



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase__     =	getLogger(__name__)


lowerCamelCase__     =	"""cuda""" if torch.cuda.is_available() else """cpu"""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[str]			,       _SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     int = 8			,       _SCREAMING_SNAKE_CASE    :     str = DEFAULT_DEVICE			,       _SCREAMING_SNAKE_CASE    :     List[str]=False			,       _SCREAMING_SNAKE_CASE    :     Any="summarization"			,       _SCREAMING_SNAKE_CASE    :     int=None			,       **_SCREAMING_SNAKE_CASE    :     int			,       ):
				"""simple docstring"""







				__a					       =						Path(_SCREAMING_SNAKE_CASE      ).open("""w"""			,       encoding="""utf-8"""      )
				__a					       =						str(_SCREAMING_SNAKE_CASE      )
				__a					       =						AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE      ).to(_SCREAMING_SNAKE_CASE      )
				if fpaa:
								__a					       =						model.half()

				__a					       =						AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE      )
				logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"      )  # if this is wrong, check config.model_type.

				__a					       =						time.time()
				# update config with task specific params
				use_task_specific_params(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )
				if prefix is None:
								__a					       =						prefix or getattr(model.config			,       """prefix"""			,       """"""      ) or """"""
				for examples_chunk in tqdm(list(chunks(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )      )      ):
								__a					       =						[prefix + text for text in examples_chunk]
								__a					       =						tokenizer(_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""			,       truncation=_SCREAMING_SNAKE_CASE			,       padding="""longest"""      ).to(_SCREAMING_SNAKE_CASE      )
								__a					       =						model.generate(
								    input_ids=batch.input_ids			,       attention_mask=batch.attention_mask			,       **_SCREAMING_SNAKE_CASE			,       )
								__a					       =						tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,       skip_special_tokens=_SCREAMING_SNAKE_CASE			,       clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE      )
								for hypothesis in dec:
												fout.write(hypothesis + """\n"""      )
												fout.flush()
				fout.close()
				__a					       =						int(time.time() - start_time      )  # seconds
				__a					       =						len(_SCREAMING_SNAKE_CASE      )
				return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,       4      )}

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""      )

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any=True      ):
				"""simple docstring"""







				__a					       =						argparse.ArgumentParser()
				parser.add_argument("""model_name"""			,       type=_SCREAMING_SNAKE_CASE			,       help="""like facebook/bart-large-cnn,t5-base, etc."""      )
				parser.add_argument("""input_path"""			,       type=_SCREAMING_SNAKE_CASE			,       help="""like cnn_dm/test.source"""      )
				parser.add_argument("""save_path"""			,       type=_SCREAMING_SNAKE_CASE			,       help="""where to save summaries"""      )
				parser.add_argument("""--reference_path"""			,       type=_SCREAMING_SNAKE_CASE			,       required=_SCREAMING_SNAKE_CASE			,       help="""like cnn_dm/test.target"""      )
				parser.add_argument("""--score_path"""			,       type=_SCREAMING_SNAKE_CASE			,       required=_SCREAMING_SNAKE_CASE			,       default="""metrics.json"""			,       help="""where to save metrics"""      )
				parser.add_argument("""--device"""			,       type=_SCREAMING_SNAKE_CASE			,       required=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       help="""cuda, cuda:1, cpu etc."""      )
				parser.add_argument(
				    """--prefix"""			,       type=_SCREAMING_SNAKE_CASE			,       required=_SCREAMING_SNAKE_CASE			,       default=_SCREAMING_SNAKE_CASE			,       help="""will be added to the begininng of src examples"""      )
				parser.add_argument("""--task"""			,       type=_SCREAMING_SNAKE_CASE			,       default="""summarization"""			,       help="""used for task_specific_params + metrics"""      )
				parser.add_argument("""--bs"""			,       type=_SCREAMING_SNAKE_CASE			,       default=8			,       required=_SCREAMING_SNAKE_CASE			,       help="""batch size"""      )
				parser.add_argument(
				    """--n_obs"""			,       type=_SCREAMING_SNAKE_CASE			,       default=-1			,       required=_SCREAMING_SNAKE_CASE			,       help="""How many observations. Defaults to all."""      )
				parser.add_argument("""--fp16"""			,       action="""store_true"""      )
				parser.add_argument("""--dump-args"""			,       action="""store_true"""			,       help="""print the custom hparams with the results"""      )
				parser.add_argument(
				    """--info"""			,       nargs="""?"""			,       type=_SCREAMING_SNAKE_CASE			,       const=datetime_now()			,       help=(
				        """use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."""
				        """ lang=en-ru. If no value is passed, the current datetime string will be used."""
				    )			,       )
				# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
				__a       ,   __a					       =						parser.parse_known_args()
				__a					       =						parse_numeric_n_bool_cl_kwargs(_SCREAMING_SNAKE_CASE      )
				if parsed_args and verbose:
								print(f"parsed the following generate kwargs: {parsed_args}"      )
				with open(args.input_path      ) as f:
								__a					       =						[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
				if args.n_obs > 0:
								__a					       =						examples[: args.n_obs]
				Path(args.save_path      ).parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE      )
				if args.reference_path is None and Path(args.score_path      ).exists():
								warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c."      )
				__a					       =						generate_summaries_or_translations(
				    _SCREAMING_SNAKE_CASE			,       args.save_path			,       args.model_name			,       batch_size=args.bs			,       device=args.device			,       fpaa=args.fpaa			,       task=args.task			,       prefix=args.prefix			,       **_SCREAMING_SNAKE_CASE			,       )

