



import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCAmelCase__  =			logging.getLogger(__name__)





def 				A      (	)							->					str:







			'''simple docstring'''
			_UpperCAmelCase									=  argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'       )
			parser.add_argument(
			    '--dataset_name'				,  type=_UpperCAmelCase				,  default='wikitext'				,  help='Name of the training. Explore datasets at: hf.co/datasets.'				,  )
			parser.add_argument(
			    '--dataset_config'				,  type=_UpperCAmelCase				,  default='wikitext-103-raw-v1'				,  help='Configuration name of the dataset.'       )
			parser.add_argument(
			    '--batch_size'				,  type=_UpperCAmelCase				,  default=1_000				,  help='Batch size during training.'				,  )
			parser.add_argument(
			    '--vocab_size'				,  type=_UpperCAmelCase				,  default=10_048				,  help='Size of the desired vocabulary.'				,  )
			parser.add_argument(
			    '--limit'				,  default=_UpperCAmelCase				,  type=_UpperCAmelCase				,  help='Limit the number of shards (used for debugging).'				,  )
			parser.add_argument(
			    '--export_to_hub'				,  action='store_true'				,  )

			_UpperCAmelCase									=  parser.parse_args()
			return args





def 				A      (	_UpperCAmelCase							:  Any       )							->					str:







			'''simple docstring'''
			_UpperCAmelCase									=  datasets.load_dataset(args.dataset_name				,  args.dataset_config				,  split='train'       )

			if args.limit is not None:
						_UpperCAmelCase									=  min(len(_UpperCAmelCase       )				,  args.limit       )
						_UpperCAmelCase									=  dataset.select(range(_UpperCAmelCase       )       )
						logger.info(F"Limiting the dataset to {args.limit} entries."       )

			def batch_iterator():
						for i in range(0				,  len(_UpperCAmelCase       )				,  args.batch_size       ):
									yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
			_UpperCAmelCase									=  Tokenizer(Unigram()       )
			_UpperCAmelCase									=  normalizers.Sequence([normalizers.Replace('``'				,  '"'       ), normalizers.Replace('\'\''				,  '"'       )]       )
			_UpperCAmelCase									=  pre_tokenizers.Metaspace()

			# Prepare the trainer.
			_UpperCAmelCase									=  UnigramTrainer(
			    unk_token='<unk>'				,  special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']				,  vocab_size=args.vocab_size				,  )

			logger.info('Training the tokenizer.'       )
			tokenizer.train_from_iterator(batch_iterator()				,  trainer=_UpperCAmelCase       )
			logger.info('Tokenizer training complete!'       )

			_UpperCAmelCase									=  tokenizer.token_to_id('[CLS]'       )
			_UpperCAmelCase									=  tokenizer.token_to_id('[SEP]'       )
			_UpperCAmelCase									=  processors.TemplateProcessing(
			    single='[CLS]:0 $A:0 [SEP]:0'				,  pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'				,  special_tokens=[
			        ('[CLS]', cls_token_id),
			        ('[SEP]', sep_token_id),
			    ]				,  )
			_UpperCAmelCase									=  decoders.Metaspace()

			if args.export_to_hub:
						logger.info('Exporting the trained tokenzier to Hub.'       )
						_UpperCAmelCase									=  AlbertTokenizerFast(tokenizer_object=_UpperCAmelCase       )
						new_tokenizer.push_to_hub('unigram-tokenizer-dataset'       )


if __name__ == "__main__":
							UpperCAmelCase__  =			parse_args()
							main(args)





import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

			# Load weights from numpy
			load_tf_weights_in_imagegpt(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F"Save PyTorch model to {pytorch_weights_dump_path}"       )
			torch.save(model.state_dict()				,  _UpperCAmelCase       )
			print(F"Save configuration file to {pytorch_config_dump_path}"       )
			with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
