code: 
  
  
  
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers, processors
  
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_longformer import LongformerTokenizer
  
  
  lowerCAmelCase__	:  str 		=logging.get_logger(__name__)
  
  lowerCAmelCase__	:  Any 		={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  lowerCAmelCase__	:  Optional[Any] 		={
      """vocab_file""": {
          """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
          """allenai/longformer-large-4096""": (
              """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
          ),
          """allenai/longformer-large-4096-finetuned-triviaqa""": (
              """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
          ),
          """allenai/longformer-base-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
          ),
          """allenai/longformer-large-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
          ),
      },
      """merges_file""": {
          """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
          """allenai/longformer-large-4096""": (
              """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
          ),
          """allenai/longformer-large-4096-finetuned-triviaqa""": (
              """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
          ),
          """allenai/longformer-base-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
          ),
          """allenai/longformer-large-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
          ),
      },
      """tokenizer_file""": {
          """allenai/longformer-base-4096""": (
              """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
          ),
          """allenai/longformer-large-4096""": (
              """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
          ),
          """allenai/longformer-large-4096-finetuned-triviaqa""": (
              """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
          ),
          """allenai/longformer-base-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
          ),
          """allenai/longformer-large-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  lowerCAmelCase__	:  Tuple 		={
      """allenai/longformer-base-4096""": 4096,
      """allenai/longformer-large-4096""": 4096,
      """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
      """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
      """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
  }
  
  class      UpperCAmelCase_						(     a__ ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				List[str]       						=						VOCAB_FILES_NAMES
  						UpperCamelCase__    :				Tuple       						=						PRETRAINED_VOCAB_FILES_MAP
  						UpperCamelCase__    :				Any       						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  						UpperCamelCase__    :				int       						=						['''input_ids''', '''attention_mask''']
  						UpperCamelCase__    :				Tuple       						=						LongformerTokenizer
  
  
  
  
  
  
  						def __init__(					self   ,							_A=None   ,							_A=None   ,							_A=None   ,							_A="replace"   ,							_A="<s>"   ,							_A="</s>"   ,							_A="</s>"   ,							_A="<s>"   ,							_A="<unk>"   ,							_A="<pad>"   ,							_A="<mask>"   ,							_A=False   ,							_A=True   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													super().__init__(
  													    __A   ,							__A   ,							tokenizer_file=__A   ,							errors=__A   ,							bos_token=__A   ,							eos_token=__A   ,							sep_token=__A   ,							cls_token=__A   ,							unk_token=__A   ,							pad_token=__A   ,							mask_token=__A   ,							add_prefix_space=__A   ,							trim_offsets=__A   ,							**__A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
  													if pre_tok_state.get('add_prefix_space'   ,							__A	) != add_prefix_space:
  																				__SCREAMING_SNAKE_CASE								=      getattr(__A   ,							pre_tok_state.pop('type'	)	)
  																				__SCREAMING_SNAKE_CASE								=      add_prefix_space
  																				__SCREAMING_SNAKE_CASE								=      pre_tok_class(**__A	)
  
  													__SCREAMING_SNAKE_CASE								=      add_prefix_space
  
  													__SCREAMING_SNAKE_CASE								=      "post_processor"
  													__SCREAMING_SNAKE_CASE								=      getattr(self.backend_tokenizer   ,							__A   ,							__A	)
  													if tokenizer_component_instance:
  																				__SCREAMING_SNAKE_CASE								=      json.loads(tokenizer_component_instance.__getstate__()	)
  
  																				# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
  																				if "sep" in state:
  																											__SCREAMING_SNAKE_CASE								=      tuple(state['sep']	)
  																				if "cls" in state:
  																											__SCREAMING_SNAKE_CASE								=      tuple(state['cls']	)
  
  																				__SCREAMING_SNAKE_CASE								=      False
  
  																				if state.get('add_prefix_space'   ,							__A	) != add_prefix_space:
  																											__SCREAMING_SNAKE_CASE								=      add_prefix_space
  																											__SCREAMING_SNAKE_CASE								=      True
  
  																				if state.get('trim_offsets'   ,							__A	) != trim_offsets:
  																											__SCREAMING_SNAKE_CASE								=      trim_offsets
  																											__SCREAMING_SNAKE_CASE								=      True
  
  																				if changes_to_apply:
  																											__SCREAMING_SNAKE_CASE								=      getattr(__A   ,							state.pop('type'	)	)
  																											__SCREAMING_SNAKE_CASE								=      component_class(**__A	)
  																											setattr(self.backend_tokenizer   ,							__A   ,							__A	)
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if self._mask_token is None:
  																				if self.verbose:
  																											logger.error('Using mask_token, but it is not set yet.'	)
  																				return None
  													return str(self._mask_token	)
  
  
  
  
  
  
  						@mask_token.setter
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      AddedToken(__A   ,							lstrip=__A   ,							rstrip=__A	) if isinstance(__A   ,							__A	) else value
  													__SCREAMING_SNAKE_CASE								=      value
  
  
  
  
  
  
  						def  _A			(					self   ,							*_A   ,							**_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      kwargs.get('is_split_into_words'   ,							__A	)
  													assert self.add_prefix_space or not is_split_into_words, (
  													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
  													    "to use it with pretokenized inputs."
  													)
  
  													return super()._batch_encode_plus(*__A   ,							**__A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							*_A   ,							**_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      kwargs.get('is_split_into_words'   ,							__A	)
  
  													assert self.add_prefix_space or not is_split_into_words, (
  													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
  													    "to use it with pretokenized inputs."
  													)
  
  													return super()._encode_plus(*__A   ,							**__A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A = None	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self._tokenizer.model.save(__A   ,							name=__A	)
  													return tuple(__A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A=None	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
  													if token_ids_a is None:
  																				return output
  
  													return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A = None	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      [self.sep_token_id]
  													__SCREAMING_SNAKE_CASE								=      [self.cls_token_id]
  
  													if token_ids_a is None:
  																				return len(cls + token_ids_a + sep	) * [0]
  													return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
  
  
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)
  
  
  
  
  
  
  