





'''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


_SCREAMING_SNAKE_CASE					=    getLogger(__name__)


_SCREAMING_SNAKE_CASE					=    '''cuda''' if torch.cuda.is_available() else '''cpu'''




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

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

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




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




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]=True				):
			__lowercase										=							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
			__lowercase						,		__lowercase										=							parser.parse_known_args()
			__lowercase										=							parse_numeric_n_bool_cl_kwargs(__SCREAMING_SNAKE_CASE				)
			if parsed_args and verbose:
						print(f"parsed the following generate kwargs: {parsed_args}"				)
			__lowercase										=							[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
			if args.n_obs > 0:
						__lowercase										=							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."				)

			if args.device == "cpu" and args.fpaa:
						# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
						raise ValueError('''Can\'t mix --fp16 and --device cpu'''				)

			__lowercase										=							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
			__lowercase										=							calculate_bleu if '''translation''' in args.task else calculate_rouge
			__lowercase										=							[x.rstrip() for x in open(args.save_path				).readlines()]
			__lowercase										=							[x.rstrip() for x in open(args.reference_path				).readlines()][: len(__SCREAMING_SNAKE_CASE				)]
			__lowercase										=							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:
						__lowercase										=							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)






'''simple docstring'''



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = "▁"     ,_lowerCamelCase = True     ,_lowerCamelCase = "<unk>"     ,_lowerCamelCase = "</s>"     ,_lowerCamelCase = "<pad>"     ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							{
				    '''pad''': {'''id''': 0, '''token''': pad_token},
				    '''eos''': {'''id''': 1, '''token''': eos_token},
				    '''unk''': {'''id''': 2, '''token''': unk_token},
				}

				__lowercase										=							[None] * len(self.special_tokens )
				for token_dict in self.special_tokens.values():
							__lowercase										=							token_dict['''token''']

				__lowercase										=							Tokenizer(Unigram() )

				__lowercase										=							normalizers.Sequence(
				    [
				        normalizers.Nmt(),
				        normalizers.NFKC(),
				        normalizers.Replace(Regex(''' {2,}''' )     ,''' ''' ),
				        normalizers.Lowercase(),
				    ] )
				__lowercase										=							pre_tokenizers.Sequence(
				    [
				        pre_tokenizers.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase ),
				        pre_tokenizers.Digits(individual_digits=_lowerCamelCase ),
				        pre_tokenizers.Punctuation(),
				    ] )
				__lowercase										=							decoders.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase )

				__lowercase										=							TemplateProcessing(
				    single=f"$A {self.special_tokens['eos']['token']}"     ,special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]     ,)

				__lowercase										=							{
				    '''model''': '''SentencePieceUnigram''',
				    '''replacement''': replacement,
				    '''add_prefix_space''': add_prefix_space,
				}

				super().__init__(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							__lowercase										=							[files]
				self._tokenizer.train(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				self._tokenizer.train_from_iterator(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							json.loads(self._tokenizer.to_str() )

				__lowercase										=							self.special_tokens['''unk''']['''id''']

				__lowercase										=							Tokenizer.from_str(json.dumps(_lowerCamelCase ) )





