"""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


_UpperCamelCase         =		getLogger(__name__)


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

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 8 ,     lowerCAmelCase = DEFAULT_DEVICE ,     lowerCAmelCase=False ,     lowerCAmelCase="summarization" ,     lowerCAmelCase=None ,     **lowerCAmelCase ,     ):
							A__					:    Tuple														= Path(lowerCAmelCase       ).open("""w""" ,     encoding="""utf-8"""       )
							A__					:    Dict														= str(lowerCAmelCase       )
							A__					:    Union[str, Any]														= AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase       ).to(lowerCAmelCase       )
							if fpaa:
														A__					:    Dict														= model.half()

							A__					:    Optional[int]														= AutoTokenizer.from_pretrained(lowerCAmelCase       )
							logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''       )  # if this is wrong, check config.model_type.

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

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

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

							if args.reference_path is None:
														return {}

							# Compute scores
							A__					:    List[str]														= calculate_bleu if """translation""" in args.task else calculate_rouge
							A__					:    Dict														= [x.rstrip() for x in open(args.save_path       ).readlines()]
							A__					:    Union[str, Any]														= [x.rstrip() for x in open(args.reference_path       ).readlines()][: len(lowerCAmelCase       )]
							A__					:    dict														= score_fn(lowerCAmelCase ,     lowerCAmelCase       )
							scores.update(lowerCAmelCase       )

							if args.dump_args:
														scores.update(lowerCAmelCase       )
							if args.info:
														A__					:    Tuple														= args.info

							if verbose:
														print(lowerCAmelCase       )

							if args.score_path is not None:
														json.dump(lowerCAmelCase ,     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"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							'opt'
			_UpperCamelCase     :	Optional[Any]				    =							['past_key_values']





			def __init__( self      ,			snake_case_=50_272      ,			snake_case_=768      ,			snake_case_=12      ,			snake_case_=3_072      ,			snake_case_=2_048      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=None      ,			snake_case_=0.1      ,			snake_case_=0.0      ,			snake_case_=12      ,			snake_case_="relu"      ,			snake_case_=0.0      ,			snake_case_=0.02      ,			snake_case_=True      ,			snake_case_=1      ,			snake_case_=2      ,			snake_case_=2      ,			snake_case_=True      ,			snake_case_=True      ,			**snake_case_      ,			):







										'''simple docstring'''


										super().__init__(
										    pad_token_id=snake_case_      ,			bos_token_id=snake_case_      ,			eos_token_id=snake_case_      ,			**snake_case_      ,			)
										A__					:    int														= vocab_size
										A__					:    List[str]														= max_position_embeddings
										A__					:    str														= num_attention_heads
										A__					:    Dict														= word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
										A__					:    Optional[int]														= ffn_dim
										A__					:    Optional[int]														= hidden_size
										A__					:    int														= num_hidden_layers
										A__					:    str														= dropout
										A__					:    Tuple														= attention_dropout
										A__					:    Optional[Any]														= activation_function
										A__					:    Any														= init_std
										A__					:    Optional[int]														= layerdrop
										A__					:    Optional[int]														= use_cache
										A__					:    int														= do_layer_norm_before
										# We keep these variables at `True` for backward compatibility.
										A__					:    str														= enable_bias
										A__					:    List[str]														= layer_norm_elementwise_affine

										# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
										# with checkpoints that have been fine-tuned before transformers v4.20.1
										# see https://github.com/facebookresearch/metaseq/pull/164
										A__					:    str														= _remove_final_layer_norm





