





from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_					:						str      = logging.get_logger(__name__)

lowerCamelCase_					:						List[str]      = {
    'microsoft/prophetnet-large-uncased': (
        'https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'
    ),
}

class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Optional[int]        =       'prophetnet'
      lowercase_				:			Any        =       ['past_key_values']
      lowercase_				:			int        =       {
          'num_attention_heads': 'num_encoder_attention_heads',
      }

      def __init__(					self ,				snake_case_ = 0.1 ,				snake_case_ = "gelu" ,				snake_case_ = 3_0_5_2_2 ,				snake_case_ = 1_0_2_4 ,				snake_case_ = 4_0_9_6 ,				snake_case_ = 1_2 ,				snake_case_ = 1_6 ,				snake_case_ = 4_0_9_6 ,				snake_case_ = 1_2 ,				snake_case_ = 1_6 ,				snake_case_ = 0.1 ,				snake_case_ = 0.1 ,				snake_case_ = 5_1_2 ,				snake_case_ = 0.02 ,				snake_case_ = True ,				snake_case_ = True ,				snake_case_ = 0 ,				snake_case_ = 2 ,				snake_case_ = 3_2 ,				snake_case_ = 1_2_8 ,				snake_case_ = False ,				snake_case_ = 0.0 ,				snake_case_ = True ,				snake_case_ = 0 ,				snake_case_ = 1 ,				snake_case_ = 2 ,				**snake_case_ ,				):

        """simple docstring"""

        A_  :	Optional[Any]    						=  vocab_size
        A_  :	Any    						=  hidden_size
        A_  :	int    						=  encoder_ffn_dim
        A_  :	Optional[int]    						=  num_encoder_layers
        A_  :	Union[str, Any]    						=  num_encoder_attention_heads
        A_  :	Optional[Any]    						=  decoder_ffn_dim
        A_  :	Union[str, Any]    						=  num_decoder_layers
        A_  :	List[Any]    						=  num_decoder_attention_heads
        A_  :	Tuple    						=  max_position_embeddings
        A_  :	Union[str, Any]    						=  init_std  # Normal(0, this parameter)
        A_  :	Any    						=  activation_function

        # parameters for prophetnet
        A_  :	Any    						=  ngram
        A_  :	Dict    						=  num_buckets
        A_  :	Tuple    						=  relative_max_distance
        A_  :	int    						=  disable_ngram_loss
        A_  :	Any    						=  eps

        # 3 Types of Dropout
        A_  :	str    						=  attention_dropout
        A_  :	Tuple    						=  activation_dropout
        A_  :	Optional[Any]    						=  dropout

        A_  :	str    						=  use_cache

        super().__init__(
            pad_token_id=snake_case_ ,				bos_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				is_encoder_decoder=snake_case_ ,				add_cross_attention=snake_case_ ,				decoder_start_token_id=snake_case_ ,				**snake_case_ ,				)

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self.num_encoder_layers + self.num_decoder_layers

      @num_hidden_layers.setter
      def  lowerCamelCase_      (					self ,				snake_case_  ):

        """simple docstring"""

        raise NotImplementedError(
            'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
            ' `num_decoder_layers`.'  )






"""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)