
'''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 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]						)      ->  List[Any]:
  np.random.seed(args.seed						)
  torch.manual_seed(args.seed						)
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed						)


def 		_UpperCAmelCase			(					)      ->  Dict:
  _lowerCAmelCase    :					str							=   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"""					, )
  _lowerCAmelCase    :					Union[str, Any]							=   parser.parse_args()

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

  _lowerCAmelCase    :					Any							=   tokenizer(_lowerCamelCase					, return_tensors="""pt"""					, add_special_tokens=_lowerCamelCase						)
  _lowerCAmelCase    :					Optional[int]							=   {key: value.to(args.device						) for key, value in inputs.items()}

  _lowerCAmelCase    :					Union[str, Any]							=   model.generate(
      **_lowerCamelCase					, max_length=args.length + len(inputs["""input_ids"""][0]						)					, penalty_alpha=args.penalty_alpha					, top_k=args.k					, )

  _lowerCAmelCase    :					List[str]							=   []
  for generated_sequence_idx, generated_sequence in enumerate(_lowerCamelCase						):
    print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='						)
    _lowerCAmelCase    :					Tuple							=   generated_sequence.tolist()

    # Decode text
    _lowerCAmelCase    :					Dict							=   tokenizer.decode(_lowerCamelCase					, clean_up_tokenization_spaces=_lowerCamelCase					, add_special_tokens=_lowerCamelCase						)

    # Remove all text after the stop token
    _lowerCAmelCase    :					Optional[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
    _lowerCAmelCase    :					Optional[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'''

from __future__ import annotations


class 					a_					:




       def __init__(       self				,				snake_case_ = 0					):
         _lowerCAmelCase    :					Optional[Any]							=   value
         _lowerCAmelCase    :					Node | None							=   None
         _lowerCAmelCase    :					Node | None							=   None



def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None					, _lowerCamelCase  :    Node | None						)      ->  Node | None:
  if treea is None:
    return treea
  if treea is None:
    return treea

  _lowerCAmelCase    :					Any							=   treea.value + treea.value
  _lowerCAmelCase    :					Tuple							=   merge_two_binary_trees(treea.left					, treea.left						)
  _lowerCAmelCase    :					List[str]							=   merge_two_binary_trees(treea.right					, treea.right						)
  return treea


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None						)      ->  None:
  if root:
    print(root.value						)
    print_preorder(root.left						)
    print_preorder(root.right						)


if __name__ == "__main__":
     UpperCamelCase_            =    Node(1)
     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(3)
     UpperCamelCase_            =    Node(4)

     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(4)
     UpperCamelCase_            =    Node(6)
     UpperCamelCase_            =    Node(9)
     UpperCamelCase_            =    Node(5)

     print("""Tree1 is: """)
     print_preorder(treea)
     print("""Tree2 is: """)
     print_preorder(treea)
     UpperCamelCase_            =    merge_two_binary_trees(treea, treea)
     print("""Merged Tree is: """)
     print_preorder(merged_tree)


