



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


a__   :					Union[str, Any] 						=      getLogger(__name__)


a__   :					str 						=      "cuda" if torch.cuda.is_available() else "cpu"

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__ = 8    ,	A__ = DEFAULT_DEVICE    ,	A__=False    ,	A__="summarization"    ,	A__=None    ,	**A__    ,	):
      lowercase__  =	Path(A__						).open('w'    ,	encoding='utf-8'						)
      lowercase__  =	str(A__						)
      lowercase__  =	AutoModelForSeqaSeqLM.from_pretrained(A__						).to(A__						)
      if fpaa:
            lowercase__  =	model.half()

      lowercase__  =	AutoTokenizer.from_pretrained(A__						)
      logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''						)  # if this is wrong, check config.model_type.

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

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

def 	_lowerCAmelCase (    A__=True						):
      lowercase__  =	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
      lowercase__,						lowercase__  =	parser.parse_known_args()
      lowercase__  =	parse_numeric_n_bool_cl_kwargs(A__						)
      if parsed_args and verbose:
            print(F'''parsed the following generate kwargs: {parsed_args}'''						)
      lowercase__  =	[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
      if args.n_obs > 0:
            lowercase__  =	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.'''						)

      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'						)

      lowercase__  =	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
      lowercase__  =	calculate_bleu if 'translation' in args.task else calculate_rouge
      lowercase__  =	[x.rstrip() for x in open(args.save_path						).readlines()]
      lowercase__  =	[x.rstrip() for x in open(args.reference_path						).readlines()][: len(A__						)]
      lowercase__  =	score_fn(A__    ,	A__						)
      scores.update(A__						)

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





from __future__ import annotations

def 	_lowerCAmelCase (    A__    ,	A__ = None						):
      lowercase__  =	word_bank or []
      # create a table
      lowercase__  =	len(A__						) + 1

      lowercase__  =	[]
      for _ in range(A__						):
            table.append([]						)
      # seed value
      lowercase__  =	[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(A__						):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(A__						)] == word:
                              lowercase__  =	[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(A__						)] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(A__						)]:
            combination.reverse()

      return table[len(A__						)]


if __name__ == "__main__":
       print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
       print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
       print(
           all_construct(
               "hexagonosaurus",
               ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
           )
       )
