


import argparse
import json
import re
import string
import sys
from collections import Counter

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int							):

     def remove_articles(SCREAMING_SNAKE_CASE__     :   Dict							):
          return re.sub(r'\b(a|an|the)\b'     ,	' '     ,	SCREAMING_SNAKE_CASE__							)

     def white_space_fix(SCREAMING_SNAKE_CASE__     :   str							):
          return " ".join(text.split()							)

     def remove_punc(SCREAMING_SNAKE_CASE__     :   Any							):
          __UpperCamelCase       				=set(string.punctuation							)
          return "".join(ch for ch in text if ch not in exclude							)

     def lower(SCREAMING_SNAKE_CASE__     :   Tuple							):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__							)							)							)							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=normalize_answer(SCREAMING_SNAKE_CASE__							).split()
     __UpperCamelCase       				=normalize_answer(SCREAMING_SNAKE_CASE__							).split()
     __UpperCamelCase       				=Counter(SCREAMING_SNAKE_CASE__							) & Counter(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=sum(common.values()							)
     if num_same == 0:
          return 0
     __UpperCamelCase       				=1.0 * num_same / len(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=1.0 * num_same / len(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=(2 * precision * recall) / (precision + recall)
     return fa

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   str							):
     return normalize_answer(SCREAMING_SNAKE_CASE__							) == normalize_answer(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   List[str]     ,	SCREAMING_SNAKE_CASE__     :   Optional[Any]							):
     __UpperCamelCase       				=[]
     for ground_truth in ground_truths:
          __UpperCamelCase       				=metric_fn(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
          scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__							)
     return max(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[str]     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=__UpperCamelCase       				=__UpperCamelCase       				=0
     __UpperCamelCase       				=[]
     for passage in dataset:
          for qa in passage["qas"]:
               total += 1
               if qa["id"] not in predictions:
                    __UpperCamelCase       				=F'Unanswered question {qa["id"]} will receive score 0.'
                    print(SCREAMING_SNAKE_CASE__     ,	file=sys.stderr							)
                    continue

               __UpperCamelCase       				=[x['text'] for x in qa['answers']]
               __UpperCamelCase       				=predictions[qa['id']]

               __UpperCamelCase       				=metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
               if int(_exact_match							) == 1:
                    correct_ids.append(qa['id']							)
               exact_match += _exact_match

               fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     __UpperCamelCase       				=exact_match / total
     __UpperCamelCase       				=fa / total

     return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
   _A									=		'1.0'
   _A									=		argparse.ArgumentParser('Official evaluation script for ReCoRD v1.0.')
   parser.add_argument('data_file', help='The dataset file in JSON format.')
   parser.add_argument('pred_file', help='The model prediction file in JSON format.')
   parser.add_argument('--output_correct_ids', action='store_true', help='Output the correctly answered query IDs.')
   _A									=		parser.parse_args()

   with open(args.data_file) as data_file:
      _A									=		json.load(data_file)
      if dataset_json["version"] != expected_version:
         print(
             f"""Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}""",
             file=sys.stderr,
         )
      _A									=		dataset_json['data']

   with open(args.pred_file) as pred_file:
      _A									=		json.load(pred_file)

   _A			,	_A									=		evaluate(dataset, predictions)

   if args.output_correct_ids:
      print(f"""Output {len(correct_ids)} correctly answered question IDs.""")
      with open('correct_ids.json', 'w') as f:
         json.dump(correct_ids, f)









import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_A									=		logging.getLogger(__name__)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str							):
     np.random.seed(args.seed							)
     torch.manual_seed(args.seed							)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=argparse.ArgumentParser()
     parser.add_argument(
         '--model_name_or_path'     ,	default=SCREAMING_SNAKE_CASE__     ,	type=SCREAMING_SNAKE_CASE__     ,	required=SCREAMING_SNAKE_CASE__     ,	)
     parser.add_argument('--prompt'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''							)
     parser.add_argument('--length'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=20							)
     parser.add_argument('--stop_token'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=SCREAMING_SNAKE_CASE__     ,	help='Token at which text generation is stopped'							)
     parser.add_argument(
         '--temperature'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='temperature of 1.0 has no effect, lower tend toward greedy sampling'     ,	)
     parser.add_argument(
         '--repetition_penalty'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='primarily useful for CTRL model; in that case, use 1.2'							)
     parser.add_argument('--k'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0							)
     parser.add_argument('--penalty_alpha'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.0							)
     parser.add_argument('--p'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.9							)

     parser.add_argument('--prefix'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Text added prior to input.'							)
     parser.add_argument('--padding_text'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Deprecated, the use of `--prefix` is preferred.'							)
     parser.add_argument('--xlm_language'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Optional language when used with the XLM model.'							)

     parser.add_argument('--seed'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=42     ,	help='random seed for initialization'							)
     parser.add_argument('--no_cuda'     ,	action='store_true'     ,	help='Avoid using CUDA when available'							)
     parser.add_argument(
         '--fp16'     ,	action='store_true'     ,	help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     ,	)
     __UpperCamelCase       				=parser.parse_args()

     __UpperCamelCase       				=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
     __UpperCamelCase       				=0 if args.no_cuda else torch.cuda.device_count()

     logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'							)

     set_seed(SCREAMING_SNAKE_CASE__							)

     # Initialize the model and tokenizer
     __UpperCamelCase       				=AutoTokenizer.from_pretrained(args.model_name_or_path							)
     __UpperCamelCase       				=AutoModelForCausalLM.from_pretrained(args.model_name_or_path							)

     # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
     # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
     model.to(args.device							)

     if args.fpaa:
          model.half()

     logger.info(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=args.prompt if args.prompt else input('Model prompt >>> '							)

     __UpperCamelCase       				=tokenizer(SCREAMING_SNAKE_CASE__     ,	return_tensors='pt'     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				={key: value.to(args.device							) for key, value in inputs.items()}

     __UpperCamelCase       				=model.generate(
         **SCREAMING_SNAKE_CASE__     ,	max_length=args.length + len(inputs['input_ids'][0]							)     ,	penalty_alpha=args.penalty_alpha     ,	top_k=args.k     ,	)

     __UpperCamelCase       				=[]
     for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE__							):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
          __UpperCamelCase       				=generated_sequence.tolist()

          # Decode text
          __UpperCamelCase       				=tokenizer.decode(SCREAMING_SNAKE_CASE__     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)

          # Remove all text after the stop token
          __UpperCamelCase       				=text[: text.find(args.stop_token							) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          __UpperCamelCase       				=(
              prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							)							) :]
          )

          generated_sequences.append(SCREAMING_SNAKE_CASE__							)
          print(SCREAMING_SNAKE_CASE__							)

     return generated_sequences


if __name__ == "__main__":
   main()





