





"""simple docstring"""





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,
)
SCREAMING_SNAKE_CASE__ =       logging.getLogger(__name__)


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[str]      ):







      '''simple docstring'''




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


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




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

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

      parser.add_argument("""--seed"""							,						type=_A							,						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"""							,						)
      lowerCAmelCase  						=	parser.parse_args()

      lowerCAmelCase  						=	torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""      )
      lowerCAmelCase  						=	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(_A      )

      # Initialize the model and tokenizer
      lowerCAmelCase  						=	AutoTokenizer.from_pretrained(args.model_name_or_path      )
      lowerCAmelCase  						=	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(_A      )
      lowerCAmelCase  						=	args.prompt if args.prompt else input("""Model prompt >>> """      )

      lowerCAmelCase  						=	tokenizer(_A							,						return_tensors="""pt"""							,						add_special_tokens=_A      )
      lowerCAmelCase  						=	{key: value.to(args.device      ) for key, value in inputs.items()}

      lowerCAmelCase  						=	model.generate(
          **_A							,						max_length=args.length + len(inputs["""input_ids"""][0]      )							,						penalty_alpha=args.penalty_alpha							,						top_k=args.k							,						)

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

            # Decode text
            lowerCAmelCase  						=	tokenizer.decode(_A							,						clean_up_tokenization_spaces=_A							,						add_special_tokens=_A      )

            # Remove all text after the stop token
            lowerCAmelCase  						=	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
            lowerCAmelCase  						=	(
                prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]							,						clean_up_tokenization_spaces=_A      )      ) :]
            )

            generated_sequences.append(_A      )
            print(_A      )

      return generated_sequences


if __name__ == "__main__":
  main()













"""simple docstring"""





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


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[Any]      ):







      '''simple docstring'''




      def remove_articles(SCREAMING_SNAKE_CASE					:						int      ):
            return re.sub(R"""\b(a|an|the)\b"""							,						""" """							,						SCREAMING_SNAKE_CASE      )

      def white_space_fix(SCREAMING_SNAKE_CASE					:						int      ):
            return " ".join(text.split()      )

      def remove_punc(SCREAMING_SNAKE_CASE					:						Union[str, Any]      ):
            lowerCAmelCase  						=	set(string.punctuation      )
            return "".join(ch for ch in text if ch not in exclude      )

      def lower(SCREAMING_SNAKE_CASE					:						Optional[Any]      ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE      )      )      )      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						SCREAMING_SNAKE_CASE					:						Optional[int]      ):







      '''simple docstring'''




      lowerCAmelCase  						=	normalize_answer(SCREAMING_SNAKE_CASE      ).split()
      lowerCAmelCase  						=	normalize_answer(SCREAMING_SNAKE_CASE      ).split()
      lowerCAmelCase  						=	Counter(SCREAMING_SNAKE_CASE      ) & Counter(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	sum(common.values()      )
      if num_same == 0:
            return 0
      lowerCAmelCase  						=	1.0 * num_same / len(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	1.0 * num_same / len(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	(2 * precision * recall) / (precision + recall)
      return fa


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Tuple							,						SCREAMING_SNAKE_CASE					:						Tuple      ):







      '''simple docstring'''




      return normalize_answer(SCREAMING_SNAKE_CASE      ) == normalize_answer(SCREAMING_SNAKE_CASE      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[Any]							,						SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						str      ):







      '''simple docstring'''




      lowerCAmelCase  						=	[]
      for ground_truth in ground_truths:
            lowerCAmelCase  						=	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					:						Optional[int]							,						SCREAMING_SNAKE_CASE					:						Dict      ):







      '''simple docstring'''




      lowerCAmelCase  						=	lowerCAmelCase  						=	lowerCAmelCase  						=	0
      for article in dataset:
            for paragraph in article["paragraphs"]:
                  for qa in paragraph["qas"]:
                        total += 1
                        if qa["id"] not in predictions:
                              lowerCAmelCase  						=	"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
                              print(SCREAMING_SNAKE_CASE							,						file=sys.stderr      )
                              continue
                        lowerCAmelCase  						=	[x["""text"""] for x in qa["""answers"""]]
                        lowerCAmelCase  						=	predictions[qa["""id"""]]
                        exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
                        fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	1_00.0 * exact_match / total
      lowerCAmelCase  						=	1_00.0 * fa / total

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ =       "1.1"
       SCREAMING_SNAKE_CASE__ =       argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
       parser.add_argument("dataset_file", help="Dataset file")
       parser.add_argument("prediction_file", help="Prediction File")
       SCREAMING_SNAKE_CASE__ =       parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              SCREAMING_SNAKE_CASE__ =       json.load(dataset_file)
              if dataset_json["version"] != expected_version:
                     print(
                         "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"],
                         file=sys.stderr,
                     )
              SCREAMING_SNAKE_CASE__ =       dataset_json["data"]
       with open(args.prediction_file) as prediction_file:
              SCREAMING_SNAKE_CASE__ =       json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))






