





import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			Tuple        =       HerbertTokenizer
      lowercase_				:			str        =       HerbertTokenizerFast
      lowercase_				:			Tuple        =       True

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        super().setUp()

        # Use a simpler test file without japanese/chinese characters
        with open(F"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt""" ,				encoding='utf-8'  ) as f_data:
          A_  :	int    						=  f_data.read().replace('\n\n' ,				'\n'  ).strip()

        A_  :	int    						=  [
            '<s>',
            '</s>',
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            'w</w>',
            'r</w>',
            't</w>',
            'lo',
            'low',
            'er</w>',
            'low</w>',
            'lowest</w>',
            'newer</w>',
            'wider</w>',
            ',</w>',
            '<unk>',
        ]
        A_  :	int    						=  dict(zip(snake_case_ ,				range(len(snake_case_  )  )  )  )
        A_  :	Any    						=  ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

        A_  :	Union[str, Any]    						=  os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['vocab_file']  )
        A_  :	List[str]    						=  os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['merges_file']  )
        with open(self.vocab_file ,				'w'  ) as fp:
          fp.write(json.dumps(snake_case_  )  )
        with open(self.merges_file ,				'w'  ) as fp:
          fp.write('\n'.join(snake_case_  )  )

      def  lowerCamelCase_      (					self ,				snake_case_  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  'lower newer'
        A_  :	List[Any]    						=  'lower newer'
        return input_text, output_text

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  self.tokenizer_class(vocab_file=self.vocab_file ,				merges_file=self.merges_file  )

        A_  :	str    						=  'lower'
        A_  :	Union[str, Any]    						=  ['low', 'er</w>']
        A_  :	int    						=  tokenizer.tokenize(snake_case_  )
        self.assertListEqual(snake_case_ ,				snake_case_  )

        A_  :	Tuple    						=  tokens + ['<unk>']
        A_  :	Dict    						=  [1_6, 1_7, 2_3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_  ) ,				snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        if not self.test_rust_tokenizer:
          return

        A_  :	Tuple    						=  self.get_tokenizer()
        A_  :	List[str]    						=  self.get_rust_tokenizer()

        A_  :	List[str]    						=  'lower,newer'

        A_  :	List[Any]    						=  tokenizer.tokenize(snake_case_  )
        A_  :	Optional[Any]    						=  rust_tokenizer.tokenize(snake_case_  )
        self.assertListEqual(snake_case_ ,				snake_case_  )

        A_  :	Tuple    						=  tokenizer.encode(snake_case_ ,				add_special_tokens=snake_case_  )
        A_  :	List[str]    						=  rust_tokenizer.encode(snake_case_ ,				add_special_tokens=snake_case_  )
        self.assertListEqual(snake_case_ ,				snake_case_  )

        A_  :	Any    						=  self.get_rust_tokenizer()
        A_  :	Union[str, Any]    						=  tokenizer.encode(snake_case_  )
        A_  :	Optional[Any]    						=  rust_tokenizer.encode(snake_case_  )
        self.assertListEqual(snake_case_ ,				snake_case_  )

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'  )

        A_  :	str    						=  tokenizer.encode('konstruowanie sekwencji' ,				add_special_tokens=snake_case_  )
        A_  :	List[str]    						=  tokenizer.encode('konstruowanie wielu sekwencji' ,				add_special_tokens=snake_case_  )

        A_  :	Dict    						=  tokenizer.build_inputs_with_special_tokens(snake_case_  )
        A_  :	Optional[Any]    						=  tokenizer.build_inputs_with_special_tokens(snake_case_ ,				snake_case_  )

        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + text_a + [2]






"""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_					:						Optional[Any]      = 'cuda' if torch.cuda.is_available() else 'cpu'





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 8			, _UpperCAmelCase = DEFAULT_DEVICE			, _UpperCAmelCase=False			, _UpperCAmelCase="summarization"			, _UpperCAmelCase=None			, **_UpperCAmelCase			, ):






  """simple docstring"""





  A_  :	Tuple    						=  Path(_UpperCAmelCase ).open('w'			, encoding='utf-8' )
  A_  :	List[str]    						=  str(_UpperCAmelCase )
  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
  if fpaa:
    A_  :	List[str]    						=  model.half()

  A_  :	int    						=  AutoTokenizer.from_pretrained(_UpperCAmelCase )
  logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

  A_  :	Optional[int]    						=  time.time()
  # update config with task specific params
  use_task_specific_params(_UpperCAmelCase			, _UpperCAmelCase )
  if prefix is None:
    A_  :	Optional[int]    						=  prefix or getattr(model.config			, 'prefix'			, '' ) or ''
  for examples_chunk in tqdm(list(chunks(_UpperCAmelCase			, _UpperCAmelCase ) ) ):
    A_  :	Any    						=  [prefix + text for text in examples_chunk]
    A_  :	List[Any]    						=  tokenizer(_UpperCAmelCase			, return_tensors='pt'			, truncation=_UpperCAmelCase			, padding='longest' ).to(_UpperCAmelCase )
    A_  :	Dict    						=  model.generate(
        input_ids=batch.input_ids			, attention_mask=batch.attention_mask			, **_UpperCAmelCase			, )
    A_  :	Dict    						=  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()
  A_  :	Dict    						=  int(time.time() - start_time )  # seconds
  A_  :	Any    						=  len(_UpperCAmelCase )
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			, 4 )}





def 						UpperCAmelCase__		(					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase=True ):






  """simple docstring"""





  A_  :	int    						=  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
  A_						,	A_  :	int    						=  parser.parse_known_args()
  A_  :	List[Any]    						=  parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase )
  if parsed_args and verbose:
    print(f"""parsed the following generate kwargs: {parsed_args}""" )
  with open(args.input_path ) as f:
    A_  :	List[str]    						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    A_  :	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.""" )
  A_  :	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
  A_  :	Union[str, Any]    						=  calculate_bleu if 'translation' in args.task else calculate_rouge
  A_  :	List[Any]    						=  [x.rstrip() for x in open(args.save_path ).readlines()]
  A_  :	Dict    						=  [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCAmelCase )]
  A_  :	dict    						=  score_fn(_UpperCAmelCase			, _UpperCAmelCase )
  scores.update(_UpperCAmelCase )

  if args.dump_args:
    scores.update(_UpperCAmelCase )
  if args.info:
    A_  :	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)