
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-0_8,
    """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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Union[str, Any]:
			lowerCAmelCase__ : int   =						'\n'.join(SCREAMING_SNAKE_CASE_	)
			Path(SCREAMING_SNAKE_CASE_	).open('w'	).writelines(SCREAMING_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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Optional[Any]:
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(SCREAMING_SNAKE_CASE_			,				F'''{split}.source'''	)			,				SCREAMING_SNAKE_CASE_	)
						_dump_articles(os.path.join(SCREAMING_SNAKE_CASE_			,				F'''{split}.target'''	)			,				SCREAMING_SNAKE_CASE_	)
			return tmp_dir







class 	A__				(	__magic_name__      ):






				@classmethod
				def 				_lowerCamelCase						(		cls   : Dict  ):






							'''simple docstring'''







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






				@slow
				@require_torch_gpu
				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						list_models()
							lowerCAmelCase__ : int   =						'sshleifer'
							lowerCAmelCase__ : Optional[int]   =						[x.modelId for x in model_list if x.modelId.startswith(a  )]
							lowerCAmelCase__ : Tuple   =						['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
							lowerCAmelCase__ : Union[str, Any]   =						[]
							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   : Union[str, Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
							self._test_distiller_cli(a  )






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						{
							    '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',
							}
							lowerCAmelCase__ : List[Any]   =						self._test_distiller_cli(a       ,	check_contents=a  )

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

							lowerCAmelCase__ : Dict   =						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   : Optional[Any]  ):






							'''simple docstring'''







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

							lowerCAmelCase__ : Optional[int]   =						model(a       ,	attention_mask=a       ,	decoder_input_ids=a       ,	use_cache=a  ).logits

							lowerCAmelCase__ : List[Any]   =						nn.functional.log_softmax(a       ,	dim=-1  )
							lowerCAmelCase__ ,							lowerCAmelCase__ : Tuple   =						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   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						{
							    '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',
							}
							lowerCAmelCase__ : Any   =						self._test_distiller_cli(a       ,	check_contents=a  )
							assert model.model.config.model_type == "mbart"

							lowerCAmelCase__ : int   =						list(Path(model.output_dir  ).glob('*.ckpt'  )  )
							self.assertEqual(1       ,	len(a  )  )
							lowerCAmelCase__ : Optional[int]   =						list(Path(model.output_dir  ).glob('**/*.bin'  )  )
							lowerCAmelCase__ : Tuple   =						list(Path(model.output_dir  ).glob('best_tfmr/*'  )  )
							assert len(a  ) > 2
							self.assertEqual(len(a  )       ,	2  )






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						{
							    '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   : List[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						{
							    'teacher': T5_TINY,
							    'student': T5_TINIER,
							    'model_name_or_path': T5_TINIER,
							    'tokenizer_name': T5_TINIER,
							}
							self._test_distiller_cli(a  )






				def 				_lowerCamelCase						(		self   : Optional[Any]       ,	a   : List[str]       ,	a   : Any=True  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						{
							    '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  )
							lowerCAmelCase__ : dict   =						CHEAP_ARGS.copy()
							lowerCAmelCase__ : List[Any]   =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
							lowerCAmelCase__ : Dict   =						self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=a       ,	output_dir=a       ,	**a  )
							lowerCAmelCase__ : List[Any]   =						distill_main(argparse.Namespace(**a  )  )
							if not check_contents:
										return model
							lowerCAmelCase__ : Tuple   =						os.listdir(a  )
							lowerCAmelCase__ : List[str]   =						{os.path.basename(a  ) for p in contents}
							lowerCAmelCase__ : int   =						[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  )

							lowerCAmelCase__ : Optional[Any]   =						load_json(model.metrics_save_path  )
							lowerCAmelCase__ : Dict   =						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  )
							lowerCAmelCase__ : List[Any]   =						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 	A__				(	__magic_name__      ):






				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]       ,	)
				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : int  ):






							'''simple docstring'''







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

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

							lowerCAmelCase__ : Dict   =						module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
										lowerCAmelCase__ : Union[str, Any]   =						module.model.lm_head
										assert not lm_head.weight.requires_grad
										assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
										lowerCAmelCase__ : List[str]   =						module.model.model
										lowerCAmelCase__ : 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:
										lowerCAmelCase__ : List[str]   =						module.model.model
										lowerCAmelCase__ : Optional[Any]   =						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

							lowerCAmelCase__ : int   =						load_json(module.output_dir / 'text_batch.json'  )
							assert isinstance(a       ,	a  )
							assert len(a  ) >= 4






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : dict   =						CHEAP_ARGS.copy()

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






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : dict   =						CHEAP_ARGS.copy()

							lowerCAmelCase__ : Optional[int]   =						'summarization'
							lowerCAmelCase__ : List[str]   =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )

							lowerCAmelCase__ : Dict   =						BART_TINY
							lowerCAmelCase__ : List[str]   =						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
							lowerCAmelCase__ : Dict   =						argparse.ArgumentParser()
							lowerCAmelCase__ : List[Any]   =						pl.Trainer.add_argparse_args(a  )
							lowerCAmelCase__ : str   =						SummarizationModule.add_model_specific_args(a       ,	os.getcwd()  )
							lowerCAmelCase__ : Union[str, Any]   =						{'--help': True}

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

import math
from decimal import Decimal, getcontext
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_ = 4_000_000	)							->						int:

			try:
						lowerCAmelCase__ : str   =						int(SCREAMING_SNAKE_CASE_	)
			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.'	)
			lowerCAmelCase__ : Union[str, Any]   =						100
			lowerCAmelCase__ : List[Any]   =						(Decimal(5	) ** Decimal(0.5	) + 1) / Decimal(2	)

			lowerCAmelCase__ : Tuple   =						(math.floor(math.log(n * (phi + 2)			,				SCREAMING_SNAKE_CASE_	) - 1	) // 3) * 3 + 2
			lowerCAmelCase__ : Optional[int]   =						Decimal(round(phi ** Decimal(index + 1	)	)	) / (phi + 2)
			lowerCAmelCase__ : Tuple   =						num // 2
			return int(SCREAMING_SNAKE_CASE_	)


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