

import itertools
import string
from collections.abc import Generator, Iterable
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Iterable[str]   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Generator[tuple[str, ...], None, None]:





    '''simple docstring'''






    A__   				=       iter(SCREAMING_SNAKE_CASE__				)
    while True:
        A__   				=       tuple(itertools.islice(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)
        if not chunk:
            return
        yield chunk
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				str:





    '''simple docstring'''






    A__   				=       ''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
    A__   				=       ''

    if len(SCREAMING_SNAKE_CASE__				) < 2:
        return dirty

    for i in range(len(SCREAMING_SNAKE_CASE__				) - 1				):
        clean += dirty[i]

        if dirty[i] == dirty[i + 1]:
            clean += "X"

    clean += dirty[-1]

    if len(SCREAMING_SNAKE_CASE__				) & 1:
        clean += "X"

    return clean
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				list[str]:





    '''simple docstring'''






    A__   				=       'ABCDEFGHIKLMNOPQRSTUVWXYZ'
    # we're using a list instead of a '2d' array because it makes the math
    # for setting up the table and doing the actual encoding/decoding simpler
    A__   				=       []

    # copy key chars into the table if they are in `alphabet` ignoring duplicates
    for char in key.upper():
        if char not in table and char in alphabet:
            table.append(SCREAMING_SNAKE_CASE__				)

    # fill the rest of the table in with the remaining alphabet chars
    for char in alphabet:
        if char not in table:
            table.append(SCREAMING_SNAKE_CASE__				)

    return table
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				str:





    '''simple docstring'''






    A__   				=       generate_table(SCREAMING_SNAKE_CASE__				)
    A__   				=       prepare_input(SCREAMING_SNAKE_CASE__				)
    A__   				=       ''

    # https://en.wikipedia.org/wiki/Playfair_cipher#Description
    for chara, chara in chunker(SCREAMING_SNAKE_CASE__   ,      2				):
        A__				,						A__   				=       divmod(table.index(SCREAMING_SNAKE_CASE__				)   ,      5				)
        A__				,						A__   				=       divmod(table.index(SCREAMING_SNAKE_CASE__				)   ,      5				)

        if rowa == rowa:
            ciphertext += table[rowa * 5 + (cola + 1) % 5]
            ciphertext += table[rowa * 5 + (cola + 1) % 5]
        elif cola == cola:
            ciphertext += table[((rowa + 1) % 5) * 5 + cola]
            ciphertext += table[((rowa + 1) % 5) * 5 + cola]
        else:  # rectangle
            ciphertext += table[rowa * 5 + cola]
            ciphertext += table[rowa * 5 + cola]

    return ciphertext

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				str:





    '''simple docstring'''






    A__   				=       generate_table(SCREAMING_SNAKE_CASE__				)
    A__   				=       ''

    # https://en.wikipedia.org/wiki/Playfair_cipher#Description
    for chara, chara in chunker(SCREAMING_SNAKE_CASE__   ,      2				):
        A__				,						A__   				=       divmod(table.index(SCREAMING_SNAKE_CASE__				)   ,      5				)
        A__				,						A__   				=       divmod(table.index(SCREAMING_SNAKE_CASE__				)   ,      5				)

        if rowa == rowa:
            plaintext += table[rowa * 5 + (cola - 1) % 5]
            plaintext += table[rowa * 5 + (cola - 1) % 5]
        elif cola == cola:
            plaintext += table[((rowa - 1) % 5) * 5 + cola]
            plaintext += table[((rowa - 1) % 5) * 5 + cola]
        else:  # rectangle
            plaintext += table[rowa * 5 + cola]
            plaintext += table[rowa * 5 + cola]

    return plaintext




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


lowercase_			=  getLogger(__name__)


lowercase_			=  "cuda" if torch.cuda.is_available() else "cpu"
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int = 8   ,      SCREAMING_SNAKE_CASE__      :     str = DEFAULT_DEVICE   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=False   ,      SCREAMING_SNAKE_CASE__      :     Dict="summarization"   ,      SCREAMING_SNAKE_CASE__      :     Any=None   ,      **SCREAMING_SNAKE_CASE__      :     str   ,      )							->				Dict:





    '''simple docstring'''






    A__   				=       Path(SCREAMING_SNAKE_CASE__				).open('w'   ,      encoding='utf-8'				)
    A__   				=       str(SCREAMING_SNAKE_CASE__				)
    A__   				=       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__				).to(SCREAMING_SNAKE_CASE__				)
    if fpaa:
        A__   				=       model.half()

