






'''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				(  snake_case__				,      snake_case__				,      snake_case__				):
      # Initialise PyTorch model
      __A		 =   json.load(open(snake_case__				,      "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(snake_case__				)

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

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


if __name__ == "__main__":
       __A   :							Tuple     	=						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   :							Optional[Any]     	=						parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''







def     _UpperCamelCase				(  snake_case__ = 1_000				):
      __A		 =   []
      __A		 =   []
      __A		 =   3
      __A		 =   5
      __A		 =   1
      while True:
            __A		 =   z * temp
            if result < n:
                  zmulti.append(snake_case__				)
                  temp += 1
            else:
                  __A		 =   1
                  break
      while True:
            __A		 =   x * temp
            if result < n:
                  xmulti.append(snake_case__				)
                  temp += 1
            else:
                  break
      __A		 =   list(set(xmulti + zmulti				)				)
      return sum(snake_case__				)


if __name__ == "__main__":
       print(F"""{solution() = }""")


