
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)

SCREAMING_SNAKE_CASE__				=							logging.getLogger()
SCREAMING_SNAKE_CASE__				=							torch.cuda.is_available()
SCREAMING_SNAKE_CASE__				=							{
    """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  (						SCREAMING_SNAKE_CASE			: Path       ,				SCREAMING_SNAKE_CASE			: list     )	->		List[str]:
				__lowercase								=   '\n'.join(SCREAMING_SNAKE_CASE     )
				Path(SCREAMING_SNAKE_CASE     ).open('w'     ).writelines(SCREAMING_SNAKE_CASE     )


SCREAMING_SNAKE_CASE__				=							[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
SCREAMING_SNAKE_CASE__				=							["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
SCREAMING_SNAKE_CASE__				=							"""patrickvonplaten/t5-tiny-random"""
SCREAMING_SNAKE_CASE__				=							"""sshleifer/t5-tinier-random"""
SCREAMING_SNAKE_CASE__				=							"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__				=							"""sshleifer/tiny-mbart"""
SCREAMING_SNAKE_CASE__				=							"""sshleifer/tiny-marian-en-de"""
SCREAMING_SNAKE_CASE__				=							"""stas/tiny-wmt19-en-de"""


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




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		str:
				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__  (		lowerCAmelCase__							):





							@classmethod
							def      a__					(					cls    :  Dict							)		->						Tuple:



											"""simple docstring"""


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





							@slow
							@require_torch_gpu
							def      a__					(					self    :  Union[str, Any]							)		->						Union[str, Any]:



											"""simple docstring"""


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



											"""simple docstring"""


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





							def      a__					(					self    :  Tuple							)		->						Any:



											"""simple docstring"""


											__lowercase								=   {
											    '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								=   self._test_distiller_cli(_UpperCAmelCase	,					check_contents=_UpperCAmelCase							)

											__lowercase								=   list(Path(model.output_dir							).glob('*.ckpt'							)							)
											self.assertEqual(1	,					len(_UpperCAmelCase							)							)
											__lowercase								=   list(Path(model.output_dir							).glob('**/*.bin'							)							)
											self.assertEqual(len(_UpperCAmelCase							)	,					2							)
											__lowercase								=   lmap(str.strip	,					Path(model.hparams.data_dir							).joinpath('test.source'							).open().readlines()							)
											__lowercase								=   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								=   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      a__					(					self    :  int							)		->						str:



											"""simple docstring"""


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

											__lowercase								=   model(_UpperCAmelCase	,					attention_mask=_UpperCAmelCase	,					decoder_input_ids=_UpperCAmelCase	,					use_cache=_UpperCAmelCase							).logits

											__lowercase								=   nn.functional.log_softmax(_UpperCAmelCase	,					dim=-1							)
											__lowercase						,   __lowercase								=   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      a__					(					self    :  int							)		->						List[str]:



											"""simple docstring"""


											__lowercase								=   {
											    '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								=   self._test_distiller_cli(_UpperCAmelCase	,					check_contents=_UpperCAmelCase							)
											assert model.model.config.model_type == "mbart"

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





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



											"""simple docstring"""


											__lowercase								=   {
											    '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      a__					(					self    :  Union[str, Any]							)		->						Dict:



											"""simple docstring"""


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





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  Any	,					_UpperCAmelCase    :  int=True							)		->						Dict:



											"""simple docstring"""


											__lowercase								=   {
											    '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								=   CHEAP_ARGS.copy()
											__lowercase								=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
											__lowercase								=   self.get_auto_remove_tmp_dir()

											args_d.update(data_dir=_UpperCAmelCase	,					output_dir=_UpperCAmelCase	,					**_UpperCAmelCase							)
											__lowercase								=   distill_main(argparse.Namespace(**_UpperCAmelCase							)							)
											if not check_contents:
															return model
											__lowercase								=   os.listdir(_UpperCAmelCase							)
											__lowercase								=   {os.path.basename(_UpperCAmelCase							) for p in contents}
											__lowercase								=   [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								=   load_json(model.metrics_save_path							)
											__lowercase								=   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								=   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       A__  (		lowerCAmelCase__							):





							@parameterized.expand(
							    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]	,					)
							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[Any]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   CHEAP_ARGS.copy()
											__lowercase								=   'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
											__lowercase								=   0.1 if task == 'translation' else 0

											__lowercase								=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
											__lowercase								=   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								=   argparse.Namespace(**_UpperCAmelCase							)
											__lowercase								=   main(_UpperCAmelCase							)

											__lowercase								=   module.model.get_input_embeddings()
											assert not input_embeds.weight.requires_grad
											if model == T5_TINY:
															__lowercase								=   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								=   module.model.model
															__lowercase								=   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								=   module.model.model
															__lowercase								=   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								=   load_json(module.output_dir / 'text_batch.json'							)
											assert isinstance(_UpperCAmelCase	,					_UpperCAmelCase							)
											assert len(_UpperCAmelCase							) >= 4





							def      a__					(					self    :  List[Any]							)		->						Dict:



											"""simple docstring"""


											__lowercase								=   CHEAP_ARGS.copy()

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





							def      a__					(					self    :  Dict							)		->						Optional[int]:



											"""simple docstring"""


											__lowercase								=   CHEAP_ARGS.copy()

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

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

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







import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
											# Log results
											__lowercase								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   od / 'test_generations.txt'
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__lowercase								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

											if "preds" in metrics:
															__lowercase								=   '\n'.join(metrics['preds']							)
															generations_file.open('w+'							).write(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											try:
															__lowercase								=   pl_module.model.model.num_parameters()
											except AttributeError:
															__lowercase								=   pl_module.model.num_parameters()

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# mp stands for million parameters
											trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}							)





							@rank_zero_only
							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
								    ' this function.'     )

				__lowercase								=   ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





