"""simple docstring"""


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


__UpperCamelCase       :    Optional[int]         =		torch.cuda.is_available()
__UpperCamelCase       :    Optional[Any]         =		[" Sam ate lunch today.", "Sams lunch ingredients."]
__UpperCamelCase       :    Any         =		["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__UpperCamelCase       :    Optional[int]         =		{
    "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": 4_2,
    "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": 1_2,
    "max_target_length": 1_2,
    "val_max_target_length": 1_2,
    "test_max_target_length": 1_2,
    "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   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       Union[str, Any]       ):
   _lowercase							     =       "\n".join(__lowerCAmelCase       )
   Path(__lowerCAmelCase       ).open("w"       ).writelines(__lowerCAmelCase       )


def   __UpperCAmelCase     (	_snake_case		:       List[str]       ):
   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   UpperCAmelCase_	(				UpperCAmelCase__   ):
    @classmethod
    def 		_lowerCamelCase      (	cls			:       Tuple		)						->    Optional[Any]:
       return cls
    @require_torch_multi_gpu
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    Any:
       _lowercase							     =       {
           "no_teacher": True,
           "freeze_encoder": True,
           "gpus": 2,
           "overwrite_output_dir": True,
           "sortish_sampler": True,
       }
       self._test_distiller_cli_fork(lowerCamelCase__   ,						check_contents=lowerCamelCase__		)




    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       str   ,						_lowercase			:       Optional[Any]=True		)						->    Optional[Any]:
       _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(lowerCamelCase__		)
       _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=lowerCamelCase__   ,						output_dir=lowerCamelCase__   ,						**lowerCamelCase__		)

       def convert(_lowercase			:       str   ,						_lowercase			:       str		):
          if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
             return ""
          if v is False or v is None:
             return ""
          if v is True:  # or len(str(v))==0:
             return f"""--{k}"""
          return f"""--{k}={v}"""

       _lowercase							     =       [x for x in (convert(lowerCamelCase__   ,						lowerCamelCase__		) for k, v in args_d.items()) if len(lowerCamelCase__		)]
       _lowercase							     =       [sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
       execute_subprocess_async(lowerCamelCase__   ,						env=self.get_env()		)

       _lowercase							     =       os.listdir(lowerCamelCase__		)
       _lowercase							     =       {os.path.basename(lowerCamelCase__		) for p in contents}
       _lowercase							     =       [p for p in contents if p.endswith("ckpt"		)]
       assert len(lowerCamelCase__		) > 0

       self.assertIn("test_generations.txt"   ,						lowerCamelCase__		)
       self.assertIn("test_results.txt"   ,						lowerCamelCase__		)

       # get the following from the module, (we don't have access to `model` here)
       _lowercase							     =       os.path.join(lowerCamelCase__   ,						"metrics.json"		)
       _lowercase							     =       "rouge2"

       _lowercase							     =       load_json(lowerCamelCase__		)
       # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
       print(lowerCamelCase__		)
       _lowercase							     =       metrics["val"][-1]
       self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]   ,						0.01		)
       self.assertIsInstance(last_step_stats[f"""val_avg_{val_metric}"""]   ,						lowerCamelCase__		)
       self.assertEqual(len(metrics["test"]		)   ,						1		)
       _lowercase							     =       int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
       self.assertEqual(len(metrics["val"]		)   ,						lowerCamelCase__		)



'''simple docstring'''


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 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Dict             =   filter(lambda __lowerCAmelCase : p.requires_grad   ,			model.parameters() )
		_UpperCAmelCase		:							List[Any]             =   sum([np.prod(p.size() ) for p in model_parameters] )
		return params


lowerCamelCase__     						=  logging.getLogger(__name__)






class 						lowerCAmelCase__      (      pl.Callback						):




			def 				lowerCAmelCase__     (		self	:  Optional[Any]						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Union[str, Any]			) ->List[Any]:



					'''simple docstring'''







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




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Any=True			) ->None:



					'''simple docstring'''







					logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
					_UpperCAmelCase		:							Tuple             =   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
					_UpperCAmelCase		:							Optional[int]             =   Path(pl_module.hparams.output_dir			)
					if type_path == "test":
							_UpperCAmelCase		:							Optional[Any]             =   od / "test_results.txt"
							_UpperCAmelCase		:							str             =   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.
							_UpperCAmelCase		:							Dict             =   od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
							_UpperCAmelCase		:							List[str]             =   od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
							results_file.parent.mkdir(exist_ok=lowerCamelCase__			)
							generations_file.parent.mkdir(exist_ok=lowerCamelCase__			)
					with open(lowerCamelCase__						,	"a+"			) as writer:
							for key in sorted(lowerCamelCase__			):
									if key in ["log", "progress_bar", "preds"]:
											continue
									_UpperCAmelCase		:							Union[str, Any]             =   metrics[key]
									if isinstance(lowerCamelCase__						,	torch.Tensor			):
											_UpperCAmelCase		:							int             =   val.item()
									_UpperCAmelCase		:							List[str]             =   F"""{key}: {val:.6f}\n"""
									writer.write(lowerCamelCase__			)

					if not save_generations:
							return

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




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[Any]						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  int			) ->Optional[int]:



					'''simple docstring'''







					try:
							_UpperCAmelCase		:							int             =   pl_module.model.model.num_parameters()
					except AttributeError:
							_UpperCAmelCase		:							Dict             =   pl_module.model.num_parameters()

					_UpperCAmelCase		:							Dict             =   count_trainable_parameters(lowerCamelCase__			)
					# 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 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule			) ->int:



					'''simple docstring'''







					save_json(pl_module.metrics						,	pl_module.metrics_save_path			)
					return self._write_logs(lowerCamelCase__						,	lowerCamelCase__						,	"test"			)






			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  Tuple			) ->Dict:



					'''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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=1   ,			__lowerCAmelCase=False ):
		if metric == "rouge2":
				_UpperCAmelCase		:							Tuple             =   "{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase		:							List[str]             =   "{val_avg_bleu:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase		:							Union[str, Any]             =   "{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." )

		_UpperCAmelCase		:							Optional[int]             =   ModelCheckpoint(
		    dirpath=__lowerCAmelCase   ,			filename=__lowerCAmelCase   ,			monitor=F"""val_{metric}"""   ,			mode="min" if "loss" in metric else "max"   ,			save_top_k=__lowerCAmelCase   ,			)
		return checkpoint_callback






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		return EarlyStopping(
		    monitor=F"""val_{metric}"""   ,			mode="min" if "loss" in metric else "max"   ,			patience=__lowerCAmelCase   ,			verbose=__lowerCAmelCase   ,			)



