




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






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict						)    ->       Optional[int]:
 np.random.seed(args.seed						)
 torch.manual_seed(args.seed						)
 if args.n_gpu > 0:
  torch.cuda.manual_seed_all(args.seed						)






def 				__lowerCAmelCase					(					)    ->       str:
 _UpperCamelCase      :	Optional[Any]					      =  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      :	Union[str, Any]					      =  parser.parse_args()

 _UpperCamelCase      :	Dict					      =  torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
 _UpperCamelCase      :	int					      =  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      :	Union[str, Any]					      =  AutoTokenizer.from_pretrained(args.model_name_or_path						)
 _UpperCamelCase      :	Dict					      =  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      :	Union[str, Any]					      =  args.prompt if args.prompt else input("Model prompt >>> "						)

 _UpperCamelCase      :	int					      =  tokenizer(__lowerCAmelCase  ,	return_tensors="pt"  ,	add_special_tokens=__lowerCAmelCase						)
 _UpperCamelCase      :	List[str]					      =  {key: value.to(args.device						) for key, value in inputs.items()}

 _UpperCamelCase      :	Tuple					      =  model.generate(
     **__lowerCAmelCase  ,	max_length=args.length + len(inputs["input_ids"][0]						)  ,	penalty_alpha=args.penalty_alpha  ,	top_k=args.k  ,	)

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

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

  # Remove all text after the stop token
  _UpperCamelCase      :	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
  _UpperCamelCase      :	int					      =  (
      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()









"""simple docstring"""
import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)

 _UpperCamelCase      :	NDArray[floataa]					      =  np.zeros((rows, 1)  ,	dtype=__lowerCAmelCase						)
 for row in reversed(range(__lowerCAmelCase						)						):
  _UpperCamelCase      :	List[str]					      =  0
  for col in range(row + 1  ,	__lowerCAmelCase						):
   total += coefficients[row, col] * x[col]

  _UpperCamelCase      :	Dict					      =  (vector[row] - total) / coefficients[row, row]

 return x






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)
 if rows != columns:
  return np.array(()  ,	dtype=__lowerCAmelCase						)

 # augmented matrix
 _UpperCamelCase      :	NDArray[floataa]					      =  np.concatenate((coefficients, vector)  ,	axis=1						)
 _UpperCamelCase      :	List[str]					      =  augmented_mat.astype("float64"						)

 # scale the matrix leaving it triangular
 for row in range(rows - 1						):
  _UpperCamelCase      :	Optional[Any]					      =  augmented_mat[row, row]
  for col in range(row + 1  ,	__lowerCAmelCase						):
   _UpperCamelCase      :	Union[str, Any]					      =  augmented_mat[col, row] / pivot
   augmented_mat[col, :] -= factor * augmented_mat[row, :]

 _UpperCamelCase      :	Any					      =  retroactive_resolution(
     augmented_mat[:, 0:columns]  ,	augmented_mat[:, columns : columns + 1]						)

 return x


if __name__ == "__main__":
    import doctest

    doctest.testmod()



