

"""simple docstring"""




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


A_							:     Optional[Any]     =	getLogger(__name__)


A_							:     Optional[int]     =	"cuda" if torch.cuda.is_available() else "cpu"

def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__ = 8						,			snake_case__ = DEFAULT_DEVICE						,			snake_case__=False						,			snake_case__="summarization"						,			snake_case__=None						,			**snake_case__						,			):




			'''simple docstring'''


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

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

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

def 		A    (       ):




			'''simple docstring'''


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

def 		A    (       snake_case__=True      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= argparse.ArgumentParser()
			parser.add_argument("""model_name"""						,			type=snake_case__						,			help="""like facebook/bart-large-cnn,t5-base, etc."""      )
			parser.add_argument("""input_path"""						,			type=snake_case__						,			help="""like cnn_dm/test.source"""      )
			parser.add_argument("""save_path"""						,			type=snake_case__						,			help="""where to save summaries"""      )
			parser.add_argument("""--reference_path"""						,			type=snake_case__						,			required=snake_case__						,			help="""like cnn_dm/test.target"""      )
			parser.add_argument("""--score_path"""						,			type=snake_case__						,			required=snake_case__						,			default="""metrics.json"""						,			help="""where to save metrics"""      )
			parser.add_argument("""--device"""						,			type=snake_case__						,			required=snake_case__						,			default=snake_case__						,			help="""cuda, cuda:1, cpu etc."""      )
			parser.add_argument(
			    """--prefix"""						,			type=snake_case__						,			required=snake_case__						,			default=snake_case__						,			help="""will be added to the begininng of src examples"""      )
			parser.add_argument("""--task"""						,			type=snake_case__						,			default="""summarization"""						,			help="""used for task_specific_params + metrics"""      )
			parser.add_argument("""--bs"""						,			type=snake_case__						,			default=8						,			required=snake_case__						,			help="""batch size"""      )
			parser.add_argument(
			    """--n_obs"""						,			type=snake_case__						,			default=-1						,			required=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=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
			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= parser.parse_known_args()
			SCREAMING_SNAKE_CASE__									= parse_numeric_n_bool_cl_kwargs(snake_case__      )
			if parsed_args and verbose:
						print(f"""parsed the following generate kwargs: {parsed_args}"""      )
			with open(args.input_path      ) as f:
						SCREAMING_SNAKE_CASE__									= [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
			if args.n_obs > 0:
						SCREAMING_SNAKE_CASE__									= examples[: args.n_obs]
			Path(args.save_path      ).parent.mkdir(exist_ok=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."""      )
			SCREAMING_SNAKE_CASE__									= generate_summaries_or_translations(
			    snake_case__						,			args.save_path						,			args.model_name						,			batch_size=args.bs						,			device=args.device						,			fpaa=args.fpaa						,			task=args.task						,			prefix=args.prefix						,			**snake_case__						,			)

			if args.reference_path is None:
						return {}

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

			if args.dump_args:
						scores.update(snake_case__      )
			if args.info:
						SCREAMING_SNAKE_CASE__									= args.info

			if verbose:
						print(snake_case__      )

			if args.score_path is not None:
						json.dump(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)






"""simple docstring"""




import importlib.metadata
import subprocess
import sys

def 		A    (       snake_case__ = False      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= False
			if "IPython" in sys.modules:
						SCREAMING_SNAKE_CASE__									= """google.colab""" in str(sys.modules["""IPython"""].get_ipython()      )

			if in_colab:
						if upgrade:
									SCREAMING_SNAKE_CASE__									= ["""pip""", """install""", """-U""", """torch"""]
									subprocess.run(snake_case__						,			check=snake_case__      )
						# get the current version of torch
						SCREAMING_SNAKE_CASE__									= importlib.metadata.version("""torch"""      )
						SCREAMING_SNAKE_CASE__									= torch_version[: torch_version.rindex("""."""      )]
						SCREAMING_SNAKE_CASE__									= f"""https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl"""
						SCREAMING_SNAKE_CASE__									= ["""pip""", """install""", xla_wheel]
						subprocess.run(snake_case__						,			check=snake_case__      )
			else:
						raise RuntimeError("""`install_xla` utility works only on google colab."""      )


