
'''simple docstring'''
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       list:


		'''simple docstring'''


		snake_case     :					Optional[Any]     =	len(SCREAMING_SNAKE_CASE__ )
		for cycle_start in range(0   ,					array_len - 1 ):
				snake_case     :					str     =	array[cycle_start]

				snake_case     :					List[Any]     =	cycle_start
				for i in range(cycle_start + 1   ,					SCREAMING_SNAKE_CASE__ ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

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

				snake_case ,snake_case     :					List[Any]     =	item, array[pos]
				while pos != cycle_start:
						snake_case     :					List[Any]     =	cycle_start
						for i in range(cycle_start + 1   ,					SCREAMING_SNAKE_CASE__ ):
								if array[i] < item:
										pos += 1

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

						snake_case ,snake_case     :					Tuple     =	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, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]




'''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__ )     ->       Dict:


		'''simple docstring'''


		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
		snake_case     :					Any     =	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     :					Union[str, 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__":
						lowercase__						=			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."
						)
						lowercase__						=			parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


