
"""simple docstring"""







import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast




@dataclass
class 			A__	(     datasets.BuilderConfig):
		A_						:	Optional[datasets.Features]           =     None




class 			A__	(     datasets.ArrowBasedBuilder):
		A_						:	Dict           =     PandasConfig

		def 							__lowerCamelCase							(				self			):
			return datasets.DatasetInfo(features=self.config.features			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if not self.config.data_files:
				raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"			)
			__lowerCAmelCase      :						Tuple						      =				dl_manager.download_and_extract(self.config.data_files			)
			if isinstance(_SCREAMING_SNAKE_CASE	,		(str, list, tuple)			):
				__lowerCAmelCase      :						Tuple						      =				data_files
				if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
					__lowerCAmelCase      :						List[str]						      =				[files]
				# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
				__lowerCAmelCase      :						int						      =				[dl_manager.iter_files(_SCREAMING_SNAKE_CASE			) for file in files]
				return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,		gen_kwargs={'files': files}			)]
			__lowerCAmelCase      :						List[str]						      =				[]
			for split_name, files in data_files.items():
				if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
					__lowerCAmelCase      :						int						      =				[files]
				# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
				__lowerCAmelCase      :						str						      =				[dl_manager.iter_files(_SCREAMING_SNAKE_CASE			) for file in files]
				splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE	,		gen_kwargs={'files': files}			)			)
			return splits

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if self.config.features is not None:
				# more expensive cast to support nested features with keys in a different order
				# allows str <-> int/float or str to Audio for example
				__lowerCAmelCase      :						List[str]						      =				table_cast(_SCREAMING_SNAKE_CASE	,		self.config.features.arrow_schema			)
			return pa_table







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			for i, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE			)			):
				with open(_SCREAMING_SNAKE_CASE	,		'rb'			) as f:
					__lowerCAmelCase      :						List[str]						      =				pa.Table.from_pandas(pd.read_pickle(_SCREAMING_SNAKE_CASE			)			)
					yield i, self._cast_table(_SCREAMING_SNAKE_CASE			)

"""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


lowerCamelCase__        =		getLogger(__name__)


lowerCamelCase__        =		"""cuda""" if torch.cuda.is_available() else """cpu"""
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = 8	,  _UpperCamelCase = DEFAULT_DEVICE	,  _UpperCamelCase=False	,  _UpperCamelCase="summarization"	,  _UpperCamelCase=None	,  **_UpperCamelCase	,  ):
	__lowerCAmelCase      :						str						      =				Path(_UpperCamelCase		).open('w'	,  encoding='utf-8'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				str(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase		).to(_UpperCamelCase		)
	if fpaa:
		__lowerCAmelCase      :						Optional[Any]						      =				model.half()

	__lowerCAmelCase      :						List[str]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

	__lowerCAmelCase      :						List[Any]						      =				time.time()
	# update config with task specific params
	use_task_specific_params(_UpperCamelCase	,  _UpperCamelCase		)
	if prefix is None:
		__lowerCAmelCase      :						Optional[int]						      =				prefix or getattr(model.config	,  'prefix'	,  ''		) or ''
	for examples_chunk in tqdm(list(chunks(_UpperCamelCase	,  _UpperCamelCase		)		)		):
		__lowerCAmelCase      :						List[str]						      =				[prefix + text for text in examples_chunk]
		__lowerCAmelCase      :						List[str]						      =				tokenizer(_UpperCamelCase	,  return_tensors='pt'	,  truncation=_UpperCamelCase	,  padding='longest'		).to(_UpperCamelCase		)
		__lowerCAmelCase      :						str						      =				model.generate(
		    input_ids=batch.input_ids	,  attention_mask=batch.attention_mask	,  **_UpperCamelCase	,  )
		__lowerCAmelCase      :						str						      =				tokenizer.batch_decode(_UpperCamelCase	,  skip_special_tokens=_UpperCamelCase	,  clean_up_tokenization_spaces=_UpperCamelCase		)
		for hypothesis in dec:
			fout.write(hypothesis + '\n'		)
			fout.flush()
	fout.close()
	__lowerCAmelCase      :						Optional[int]						      =				int(time.time() - start_time		)  # seconds
	__lowerCAmelCase      :						List[Any]						      =				len(_UpperCamelCase		)
	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    (_UpperCamelCase=True		):
	__lowerCAmelCase      :						List[Any]						      =				argparse.ArgumentParser()
	parser.add_argument('model_name'	,  type=_UpperCamelCase	,  help='like facebook/bart-large-cnn,t5-base, etc.'		)
	parser.add_argument('input_path'	,  type=_UpperCamelCase	,  help='like cnn_dm/test.source'		)
	parser.add_argument('save_path'	,  type=_UpperCamelCase	,  help='where to save summaries'		)
	parser.add_argument('--reference_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  help='like cnn_dm/test.target'		)
	parser.add_argument('--score_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default='metrics.json'	,  help='where to save metrics'		)
	parser.add_argument('--device'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='cuda, cuda:1, cpu etc.'		)
	parser.add_argument(
	    '--prefix'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='will be added to the begininng of src examples'		)
	parser.add_argument('--task'	,  type=_UpperCamelCase	,  default='summarization'	,  help='used for task_specific_params + metrics'		)
	parser.add_argument('--bs'	,  type=_UpperCamelCase	,  default=8	,  required=_UpperCamelCase	,  help='batch size'		)
	parser.add_argument(
	    '--n_obs'	,  type=_UpperCamelCase	,  default=-1	,  required=_UpperCamelCase	,  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=_UpperCamelCase	,  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
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				parser.parse_known_args()
	__lowerCAmelCase      :						Optional[int]						      =				parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"		)
	__lowerCAmelCase      :						Dict						      =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path		).readlines()]
	if args.n_obs > 0:
		__lowerCAmelCase      :						int						      =				examples[: args.n_obs]
	Path(args.save_path		).parent.mkdir(exist_ok=_UpperCamelCase		)

	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'		)

	__lowerCAmelCase      :						Optional[Any]						      =				generate_summaries_or_translations(
	    _UpperCamelCase	,  args.save_path	,  args.model_name	,  batch_size=args.bs	,  device=args.device	,  fpaa=args.fpaa	,  task=args.task	,  prefix=args.prefix	,  **_UpperCamelCase	,  )

	if args.reference_path is None:
		return {}

	# Compute scores
	__lowerCAmelCase      :						str						      =				calculate_bleu if 'translation' in args.task else calculate_rouge
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.save_path		).readlines()]
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(_UpperCamelCase		)]
	__lowerCAmelCase      :						dict						      =				score_fn(_UpperCamelCase	,  _UpperCamelCase		)
	scores.update(_UpperCamelCase		)

	if args.dump_args:
		scores.update(_UpperCamelCase		)
	if args.info:
		__lowerCAmelCase      :						Optional[Any]						      =				args.info

	if verbose:
		print(_UpperCamelCase		)

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