code: 
  
  
  
  
  
  '''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							:			List[str]				=getLogger(__name__)
  
  
  __lowerCAmelCase							:			int				="cuda" if torch.cuda.is_available() else "cpu"
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      str		, _lowerCamelCase      :      str		, _lowerCamelCase      :      int = 8		, _lowerCamelCase      :      str = DEFAULT_DEVICE		, _lowerCamelCase      :      Dict=False		, _lowerCamelCase      :      int="summarization"		, _lowerCamelCase      :      Dict=None		, **_lowerCamelCase      :      Any		, ):
      A__       =    Path(_lowerCamelCase ).open("w"		, encoding="utf-8" )
      A__       =    str(_lowerCamelCase )
      A__       =    AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase ).to(_lowerCamelCase )
      if fpaa:
          A__       =    model.half()
  
      A__       =    AutoTokenizer.from_pretrained(_lowerCamelCase )
      logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" )  # if this is wrong, check config.model_type.
  
      A__       =    time.time()
      # update config with task specific params
      use_task_specific_params(_lowerCamelCase		, _lowerCamelCase )
      if prefix is None:
          A__       =    prefix or getattr(model.config		, "prefix"		, "" ) or ""
      for examples_chunk in tqdm(list(chunks(_lowerCamelCase		, _lowerCamelCase ) ) ):
          A__       =    [prefix + text for text in examples_chunk]
          A__       =    tokenizer(_lowerCamelCase		, return_tensors="pt"		, truncation=_lowerCamelCase		, padding="longest" ).to(_lowerCamelCase )
          A__       =    model.generate(
              input_ids=batch.input_ids		, attention_mask=batch.attention_mask		, **_lowerCamelCase		, )
          A__       =    tokenizer.batch_decode(_lowerCamelCase		, skip_special_tokens=_lowerCamelCase		, clean_up_tokenization_spaces=_lowerCamelCase )
          for hypothesis in dec:
              fout.write(hypothesis + "\n" )
              fout.flush()
      fout.close()
      A__       =    int(time.time() - start_time )  # seconds
      A__       =    len(_lowerCamelCase )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		, 4 )}
  
  
  
  
  
  def 		UpperCamelCase       (							):
      return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int=True ):
      A__       =    argparse.ArgumentParser()
      parser.add_argument("model_name"		, type=_lowerCamelCase		, help="like facebook/bart-large-cnn,t5-base, etc." )
      parser.add_argument("input_path"		, type=_lowerCamelCase		, help="like cnn_dm/test.source" )
      parser.add_argument("save_path"		, type=_lowerCamelCase		, help="where to save summaries" )
      parser.add_argument("--reference_path"		, type=_lowerCamelCase		, required=_lowerCamelCase		, help="like cnn_dm/test.target" )
      parser.add_argument("--score_path"		, type=_lowerCamelCase		, required=_lowerCamelCase		, default="metrics.json"		, help="where to save metrics" )
      parser.add_argument("--device"		, type=_lowerCamelCase		, required=_lowerCamelCase		, default=_lowerCamelCase		, help="cuda, cuda:1, cpu etc." )
      parser.add_argument(
          "--prefix"		, type=_lowerCamelCase		, required=_lowerCamelCase		, default=_lowerCamelCase		, help="will be added to the begininng of src examples" )
      parser.add_argument("--task"		, type=_lowerCamelCase		, default="summarization"		, help="used for task_specific_params + metrics" )
      parser.add_argument("--bs"		, type=_lowerCamelCase		, default=8		, required=_lowerCamelCase		, help="batch size" )
      parser.add_argument(
          "--n_obs"		, type=_lowerCamelCase		, default=-1		, required=_lowerCamelCase		, 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=_lowerCamelCase		, 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
      A__,			A__       =    parser.parse_known_args()
      A__       =    parse_numeric_n_bool_cl_kwargs(_lowerCamelCase )
      if parsed_args and verbose:
          print(F"parsed the following generate kwargs: {parsed_args}" )
      with open(args.input_path ) as f:
          A__       =    [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
      if args.n_obs > 0:
          A__       =    examples[: args.n_obs]
      Path(args.save_path ).parent.mkdir(exist_ok=_lowerCamelCase )
      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." )
      A__       =    generate_summaries_or_translations(
          _lowerCamelCase		, args.save_path		, args.model_name		, batch_size=args.bs		, device=args.device		, fpaa=args.fpaa		, task=args.task		, prefix=args.prefix		, **_lowerCamelCase		, )
  
      if args.reference_path is None:
          return {}
  
      # Compute scores
      A__       =    calculate_bleu if "translation" in args.task else calculate_rouge
      A__       =    [x.rstrip() for x in open(args.save_path ).readlines()]
      A__       =    [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_lowerCamelCase )]
      A__       =    score_fn(_lowerCamelCase		, _lowerCamelCase )
      scores.update(_lowerCamelCase )
  
      if args.dump_args:
          scores.update(_lowerCamelCase )
      if args.info:
          A__       =    args.info
  
      if verbose:
          print(_lowerCamelCase )
  
      if args.score_path is not None:
          json.dump(_lowerCamelCase		, 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)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import os
  import tempfile
  import unittest
  from typing import List
  
  from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
  from transformers.testing_utils import require_tokenizers
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  
  
  
  
  
  
  @require_tokenizers
  class      UpperCAmelCase			( unittest.TestCase       ):
   __lowercase     	=       EsmTokenizer
   def   UpperCAmelCase_							(       self   :Any							)->							int:
       super().setUp()
       A__       =    tempfile.mkdtemp()
       # fmt: off
       A__       =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
       # fmt: on
       A__       =    os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES["vocab_file"]							)
       with open(self.vocab_file		, "w"		, encoding="utf-8"							) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)
   def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :int							)->							List[PreTrainedTokenizerBase]:
       return [self.get_tokenizer(**lowercase_							)]
   def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :Dict							)->							PreTrainedTokenizer:
       return self.tokenizer_class.from_pretrained(self.tmpdirname		, **lowercase_							)
   def   UpperCAmelCase_							(       self   :int							)->							Dict:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    tokenizer.tokenize("LAGVS"							)
       self.assertListEqual(lowercase_		, ["L", "A", "G", "V", "S"]							)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)		, [4, 5, 6, 7, 8]							)
   def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    "LAGVS"
       self.assertListEqual(tokenizer.encode(lowercase_							)		, [0, 4, 5, 6, 7, 8, 2]							)
   def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    ["LAGVS", "WCB"]
       A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]
  
       self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]							)
   def   UpperCAmelCase_							(       self   :str							)->							Any:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    ["LAGVS", "WCB"]
       A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]
  
       self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]							)
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							List[str]:
       A__       =    self.get_tokenizers(fast=lowercase_							)
       for tokenizer in tokenizers:
           with self.subTest(F"{tokenizer.__class__.__name__}"							):
               A__       =    "<unk>"
               A__       =    "<mask>"
  
               A__       =    tokenizer.tokenize(lowercase_							)
               A__       =    tokenizer.tokenize(lowercase_							)
  
               self.assertEqual(len(lowercase_							)		, 1							)
               self.assertEqual(len(lowercase_							)		, 1							)
               self.assertEqual(token_a[0]		, lowercase_							)
               self.assertEqual(token_a[0]		, lowercase_							)
  
  
  
  