


'''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


a_  :    Optional[int]      =			getLogger(__name__)


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





def __snake_case (				UpperCAmelCase_						:						List[str] ,    UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						int = 8 ,    UpperCAmelCase_						:						str = DEFAULT_DEVICE ,    UpperCAmelCase_						:						Union[str, Any]=False ,    UpperCAmelCase_						:						Tuple="summarization" ,    UpperCAmelCase_						:						int=None ,    **UpperCAmelCase_						:						Optional[Any] ,    ):
	lowerCamelCase_								=  Path(UpperCAmelCase_		).open("w" ,    encoding="utf-8"		)
	lowerCamelCase_								=  str(UpperCAmelCase_		)
	lowerCamelCase_								=  AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_		).to(UpperCAmelCase_		)
	if fpaa:
		lowerCamelCase_								=  model.half()

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

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





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





def __snake_case (				UpperCAmelCase_						:						Tuple=True		):
	lowerCamelCase_								=  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
	lowerCamelCase_								=  parser.parse_known_args()
	lowerCamelCase_								=  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:
		lowerCamelCase_								=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
	if args.n_obs > 0:
		lowerCamelCase_								=  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.'''		)
	lowerCamelCase_								=  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
	lowerCamelCase_								=  calculate_bleu if 'translation' in args.task else calculate_rouge
	lowerCamelCase_								=  [x.rstrip() for x in open(args.save_path		).readlines()]
	lowerCamelCase_								=  [x.rstrip() for x in open(args.reference_path		).readlines()][: len(UpperCAmelCase_		)]
	lowerCamelCase_								=  score_fn(UpperCAmelCase_ ,    UpperCAmelCase_		)
	scores.update(UpperCAmelCase_		)

	if args.dump_args:
		scores.update(UpperCAmelCase_		)
	if args.info:
		lowerCamelCase_								=  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)







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_  :    Optional[Any]      =			logging.get_logger(__name__)

a_  :    Tuple      =			{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "electra"






  def __init__(   self      ,  UpperCamelCase=3_0522      ,  UpperCamelCase=128      ,  UpperCamelCase=256      ,  UpperCamelCase=12      ,  UpperCamelCase=4      ,  UpperCamelCase=1024      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=512      ,  UpperCamelCase=2      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-12      ,  UpperCamelCase="first"      ,  UpperCamelCase=True      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0      ,  UpperCamelCase="absolute"      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase      ,  **UpperCamelCase					)

   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  embedding_size
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  layer_norm_eps

   lowerCamelCase_								=  summary_type
   lowerCamelCase_								=  summary_use_proj
   lowerCamelCase_								=  summary_activation
   lowerCamelCase_								=  summary_last_dropout
   lowerCamelCase_								=  position_embedding_type
   lowerCamelCase_								=  use_cache
   lowerCamelCase_								=  classifier_dropout





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  @property
  def   snake_case		(   self					):







   """simple docstring"""
   if self.task == "multiple-choice":
    lowerCamelCase_								=  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    lowerCamelCase_								=  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
       ]					)



