






"""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,
)
UpperCamelCase			=      logging.getLogger(__name__)






def     A(  snake_case_		):
       """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     A(  ):
       """simple docstring"""





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

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

       parser.add_argument("--seed"						,       type=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"						,       )
       lowercase__:    int        =      parser.parse_args()

       lowercase__:    List[Any]        =      torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
       lowercase__:    Any        =      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(snake_case_		)

       # Initialize the model and tokenizer
       lowercase__:    Union[str, Any]        =      AutoTokenizer.from_pretrained(args.model_name_or_path		)
       lowercase__:    int        =      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(snake_case_		)
       lowercase__:    Union[str, Any]        =      args.prompt if args.prompt else input("Model prompt >>> "		)

       lowercase__:    Any        =      tokenizer(snake_case_						,       return_tensors="pt"						,       add_special_tokens=snake_case_		)
       lowercase__:    str        =      {key: value.to(args.device		) for key, value in inputs.items()}

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

       lowercase__:    Dict        =      []
       for generated_sequence_idx, generated_sequence in enumerate(snake_case_		):
              print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""		)
              lowercase__:    str        =      generated_sequence.tolist()

              # Decode text
              lowercase__:    List[str]        =      tokenizer.decode(snake_case_						,       clean_up_tokenization_spaces=snake_case_						,       add_special_tokens=snake_case_		)

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

              generated_sequences.append(snake_case_		)
              print(snake_case_		)

       return generated_sequences


if __name__ == "__main__":
  main()














"""simple docstring"""





def     A(  snake_case_		):
       """simple docstring"""





       if not isinstance(snake_case_						,       snake_case_		):
              raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]"		)
       if len(snake_case_		) == 0:
              raise ValueError("Input list must be a non empty list"		)
       if len(snake_case_		) == 1 and series[0] != 0:
              return True
       lowercase__:    str        =      []
       lowercase__:    Optional[int]        =      len(snake_case_		)
       for i in range(0						,       snake_case_		):
              if series[i] == 0:
                     raise ValueError("Input series cannot have 0 as an element"		)
              rec_series.append(1 / series[i]		)
       lowercase__:    Optional[int]        =      rec_series[1] - rec_series[0]
       for index in range(2						,       snake_case_		):
              if rec_series[index] - rec_series[index - 1] != common_diff:
                     return False
       return True






def     A(  snake_case_		):
       """simple docstring"""





       if not isinstance(snake_case_						,       snake_case_		):
              raise ValueError("Input series is not valid, valid series - [2, 4, 6]"		)
       if len(snake_case_		) == 0:
              raise ValueError("Input list must be a non empty list"		)
       lowercase__:    Optional[int]        =      0
       for val in series:
              answer += 1 / val
       return len(snake_case_		) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()






