


def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) * 9 / 5) + 32,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round(float(_UpperCamelCase     ) + 2_7_3.1_5,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) * 9 / 5) + 4_9_1.6_7,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) - 32) * 5 / 9,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round(((float(_UpperCamelCase     ) - 32) * 5 / 9) + 2_7_3.1_5,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round(float(_UpperCamelCase     ) + 4_5_9.6_7,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round(float(_UpperCamelCase     ) - 2_7_3.1_5,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round(((float(_UpperCamelCase     ) - 2_7_3.1_5) * 9 / 5) + 32,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) * 9 / 5),    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) - 4_9_1.6_7) * 5 / 9,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round(float(_UpperCamelCase     ) - 4_5_9.6_7,    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) * 5 / 9),    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) * 1.2_5 + 2_7_3.1_5),    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) * 2.2_5 + 32),    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) * 1.2_5),    _UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase = 2     )    ->float:







      """simple docstring"""

      return round((float(_UpperCamelCase     ) * 2.2_5 + 32 + 4_5_9.6_7),    _UpperCamelCase     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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 			__lowercase	(						_UpperCamelCase     )    ->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 			__lowercase	(						)    ->List[str]:







      """simple docstring"""

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

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

      parser.add_argument('''--seed''',    type=_UpperCamelCase,    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''',    )
      lowercase			:   Any					   =		parser.parse_args()

      lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
      lowercase			:   List[str]					   =		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(_UpperCamelCase     )

      # Initialize the model and tokenizer
      lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
      lowercase			:   List[Any]					   =		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(_UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )

      lowercase			:   List[Any]					   =		tokenizer(_UpperCamelCase,    return_tensors='''pt''',    add_special_tokens=_UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		{key: value.to(args.device     ) for key, value in inputs.items()}

      lowercase			:   Optional[int]					   =		model.generate(
          **_UpperCamelCase,    max_length=args.length + len(inputs['''input_ids'''][0]     ),    penalty_alpha=args.penalty_alpha,    top_k=args.k,    )

      lowercase			:   Union[str, Any]					   =		[]
      for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
            print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
            lowercase			:   Optional[Any]					   =		generated_sequence.tolist()

            # Decode text
            lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )

            # Remove all text after the stop token
            lowercase			:   Optional[int]					   =		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
            lowercase			:   Optional[Any]					   =		(
                prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
            )

            generated_sequences.append(_UpperCamelCase     )
            print(_UpperCamelCase     )

      return generated_sequences


if __name__ == "__main__":
  main()

