

from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class 					__snake_case     :

  def __init__(    self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  data
      SCREAMING_SNAKE_CASE_													=  None


  def __str__(    self):
      return f"""{self.data}"""






class 					__snake_case     :

  def __init__(    self):
      SCREAMING_SNAKE_CASE_													=  None
      SCREAMING_SNAKE_CASE_													=  None

  def __iter__(    self):
      SCREAMING_SNAKE_CASE_													=  self.front
      while node:
          yield node.data
          SCREAMING_SNAKE_CASE_													=  node.next

  def __len__(    self):
      return len(tuple(iter(self)))

  def __str__(    self):
      return " <- ".join(str(_A) for item in self)

  def   lowerCAmelCase__   (    self):
      return len(self) == 0

  def   lowerCAmelCase__   (    self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  Node(_A)
      if self.is_empty():
          SCREAMING_SNAKE_CASE_													=  SCREAMING_SNAKE_CASE_													=  node
      else:
          assert isinstance(self.rear  ,						_A)
          SCREAMING_SNAKE_CASE_													=  node
          SCREAMING_SNAKE_CASE_													=  node

  def   lowerCAmelCase__   (    self):
      if self.is_empty():
          raise IndexError('dequeue from empty queue')
      assert isinstance(self.front  ,						_A)
      SCREAMING_SNAKE_CASE_													=  self.front
      SCREAMING_SNAKE_CASE_													=  self.front.next
      if self.front is None:
          SCREAMING_SNAKE_CASE_													=  None
      return node.data


  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  SCREAMING_SNAKE_CASE_													=  None


if __name__ == "__main__":
       from doctest import testmod

       testmod()





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    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  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}"""				)
    SCREAMING_SNAKE_CASE_													=  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__":
       UpperCamelCase__     :					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."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


