
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_			(   A__		)							->							int:
    """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_			(   )							->							Any:
    """simple docstring"""

    snake_case      =					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" ,		)
    snake_case      =					parser.parse_args()

    snake_case      =					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
    snake_case      =					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
    snake_case      =					AutoTokenizer.from_pretrained(args.model_name_or_path		)
    snake_case      =					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__		)
    snake_case      =					args.prompt if args.prompt else input("Model prompt >>> "		)

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

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

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

        # Decode text
        snake_case      =					tokenizer.decode(A__ ,		clean_up_tokenization_spaces=A__ ,		add_special_tokens=A__		)

        # Remove all text after the stop token
        snake_case      =					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
        snake_case      =					(
            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()









import re


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  def docstring_decorator(lowercase ):
    SCREAMING_SNAKE_CASE				:					List[Any]		  =			fn.__doc__
    SCREAMING_SNAKE_CASE				:					Tuple		  =			func_doc.split("\n" )
    SCREAMING_SNAKE_CASE				:					Tuple		  =			0
    while i < len(lowercase ) and re.search(R"^\s*Examples?:\s*$"  ,   lines[i] ) is None:
      i += 1
    if i < len(lowercase ):
      SCREAMING_SNAKE_CASE				:					int		  =			example_docstring
      SCREAMING_SNAKE_CASE				:					Tuple		  =			"\n".join(lowercase )
    else:
      raise ValueError(
          F'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
          F'''current docstring is:\n{func_doc}''' )
    SCREAMING_SNAKE_CASE				:					int		  =			func_doc
    return fn

  return docstring_decorator






