



import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowerCAmelCase__	:  Optional[int] 		=datasets.utils.logging.get_logger(__name__)

@dataclass
class      UpperCAmelCase_						(     datasets.BuilderConfig ):

						'''simple docstring'''




						UpperCamelCase__    :				Optional[datasets.Features]       						=						None







class      UpperCAmelCase_						(     datasets.ArrowBasedBuilder ):

						'''simple docstring'''




						UpperCamelCase__    :				List[str]       						=						ArrowConfig






						def  _A			(					self	):







													'''simple docstring'''

													return datasets.DatasetInfo(features=self.config.features	)






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													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}"""	)
													__SCREAMING_SNAKE_CASE								=      dl_manager.download_and_extract(self.config.data_files	)
													if isinstance(_A   ,							(str, list, tuple)	):
																				__SCREAMING_SNAKE_CASE								=      data_files
																				if isinstance(_A   ,							_A	):
																											__SCREAMING_SNAKE_CASE								=      [files]
																				# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																				__SCREAMING_SNAKE_CASE								=      [dl_manager.iter_files(_A	) for file in files]
																				return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,							gen_kwargs={'files': files}	)]
													__SCREAMING_SNAKE_CASE								=      []
													for split_name, files in data_files.items():
																				if isinstance(_A   ,							_A	):
																											__SCREAMING_SNAKE_CASE								=      [files]
																				# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																				__SCREAMING_SNAKE_CASE								=      [dl_manager.iter_files(_A	) for file in files]
																				# Infer features is they are stoed in the arrow schema
																				if self.info.features is None:
																											for file in itertools.chain.from_iterable(_A	):
																																		with open(_A   ,							'rb'	) as f:
																																									__SCREAMING_SNAKE_CASE								=      datasets.Features.from_arrow_schema(pa.ipc.open_stream(_A	).schema	)
																																		break
																				splits.append(datasets.SplitGenerator(name=_A   ,							gen_kwargs={'files': files}	)	)
													return splits






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													if self.info.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
																				__SCREAMING_SNAKE_CASE								=      table_cast(_A   ,							self.info.features.arrow_schema	)
													return pa_table






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													for file_idx, file in enumerate(itertools.chain.from_iterable(_A	)	):
																				with open(_A   ,							'rb'	) as f:
																											try:
																																		for batch_idx, record_batch in enumerate(pa.ipc.open_stream(_A	)	):
																																									__SCREAMING_SNAKE_CASE								=      pa.Table.from_batches([record_batch]	)
																																									# Uncomment for debugging (will print the Arrow table size and elements)
																																									# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																									# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																									yield f"""{file_idx}_{batch_idx}""", self._cast_table(_A	)
																											except ValueError as e:
																																		logger.error(f"""Failed to read file '{file}' with error {type(_A	)}: {e}"""	)
																																		raise











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__	:  Union[str, Any] 		=getLogger(__name__)


lowerCAmelCase__	:  Optional[int] 		='''cuda''' if torch.cuda.is_available() else '''cpu'''






def 			__lowercase     (				a__							,     a__							,     a__							,     a__ = 8							,     a__ = DEFAULT_DEVICE							,     a__=False							,     a__="summarization"							,     a__=None							,     **a__							,     )       ->				Dict:
							__SCREAMING_SNAKE_CASE								=      Path(a__     ).open('w'							,     encoding='utf-8'     )
							__SCREAMING_SNAKE_CASE								=      str(a__     )
							__SCREAMING_SNAKE_CASE								=      AutoModelForSeqaSeqLM.from_pretrained(a__     ).to(a__     )
							if fpaa:
														__SCREAMING_SNAKE_CASE								=      model.half()

							__SCREAMING_SNAKE_CASE								=      AutoTokenizer.from_pretrained(a__     )
							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(a__							,     a__     )
							if prefix is None:
														__SCREAMING_SNAKE_CASE								=      prefix or getattr(model.config							,     'prefix'							,     ''     ) or ''
							for examples_chunk in tqdm(list(chunks(a__							,     a__     )     )     ):
														__SCREAMING_SNAKE_CASE								=      [prefix + text for text in examples_chunk]
														__SCREAMING_SNAKE_CASE								=      tokenizer(a__							,     return_tensors='pt'							,     truncation=a__							,     padding='longest'     ).to(a__     )
														__SCREAMING_SNAKE_CASE								=      model.generate(
														    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **a__							,     )
														__SCREAMING_SNAKE_CASE								=      tokenizer.batch_decode(a__							,     skip_special_tokens=a__							,     clean_up_tokenization_spaces=a__     )
														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(a__     )
							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4     )}






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






def 			__lowercase     (				a__=True     )       ->				Optional[Any]:
							__SCREAMING_SNAKE_CASE								=      argparse.ArgumentParser()
							parser.add_argument('model_name'							,     type=a__							,     help='like facebook/bart-large-cnn,t5-base, etc.'     )
							parser.add_argument('input_path'							,     type=a__							,     help='like cnn_dm/test.source'     )
							parser.add_argument('save_path'							,     type=a__							,     help='where to save summaries'     )
							parser.add_argument('--reference_path'							,     type=a__							,     required=a__							,     help='like cnn_dm/test.target'     )
							parser.add_argument('--score_path'							,     type=a__							,     required=a__							,     default='metrics.json'							,     help='where to save metrics'     )
							parser.add_argument('--device'							,     type=a__							,     required=a__							,     default=a__							,     help='cuda, cuda:1, cpu etc.'     )
							parser.add_argument(
							    '--prefix'							,     type=a__							,     required=a__							,     default=a__							,     help='will be added to the begininng of src examples'     )
							parser.add_argument('--task'							,     type=a__							,     default='summarization'							,     help='used for task_specific_params + metrics'     )
							parser.add_argument('--bs'							,     type=a__							,     default=8							,     required=a__							,     help='batch size'     )
							parser.add_argument(
							    '--n_obs'							,     type=a__							,     default=-1							,     required=a__							,     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=a__							,     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(a__     )
							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=a__     )
							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(
							    a__							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **a__							,     )

							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(a__     )]
							__SCREAMING_SNAKE_CASE								=      score_fn(a__							,     a__     )
							scores.update(a__     )

							if args.dump_args:
														scores.update(a__     )
							if args.info:
														__SCREAMING_SNAKE_CASE								=      args.info

							if verbose:
														print(a__     )

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






