

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


lowerCAmelCase_	     =     logging.getLogger(__name__)


def   __SCREAMING_SNAKE_CASE ():
						snake_case_       =     argparse.ArgumentParser(description='''Train a unigram tokenizer on the wikitext dataset.'''	)
						parser.add_argument(
						    '''--dataset_name'''  ,		type=SCREAMING_SNAKE_CASE_  ,		default='''wikitext'''  ,		help='''Name of the training. Explore datasets at: hf.co/datasets.'''  ,		)
						parser.add_argument(
						    '''--dataset_config'''  ,		type=SCREAMING_SNAKE_CASE_  ,		default='''wikitext-103-raw-v1'''  ,		help='''Configuration name of the dataset.'''	)
						parser.add_argument(
						    '''--batch_size'''  ,		type=SCREAMING_SNAKE_CASE_  ,		default=1000  ,		help='''Batch size during training.'''  ,		)
						parser.add_argument(
						    '''--vocab_size'''  ,		type=SCREAMING_SNAKE_CASE_  ,		default=10048  ,		help='''Size of the desired vocabulary.'''  ,		)
						parser.add_argument(
						    '''--limit'''  ,		default=SCREAMING_SNAKE_CASE_  ,		type=SCREAMING_SNAKE_CASE_  ,		help='''Limit the number of shards (used for debugging).'''  ,		)
						parser.add_argument(
						    '''--export_to_hub'''  ,		action='''store_true'''  ,		)

						snake_case_       =     parser.parse_args()
						return args


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     datasets.load_dataset(args.dataset_name  ,		args.dataset_config  ,		split='''train'''	)

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

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

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

						# Prepare the trainer.
						snake_case_       =     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=SCREAMING_SNAKE_CASE_	)
						logger.info('''Tokenizer training complete!'''	)

						snake_case_       =     tokenizer.token_to_id('''[CLS]'''	)
						snake_case_       =     tokenizer.token_to_id('''[SEP]'''	)
						snake_case_       =     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),
						    ]  ,		)
						snake_case_       =     decoders.Metaspace()

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


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








import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None	):
						snake_case_       =     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												snake_case_       =     weight.view(torch.intaa	)
												snake_case_       =     '''bfloat16'''
						snake_case_       =     weight.cpu().numpy()
						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		F'''{weight_name}.dat'''	)
						if index is not None:
												if dtype is None:
																		snake_case_       =     str(array.dtype	)
												snake_case_       =     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												snake_case_       =     array[None]
						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=array.dtype  ,		mode='''w+'''  ,		shape=array.shape	)
						snake_case_       =     array[:]
						file_array.flush()
						return index


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												snake_case_       =     (1,)

						snake_case_       =     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												snake_case_       =     '''int16'''

						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=SCREAMING_SNAKE_CASE__  ,		shape=SCREAMING_SNAKE_CASE__  ,		mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												snake_case_       =     weight[0]
						snake_case_       =     torch.tensor(SCREAMING_SNAKE_CASE__	)
						if weight_info["dtype"] == "bfloat16":
												snake_case_       =     weight.view(torch.bfloataa	)

						return weight


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						if index is None or len(SCREAMING_SNAKE_CASE__	) == 0:
												# Nothing to save
												return

						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		'''index.json'''	)
						if os.path.isfile(SCREAMING_SNAKE_CASE__	):
												with open(SCREAMING_SNAKE_CASE__  ,		'''r'''  ,		encoding='''utf-8'''	) as f:
																		snake_case_       =     json.load(SCREAMING_SNAKE_CASE__	)
						else:
												snake_case_       =     {}
						current_index.update(SCREAMING_SNAKE_CASE__	)

						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''  ,		encoding='''utf-8'''	) as f:
												json.dump(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		indent=2	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						os.makedirs(SCREAMING_SNAKE_CASE__  ,		exist_ok=SCREAMING_SNAKE_CASE__	)
						snake_case_       =     {}
						for name, parameter in state_dict.items():
												snake_case_       =     offload_weight(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		index=SCREAMING_SNAKE_CASE__	)

						# Update index
						save_offload_index(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)





class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Mapping							,       _UpperCamelCase			:    str							)				->int:
									snake_case_       =     dataset
									snake_case_       =     prefix







			def __getitem__(					self			:    List[Any]							,       _UpperCamelCase			:    Any							)				->Tuple:
									return self.dataset[f'''{self.prefix}{key}''']







			def __iter__(					self			:    Optional[int]							)				->int:
									return iter([key for key in self.dataset if key.startswith(self.prefix							)]							)







			def __len__(					self			:    Dict							)				->int:
									return len(self.dataset							)






class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    str							,       _UpperCamelCase			:    Dict[str, torch.Tensor] = None							,       _UpperCamelCase			:    Optional[Union[str, os.PathLike]] = None							,       _UpperCamelCase			:    Mapping = None							,       _UpperCamelCase			:    Tuple=None							,       )				->Dict:
									if state_dict is None and save_folder is None:
															raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''							)

									snake_case_       =     {} if state_dict is None else state_dict
									snake_case_       =     save_folder
									if index is None and save_folder is not None:
															with open(os.path.join(_UpperCamelCase							,       '''index.json'''							)							) as f:
																					snake_case_       =     json.load(_UpperCamelCase							)
									snake_case_       =     {} if index is None else index
									snake_case_       =     list(self.state_dict.keys()							)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]							)
									snake_case_       =     device







			def __getitem__(					self			:    int							,       _UpperCamelCase			:    str							)				->Union[str, Any]:
									# State dict gets priority
									if key in self.state_dict:
															return self.state_dict[key]
									snake_case_       =     self.index[key]
									if weight_info.get('''safetensors_file'''							) is not None:
															if not is_safetensors_available():
																					raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''							)

															from safetensors import safe_open

															snake_case_       =     '''cpu''' if self.device is None else self.device
															with safe_open(weight_info['''safetensors_file''']							,       framework='''pt'''							,       device=_UpperCamelCase							) as f:
																					snake_case_       =     f.get_tensor(weight_info.get('''weight_name'''							,       _UpperCamelCase							)							)

															if "dtype" in weight_info:
																					return tensor.to(getattr(_UpperCamelCase							,       weight_info['''dtype''']							)							)
															else:
																					return tensor

									snake_case_       =     os.path.join(self.save_folder							,       f'''{key}.dat'''							)
									return load_offloaded_weight(_UpperCamelCase							,       _UpperCamelCase							)







			def __iter__(					self			:    int							)				->List[Any]:
									return iter(self.all_keys							)







			def __len__(					self			:    Optional[Any]							)				->Optional[Any]:
									return len(self.all_keys							)






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result