'''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 __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       )			->					Union[str, Any]:
    # Initialise PyTorch model
    __lowerCamelCase						=						json.load(open(UpperCamelCase__       ,						'''r'''       )       )
    __lowerCamelCase						=						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}"""       )
    __lowerCamelCase						=						GPTNeoForCausalLM(UpperCamelCase__       )

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

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


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

'''simple docstring'''




def __lowerCAmelCase				(							UpperCamelCase__       )			->					bool:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    if len(UpperCamelCase__       ) == 1 and series[0] != 0:
        return True
    __lowerCamelCase						=						[]
    __lowerCamelCase						=						len(UpperCamelCase__       )
    for i in range(0       ,						UpperCamelCase__       ):
        if series[i] == 0:
            raise ValueError('''Input series cannot have 0 as an element'''       )
        rec_series.append(1 / series[i]       )
    __lowerCamelCase						=						rec_series[1] - rec_series[0]
    for index in range(2       ,						UpperCamelCase__       ):
        if rec_series[index] - rec_series[index - 1] != common_diff:
            return False
    return True






def __lowerCAmelCase				(							UpperCamelCase__       )			->					float:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    __lowerCamelCase						=						0
    for val in series:
        answer += 1 / val
    return len(UpperCamelCase__       ) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()
