code: 
  
  
  
  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
  
  
  
  
  
  
  
style_context: 
  
  
  
  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)
  
  
  
  
  
  
  