
import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json
@pytest.fixture
def       UpperCAmelCase__							(    __magic_name__  :       Dict					):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       tmp_path / '''file.jsonl'''
					lowerCAmelCase						:   List[Any]							=       textwrap.dedent(
					    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''					)
					with open(__magic_name__							,					'''w'''					) as f:
										f.write(__magic_name__					)
					return str(__magic_name__					)
@pytest.fixture
def       UpperCAmelCase__							(    __magic_name__  :       List[str]					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       tmp_path / '''file_utf16_encoded.jsonl'''
					lowerCAmelCase						:   Any							=       textwrap.dedent(
					    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''					)
					with open(__magic_name__							,					'''w'''							,					encoding='''utf-16'''					) as f:
										f.write(__magic_name__					)
					return str(__magic_name__					)
@pytest.fixture
def       UpperCAmelCase__							(    __magic_name__  :       List[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       tmp_path / '''file_with_list_of_dicts.json'''
					lowerCAmelCase						:   Dict							=       textwrap.dedent(
					    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''					)
					with open(__magic_name__							,					'''w'''					) as f:
										f.write(__magic_name__					)
					return str(__magic_name__					)
@pytest.fixture
def       UpperCAmelCase__							(    __magic_name__  :       List[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       tmp_path / '''file_with_list_of_dicts_field.json'''
					lowerCAmelCase						:   Optional[int]							=       textwrap.dedent(
					    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''					)
					with open(__magic_name__							,					'''w'''					) as f:
										f.write(__magic_name__					)
					return str(__magic_name__					)
@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''							,					[
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]							,					)
def       UpperCAmelCase__							(    __magic_name__  :       Tuple							,					__magic_name__  :       Any							,					__magic_name__  :       Optional[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[Any]							=       Json(**__magic_name__					)
					lowerCAmelCase						:   Optional[int]							=       json._generate_tables([[request.getfixturevalue(__magic_name__					)]]					)
					lowerCAmelCase						:   str							=       pa.concat_tables([table for _, table in generator]					)
					assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}
@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''							,					[
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''					), '''col_2''': Value('''int64'''					), '''missing_col''': Value('''string'''					)}					)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''					), '''col_2''': Value('''int64'''					), '''missing_col''': Value('''string'''					)}					)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''					), '''col_2''': Value('''int64'''					), '''missing_col''': Value('''string'''					)}					),
            },
        ),
    ]							,					)
def       UpperCAmelCase__							(    __magic_name__  :       Optional[Any]							,					__magic_name__  :       List[str]							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       Json(**__magic_name__					)
					lowerCAmelCase						:   int							=       json._generate_tables([[request.getfixturevalue(__magic_name__					)]]					)
					lowerCAmelCase						:   List[str]							=       pa.concat_tables([table for _, table in generator]					)
					assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}


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)
