

"""simple docstring"""

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

a__			:						int							=			TypeVar("""T""", bound=bool)


class       _snake_case  (    Generic[T]							):







						def __init__(self  ,				UpperCAmelCase_				)      ->    int:
											__lowercase        =      value
											__lowercase        =      None
											__lowercase        =      None







						@property
						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											return self._value







						@staticmethod
						def 							_UpperCamelCase     (UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    List[Any]:
											if not roota:
																return roota

											if not roota:
																return roota

											if roota.value > roota.value:
																__lowercase        =      roota, roota

											if random.choice([True, False]				):
																__lowercase        =      roota.right, roota.left

											__lowercase        =      RandomizedHeapNode.merge(roota.left  ,				lowercase_				)

											return roota



class       _snake_case  (    Generic[T]							):







						def __init__(self  ,				UpperCAmelCase_ = ()				)      ->    Union[str, Any]:
											__lowercase        =      None

											if data:
																for item in data:
																					self.insert(lowercase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    Dict:
											__lowercase        =      RandomizedHeapNode.merge(self._root  ,				RandomizedHeapNode(lowercase_				)				)







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase        =      self.top()

											if self._root is None:
																return None

											__lowercase        =      RandomizedHeapNode.merge(self._root.left  ,				self._root.right				)

											return result







						def 							_UpperCamelCase     (self				)      ->    List[str]:
											if not self._root:
																raise IndexError('''Can\'t get top element for the empty heap.'''				)
											return self._root.value







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      None







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											__lowercase        =      []
											while self:
																result.append(self.pop()				)

											return result







						def __bool__(self				)      ->    str:
											return self._root is not None


if __name__ == "__main__":
							import doctest

							doctest.testmod()




'''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)




