




"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						list:
 A__			=							len(lowercase_		)
 for cycle_start in range(0     ,					array_len - 1		):
  A__			=							array[cycle_start]

  A__			=							cycle_start
  for i in range(cycle_start + 1     ,					lowercase_		):
   if array[i] < item:
    pos += 1

  if pos == cycle_start:
   continue

  while item == array[pos]:
   pos += 1

  A__, A__			=							item, array[pos]
  while pos != cycle_start:
   A__			=							cycle_start
   for i in range(cycle_start + 1     ,					lowercase_		):
    if array[i] < item:
     pos += 1

   while item == array[pos]:
    pos += 1

   A__, A__			=							item, array[pos]

 return array


if __name__ == "__main__":
      assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
      assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]











"""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     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 # Initialise PyTorch model
 A__			=							json.load(open(lowercase_     ,					"r"		)		)
 A__			=							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}"""		)
 A__			=							GPTNeoForCausalLM(lowercase_		)

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=			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."
      )
      SCREAMING_SNAKE_CASE							=			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





