelCase_							:       Optional[Any]		)      ->			Union[str, Any]:







									'''simple docstring'''





									_lowercase							:		dict				=   CHEAP_ARGS.copy()
									_lowercase							:		List[str]				=   '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
									_lowercase							:		List[Any]				=   0.1 if task == '''translation''' else 0

									_lowercase							:		int				=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									_lowercase							:		Optional[int]				=   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
									_lowercase							:		Union[str, Any]				=   argparse.Namespace(**UpperCamelCase_		)
									_lowercase							:		List[Any]				=   main(UpperCamelCase_		)

									_lowercase							:		List[str]				=   module.model.get_input_embeddings()
									assert not input_embeds.weight.requires_grad
									if model == T5_TINY:
																_lowercase							:		Dict				=   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							:		Dict				=   module.model.model
																_lowercase							:		List[Any]				=   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							:		Dict				=   module.model.model
																_lowercase							:		str				=   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							:		Any				=   load_json(module.output_dir / '''text_batch.json'''		)
									assert isinstance(UpperCamelCase_      ,		UpperCamelCase_		)
									assert len(UpperCamelCase_		) >= 4
		def 						__lowercase      (						self							:       Union[str, Any]		)      ->			List[Any]:







									'''simple docstring'''





									_lowercase							:		dict				=   CHEAP_ARGS.copy()

									_lowercase							:		Optional[int]				=   '''summarization'''
									_lowercase							:		Any				=   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
									_lowercase							:		Any				=   BART_TINY
									_lowercase							:		Any				=   self.get_auto_remove_tmp_dir()
									_lowercase							:		Any				=   args_d.copy()
									args_da.update(
									    model_name_or_path=UpperCamelCase_      ,		output_dir=UpperCamelCase_      ,		)
									_lowercase							:		List[Any]				=   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
									for p in extra_model_params:
																_lowercase							:		str				=   0.5
									_lowercase							:		Optional[Any]				=   argparse.Namespace(**UpperCamelCase_		)
									_lowercase							:		Optional[int]				=   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
									_lowercase							:		Optional[Any]				=   T5_TINY
									_lowercase							:		List[Any]				=   self.get_auto_remove_tmp_dir()
									_lowercase							:		Dict				=   args_d.copy()
									args_da.update(
									    model_name_or_path=UpperCamelCase_      ,		output_dir=UpperCamelCase_      ,		)
									_lowercase							:		str				=   '''encoder_layerdrop'''
									_lowercase							:		Union[str, Any]				=   0.5
									_lowercase							:		List[Any]				=   argparse.Namespace(**UpperCamelCase_		)
									with pytest.raises(UpperCamelCase_		) as excinfo:
																_lowercase							:		Optional[int]				=   main(UpperCamelCase_		)
									assert str(excinfo.value		) == F"model config doesn't have a `{unsupported_param}` attribute"





		def 						__lowercase      (						self							:       Tuple		)      ->			Tuple:







									'''simple docstring'''





									_lowercase							:		dict				=   CHEAP_ARGS.copy()

									_lowercase							:		Union[str, Any]				=   '''summarization'''
									_lowercase							:		Optional[Any]				=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)

									_lowercase							:		List[str]				=   BART_TINY
									_lowercase							:		Tuple				=   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
									_lowercase							:		Any				=   argparse.ArgumentParser()
									_lowercase							:		int				=   pl.Trainer.add_argparse_args(UpperCamelCase_		)
									_lowercase							:		List[str]				=   SummarizationModule.add_model_specific_args(UpperCamelCase_      ,		os.getcwd()		)
									_lowercase							:		Tuple				=   {'''--help''': True}

									# --help test
									with pytest.raises(UpperCamelCase_		) as excinfo:
																with CaptureStdout() as cs:
																							_lowercase							:		Tuple				=   parser.parse_args(UpperCamelCase_		)
																assert False, "--help is expected to sys.exit"
									assert excinfo.type == SystemExit
									_lowercase							:		Optional[Any]				=   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							:		Dict				=   '''non_existing_scheduler'''
									_lowercase							:		List[str]				=   {F"--lr_scheduler={unsupported_param}"}
									with pytest.raises(UpperCamelCase_		) as excinfo:
																with CaptureStderr() as cs:
																							_lowercase							:		List[Any]				=   parser.parse_args(UpperCamelCase_		)
																assert False, "invalid argument is expected to sys.exit"
									assert excinfo.type == SystemExit
									_lowercase							:		Union[str, Any]				=   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							:		int				=   '''cosine'''
									_lowercase							:		Dict				=   args_d.copy()
									_lowercase							:		Optional[Any]				=   supported_param
									_lowercase							:		List[str]				=   argparse.Namespace(**UpperCamelCase_		)
									_lowercase							:		List[str]				=   main(UpperCamelCase_		)
									assert (
									    getattr(model.hparams      ,		'''lr_scheduler'''		) == supported_param
									), F"lr_scheduler={supported_param} shouldn't fail"








'''simple docstring'''






def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[str]  )		->Dict:






							'''simple docstring'''
							_lowercase							:		Optional[Any]				=   [7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
							for gap in gaps:
														for i in range(snake_case_ ,       len(snake_case_  )  ):
																					_lowercase							:		int				=   collection[i]
																					_lowercase							:		Any				=   i
																					while j >= gap and collection[j - gap] > insert_value:
																												_lowercase							:		Optional[int]				=   collection[j - gap]
																												j -= gap
																					if j != i:
																												_lowercase							:		str				=   insert_value
							return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						lowerCamelCase__	  =		input('Enter numbers separated by a comma:\n').strip()
						lowerCamelCase__	  =		[int(item) for item in user_input.split(',')]
						print(shell_sort(unsorted))



