






"""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							:						int   							=			logging.getLogger()
__a							:						Tuple   							=			torch.cuda.is_available()
__a							:						Optional[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 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_):
					a__ 							=	'''\n'''.join(lowerCamelCase_)
					Path(lowerCamelCase_).open('''w''').writelines(lowerCamelCase_)


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


__a							:						List[str]   							=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					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 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							@classmethod
							def 			lowercase		( cls:  Optional[Any]     ):

												'''simple docstring'''

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




							@slow
							@require_torch_gpu
							def 			lowercase		( self:  Union[str, Any]     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												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(__A					,		check_contents=__A     )

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

												a__ 							=	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 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

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

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

												a__ 							=	nn.functional.log_softmax(__A					,		dim=-1     )
												a__							,a__ 							=	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 			lowercase		( self:  str     ):

												'''simple docstring'''

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

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




							def 			lowercase		( self:  List[Any]     ):

												'''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(__A     )




							def 			lowercase		( self:  int     ):

												'''simple docstring'''

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





							def 			lowercase		( self:  Optional[int]					,		__A:  Any					,		__A:  Tuple=True     ):

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

												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}']					,		__A     )
												a__ 							=	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 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							@parameterized.expand(
							    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]					,		)
							def 			lowercase		( self:  Tuple					,		__A:  Optional[Any]     ):

												'''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=__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
												a__ 							=	argparse.Namespace(**__A     )
												a__ 							=	main(__A     )

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




							def 			lowercase		( self:  List[str]     ):

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





							def 			lowercase		( self:  str     ):

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

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










"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterator
from typing import Any







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Optional[int]					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	data
												a__ 							=	None





							def __str__( self:  str     ):

												'''simple docstring'''

												return F'{self.data}'







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  int     ):

												'''simple docstring'''

												a__ 							=	None
												a__ 							=	None




							def __iter__( self:  Tuple     ):

												'''simple docstring'''

												a__ 							=	self.front
												while node:
																	yield node.data
																	a__ 							=	node.next




							def __len__( self:  Union[str, Any]     ):

												'''simple docstring'''

												return len(tuple(iter(self     )     )     )




							def __str__( self:  Any     ):

												'''simple docstring'''

												return " <- ".join(str(__A     ) for item in self     )




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												return len(self     ) == 0




							def 			lowercase		( self:  Tuple					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	Node(__A     )
												if self.is_empty():
																	a__ 							=	a__ 							=	node
												else:
																	assert isinstance(self.rear					,		__A     )
																	a__ 							=	node
																	a__ 							=	node




							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												if self.is_empty():
																	raise IndexError('''dequeue from empty queue'''     )
												assert isinstance(self.front					,		__A     )
												a__ 							=	self.front
												a__ 							=	self.front.next
												if self.front is None:
																	a__ 							=	None
												return node.data





							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												a__ 							=	a__ 							=	None


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

	testmod()