    A__   				=       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__				)
    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(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    if prefix is None:
        A__   				=       prefix or getattr(model.config   ,      'prefix'   ,      ''				) or ''
    for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)				):
        A__   				=       [prefix + text for text in examples_chunk]
        A__   				=       tokenizer(SCREAMING_SNAKE_CASE__   ,      return_tensors='pt'   ,      truncation=SCREAMING_SNAKE_CASE__   ,      padding='longest'				).to(SCREAMING_SNAKE_CASE__				)
        A__   				=       model.generate(
            input_ids=batch.input_ids   ,      attention_mask=batch.attention_mask   ,      **SCREAMING_SNAKE_CASE__   ,      )
        A__   				=       tokenizer.batch_decode(SCREAMING_SNAKE_CASE__   ,      skip_special_tokens=SCREAMING_SNAKE_CASE__   ,      clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__				)
        for hypothesis in dec:
            fout.write(hypothesis + '\n'				)
            fout.flush()
    fout.close()
    A__   				=       int(time.time() - start_time				)  # seconds
    A__   				=       len(SCREAMING_SNAKE_CASE__				)
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   ,      4				)}
def 						_snake_case(   )							->				Tuple:





    '''simple docstring'''






    return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]=True				)							->				Dict:





    '''simple docstring'''






    A__   				=       argparse.ArgumentParser()
    parser.add_argument('model_name'   ,      type=SCREAMING_SNAKE_CASE__   ,      help='like facebook/bart-large-cnn,t5-base, etc.'				)
    parser.add_argument('input_path'   ,      type=SCREAMING_SNAKE_CASE__   ,      help='like cnn_dm/test.source'				)
    parser.add_argument('save_path'   ,      type=SCREAMING_SNAKE_CASE__   ,      help='where to save summaries'				)
    parser.add_argument('--reference_path'   ,      type=SCREAMING_SNAKE_CASE__   ,      required=SCREAMING_SNAKE_CASE__   ,      help='like cnn_dm/test.target'				)
    parser.add_argument('--score_path'   ,      type=SCREAMING_SNAKE_CASE__   ,      required=SCREAMING_SNAKE_CASE__   ,      default='metrics.json'   ,      help='where to save metrics'				)
    parser.add_argument('--device'   ,      type=SCREAMING_SNAKE_CASE__   ,      required=SCREAMING_SNAKE_CASE__   ,      default=SCREAMING_SNAKE_CASE__   ,      help='cuda, cuda:1, cpu etc.'				)
    parser.add_argument(
        '--prefix'   ,      type=SCREAMING_SNAKE_CASE__   ,      required=SCREAMING_SNAKE_CASE__   ,      default=SCREAMING_SNAKE_CASE__   ,      help='will be added to the begininng of src examples'				)
    parser.add_argument('--task'   ,      type=SCREAMING_SNAKE_CASE__   ,      default='summarization'   ,      help='used for task_specific_params + metrics'				)
    parser.add_argument('--bs'   ,      type=SCREAMING_SNAKE_CASE__   ,      default=8   ,      required=SCREAMING_SNAKE_CASE__   ,      help='batch size'				)
    parser.add_argument(
        '--n_obs'   ,      type=SCREAMING_SNAKE_CASE__   ,      default=-1   ,      required=SCREAMING_SNAKE_CASE__   ,      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=SCREAMING_SNAKE_CASE__   ,      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(SCREAMING_SNAKE_CASE__				)
    if parsed_args and verbose:
        print(f'parsed the following generate kwargs: {parsed_args}'				)
    A__   				=       [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
    if args.n_obs > 0:
        A__   				=       examples[: args.n_obs]
    Path(args.save_path				).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)

    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.'				)

    if args.device == "cpu" and args.fpaa:
        # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
        raise ValueError('Can\'t mix --fp16 and --device cpu'				)

    A__   				=       generate_summaries_or_translations(
        SCREAMING_SNAKE_CASE__   ,      args.save_path   ,      args.model_name   ,      batch_size=args.bs   ,      device=args.device   ,      fpaa=args.fpaa   ,      task=args.task   ,      prefix=args.prefix   ,      **SCREAMING_SNAKE_CASE__   ,      )

    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(SCREAMING_SNAKE_CASE__				)]
    A__   				=       score_fn(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    scores.update(SCREAMING_SNAKE_CASE__				)

    if args.dump_args:
        scores.update(SCREAMING_SNAKE_CASE__				)
    if args.info:
        A__   				=       args.info

    if verbose:
        print(SCREAMING_SNAKE_CASE__				)

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

