
'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[str]        =    len(__SCREAMING_SNAKE_CASE							)
    for _ in range(
        0,		int(((no_of_elements - 1) / 2) + 1							)							):  # we don't need to traverse to end of list as
        for j in range(0,		no_of_elements - 1							):
            if (
                lst[j + 1] < lst[j]
            ):  # applying bubble sort algorithm from left to right (or forwards)
                snake_case_			:     Union[str, Any]        =    lst[j + 1]
                snake_case_			:     Tuple        =    lst[j]
                snake_case_			:     List[Any]        =    temp
            if (
                lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
            ):  # applying bubble sort algorithm from right to left (or backwards)
                snake_case_			:     str        =    lst[no_of_elements - 1 - j]
                snake_case_			:     List[str]        =    lst[no_of_elements - 2 - j]
                snake_case_			:     Tuple        =    temp
    return lst


if __name__ == "__main__":
  print("enter the list to be sorted")
  a_           =    [int(x) for x in input().split()]  # inputing elements of the list in one line
  a_           =    double_sort(lst)
  print("the sorted list is")
  print(sorted_lst)






'''simple docstring'''
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    json.load(open(__SCREAMING_SNAKE_CASE,		"r"							)							)
    snake_case_			:     Optional[int]        =    GPTNeoConfig(
        hidden_size=config_json["n_embd"],		num_layers=config_json["n_layer"],		num_heads=config_json["n_head"],		attention_types=config_json["attention_types"],		max_position_embeddings=config_json["n_positions"],		resid_dropout=config_json["res_dropout"],		embed_dropout=config_json["embed_dropout"],		attention_dropout=config_json["attn_dropout"],		)
    print(f'Building PyTorch model from configuration: {config}'							)
    snake_case_			:     Optional[Any]        =    GPTNeoForCausalLM(__SCREAMING_SNAKE_CASE							)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'							)
    model.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
  a_           =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained mesh-tf model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  a_           =    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