				if args.reference_path is None:
								return {}

				# Compute scores
				__a					       =						calculate_bleu if """translation""" in args.task else calculate_rouge
				__a					       =						[x.rstrip() for x in open(args.save_path      ).readlines()]
				__a					       =						[x.rstrip() for x in open(args.reference_path      ).readlines()][: len(_SCREAMING_SNAKE_CASE      )]
				__a					       =						score_fn(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )
				scores.update(_SCREAMING_SNAKE_CASE      )

				if args.dump_args:
								scores.update(_SCREAMING_SNAKE_CASE      )
				if args.info:
								__a					       =						args.info

				if verbose:
								print(_SCREAMING_SNAKE_CASE      )

				if args.score_path is not None:
								json.dump(_SCREAMING_SNAKE_CASE			,       open(args.score_path			,       """w"""      )      )

				return scores


if __name__ == "__main__":
						# Usage for MT:
						# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
						run_generate(verbose=True)





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__  (							_SCREAMING_SNAKE_CASE    :     Dict      ):
				"""simple docstring"""







				__a					       =						filter(lambda _SCREAMING_SNAKE_CASE      : p.requires_grad			,       model.parameters()      )
				__a					       =						sum([np.prod(p.size()      ) for p in model_parameters]      )
				return params


lowerCamelCase__     =	logging.getLogger(__name__)






class 				SCREAMING_SNAKE_CASE     (							pl.Callback   ):





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict ,			__lowercase						:						str		):

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





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule ,			__lowercase						:						str ,			__lowercase						:						Tuple=True		):

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

											if not save_generations:
															return

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





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											try:
															__a					       =						pl_module.model.model.num_parameters()
											except AttributeError:
															__a					       =						pl_module.model.num_parameters()

											__a					       =						count_trainable_parameters(__lowercase		)
											# 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   UpperCamelCase_   (				self						:						Union[str, Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											return self._write_logs(__lowercase ,			__lowercase ,			"""test"""		)







							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						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__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple=1			,       _SCREAMING_SNAKE_CASE    :     List[str]=False      ):
				"""simple docstring"""







				if metric == "rouge2":
								__a					       =						"""{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								__a					       =						"""{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "loss":
								__a					       =						"""{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."""      )

				__a					       =						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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				return EarlyStopping(
				    monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       patience=_SCREAMING_SNAKE_CASE			,       verbose=_SCREAMING_SNAKE_CASE			,       )
