
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__							(    __magic_name__  :       Any							,					__magic_name__  :       int							,					__magic_name__  :       Optional[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       json.load(open(__magic_name__							,					'''r'''					)					)
					lowerCAmelCase						:   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}'''					)
					lowerCAmelCase						:   Union[str, Any]							=       GPTNeoForCausalLM(__magic_name__					)

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

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


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       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.'
		)
		__SCREAMING_SNAKE_CASE     :				Optional[int]             =       parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
