




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  :				List[str]						=				logging.getLogger(__name__)



def   lowerCAmelCase						(						_lowerCAmelCase							:				Union[str, Any]		) ->    List[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   lowerCAmelCase						(						) ->    Dict:




 """simple docstring"""







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

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

 parser.add_argument("--seed"  ,							type=_lowerCamelCase  ,							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(_lowerCamelCase		)

 # 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(_lowerCamelCase		)
 UpperCAmelCase__								=  args.prompt if args.prompt else input("Model prompt >>> "		)

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

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

  # Decode text
  UpperCAmelCase__								=  tokenizer.decode(_lowerCamelCase  ,							clean_up_tokenization_spaces=_lowerCamelCase  ,							add_special_tokens=_lowerCamelCase		)

  # 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=_lowerCamelCase		)		) :]
  )

  generated_sequences.append(_lowerCamelCase		)
  print(_lowerCamelCase		)

 return generated_sequences


if __name__ == "__main__":
  main()











_lowerCAmelCase  :				dict[str, float]						=				{
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

_lowerCAmelCase  :				dict[str, float]						=				{
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_60_54_01_99E-27,
}



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				float		):




 """simple docstring"""







 if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
  UpperCAmelCase__								=  (
      F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Supported values are: {", ".join(_lowerCAmelCase		)}'''
  )
  raise ValueError(_lowerCAmelCase		)
 return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
  import doctest

  doctest.testmod()





