




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,
)
_lowerCamelCase    				=		logging.getLogger(__name__)




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Dict   )						->							Union[str, Any]:
    np.random.seed(args.seed   )
    torch.manual_seed(args.seed   )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed   )




def       SCREAMING_SNAKE_CASE			( )						->							Dict:
    UpperCAmelCase_											=  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''' ,  )
    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(__UpperCamelCase   )

    # 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(__UpperCamelCase   )
    UpperCAmelCase_											=  args.prompt if args.prompt else input('''Model prompt >>> '''   )

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

    UpperCAmelCase_											=  model.generate(
        **__UpperCamelCase ,  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(__UpperCamelCase   ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='   )
        UpperCAmelCase_											=  generated_sequence.tolist()

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

        # 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=__UpperCamelCase   )   ) :]
        )

        generated_sequences.append(__UpperCamelCase   )
        print(__UpperCamelCase   )

    return generated_sequences


if __name__ == "__main__":
 main()











import itertools




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Tuple   )						->							List[Any]:
    return (
        int(''''''.join(combination[0:2]   )   ) * int(''''''.join(combination[2:5]   )   )
        == int(''''''.join(combination[5:9]   )   )
    ) or (
        int(''''''.join(combination[0]   )   ) * int(''''''.join(combination[1:5]   )   )
        == int(''''''.join(combination[5:9]   )   )
    )




def       SCREAMING_SNAKE_CASE			( )						->							Optional[Any]:
    return sum(
        {
            int(''''''.join(pandigital[5:9]   )   )
            for pandigital in itertools.permutations('''123456789'''   )
            if is_combination_valid(__UpperCamelCase   )
        }   )


if __name__ == "__main__":
 print(solution())





