


'''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,
)
a__    :   int								=			logging.getLogger(__name__)







def _UpperCamelCase			(	__A       )				->						Union[str, Any]:


      '''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			(	)				->						Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__      =							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" ,     )
      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(__A       )

      # 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(__A       )
      UpperCamelCase__      =							args.prompt if args.prompt else input("Model prompt >>> "       )

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

      UpperCamelCase__      =							model.generate(
          **__A ,     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(__A       ):
            print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''       )
            UpperCamelCase__      =							generated_sequence.tolist()

            # Decode text
            UpperCamelCase__      =							tokenizer.decode(__A ,     clean_up_tokenization_spaces=__A ,     add_special_tokens=__A       )

            # 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=__A       )       ) :]
            )

            generated_sequences.append(__A       )
            print(__A       )

      return generated_sequences


if __name__ == "__main__":
  main()









'''simple docstring'''







from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
a__    :   Optional[Any]								=			TypeVar('T', bound='Enum')
@unique
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      80
    __UpperCAmelCase       =      70
    __UpperCAmelCase       =      60
    __UpperCAmelCase       =      50
    __UpperCAmelCase       =      40
    __UpperCAmelCase       =      30
    __UpperCAmelCase       =      20
    __UpperCAmelCase       =      10

@unique
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      24
    __UpperCAmelCase       =      21
    __UpperCAmelCase       =      18
    __UpperCAmelCase       =      15
    __UpperCAmelCase       =      12
    __UpperCAmelCase       =      9
    __UpperCAmelCase       =      6
    __UpperCAmelCase       =      3
    __UpperCAmelCase       =      2
    __UpperCAmelCase       =      1
    __UpperCAmelCase       =      -1
    __UpperCAmelCase       =      -2
    __UpperCAmelCase       =      -3
    __UpperCAmelCase       =      -6
    __UpperCAmelCase       =      -9
    __UpperCAmelCase       =      -12
    __UpperCAmelCase       =      -15
    __UpperCAmelCase       =      -18
    __UpperCAmelCase       =      -21
    __UpperCAmelCase       =      -24


    @classmethod
    def   __a				(	cls       ):
          return {unit.name: unit.value for unit in cls if unit.value > 0}


    @classmethod
    def   __a				(	cls       ):
          return {unit.name: unit.value for unit in cls if unit.value < 0}







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
      for name_prefix, value_prefix in prefixes.items():
            UpperCamelCase__      =							value / (10**value_prefix)
            if numerical_part > 1:
                  return F'''{numerical_part!s} {name_prefix}'''
      return str(__A       )







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      for prefix in BinaryUnit:
            UpperCamelCase__      =							value / (2**prefix.value)
            if numerical_part > 1:
                  return F'''{numerical_part!s} {prefix.name}'''
      return str(__A       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





