




'''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


snake_case__          =						getLogger(__name__)


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




def 						snake_case__    ( lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   str			,		lowerCamelCase__				:   str			,		lowerCamelCase__				:   int = 8			,		lowerCamelCase__				:   str = DEFAULT_DEVICE			,		lowerCamelCase__				:   Dict=False			,		lowerCamelCase__				:   int="summarization"			,		lowerCamelCase__				:   str=None			,		**lowerCamelCase__				:   Dict			,		)   ->   Dict:
					A_ :     Optional[int] =							Path(lowerCamelCase__			).open('''w'''			,		encoding='''utf-8'''			)
					A_ :     Optional[Any] =							str(lowerCamelCase__			)
					A_ :     Tuple =							AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			).to(lowerCamelCase__			)
					if fpaa:
										A_ :     Tuple =							model.half()

					A_ :     Dict =							AutoTokenizer.from_pretrained(lowerCamelCase__			)
					logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'			)  # if this is wrong, check config.model_type.

					A_ :     List[str] =							time.time()
					# update config with task specific params
					use_task_specific_params(lowerCamelCase__			,		lowerCamelCase__			)
					if prefix is None:
										A_ :     Tuple =							prefix or getattr(model.config			,		'''prefix'''			,		''''''			) or ''''''
					for examples_chunk in tqdm(list(chunks(lowerCamelCase__			,		lowerCamelCase__			)			)			):
										A_ :     Optional[Any] =							[prefix + text for text in examples_chunk]
										A_ :     int =							tokenizer(lowerCamelCase__			,		return_tensors='''pt'''			,		truncation=lowerCamelCase__			,		padding='''longest'''			).to(lowerCamelCase__			)
										A_ :     Dict =							model.generate(
										    input_ids=batch.input_ids			,		attention_mask=batch.attention_mask			,		**lowerCamelCase__			,		)
										A_ :     Union[str, 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_ :     Tuple =							int(time.time() - start_time			)  # seconds
					A_ :     int =							len(lowerCamelCase__			)
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,		4			)}




def 						snake_case__    ( )   ->   Optional[Any]:
					return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''			)




def 						snake_case__    ( lowerCamelCase__				:   Tuple=True			)   ->   List[Any]:
					A_ :     str =							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_ :     Any =							parser.parse_known_args()
					A_ :     str =							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_ :     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_ :     Union[str, 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_ :     Any =							calculate_bleu if '''translation''' in args.task else calculate_rouge
					A_ :     Union[str, Any] =							[x.rstrip() for x in open(args.save_path			).readlines()]
					A_ :     List[str] =							[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_ :     List[str] =							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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__          =						{"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__          =						[
							    """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NatForImageClassification""",
							    """NatModel""",
							    """NatPreTrainedModel""",
							    """NatBackbone""",
							]

if TYPE_CHECKING:
							from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nat import (
														    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NatBackbone,
														    NatForImageClassification,
														    NatModel,
														    NatPreTrainedModel,
														)

else:
							import sys

							snake_case__          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



