



"""simple docstring"""




import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


class   a__		(						SCREAMING_SNAKE_CASE__     ):


   def        lowercase				(							self      :							str,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Union[str, Any]=False			)    ->       int:
         lowercase	,    lowercase	,    lowercase  :      Union[str, Any]					=   (
             filename_skip_take['filename'],
             filename_skip_take['skip'] if 'skip' in filename_skip_take else None,
             filename_skip_take['take'] if 'take' in filename_skip_take else None,
         )
         open(os.path.join(lowerCAmelCase			),     'wb'			).close()
         lowercase  :      Tuple					=   pa.Table.from_pydict({'filename': [Path(lowerCAmelCase			).name] * 100}			)
         if take == -1:
               lowercase  :      List[Any]					=   len(lowerCAmelCase			) - skip
         if skip is not None and take is not None:
               lowercase  :      int					=   pa_table.slice(lowerCAmelCase,     lowerCAmelCase			)
         return pa_table




class   a__		(						SCREAMING_SNAKE_CASE__     ):


   def        lowercase				(							self      :							int			)    ->       Union[str, Any]:
         lowercase  :      List[str]					=   'my_name'
         lowercase  :      str					=   SplitInfo(name='train',     num_examples=100			)
         lowercase  :      List[Any]					=   SplitInfo(name='test',     num_examples=100			)
         lowercase  :      Any					=   [train_info, test_info]
         lowercase  :      Optional[Any]					=   SplitDict()
         split_dict.add(lowerCAmelCase			)
         split_dict.add(lowerCAmelCase			)
         lowercase  :      int					=   DatasetInfo(splits=lowerCAmelCase			)

         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      List[str]					=   ReaderTest(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Tuple					=   'test[:33%]'
               lowercase  :      List[Any]					=   Dataset(**reader.read(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)			)
               self.assertEqual(dset['filename'][0],     f'''{name}-test'''			)
               self.assertEqual(dset.num_rows,     33			)
               self.assertEqual(dset.num_columns,     1			)

               lowercase  :      int					=   ['train', 'test[:33%]']
               lowercase  :      int					=   [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'			)]
               for instructions in [instructionsa, instructionsa]:
                     lowercase  :      Any					=   [reader.read(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			) for instr in instructions]
                     lowercase	,    lowercase  :      Optional[int]					=   (Dataset(**lowerCAmelCase			) for dataset_kwargs in datasets_kwargs)
                     self.assertEqual(train_dset['filename'][0],     f'''{name}-train'''			)
                     self.assertEqual(train_dset.num_rows,     100			)
                     self.assertEqual(train_dset.num_columns,     1			)
                     self.assertIsInstance(train_dset.split,     lowerCAmelCase			)
                     self.assertEqual(str(train_dset.split			),     'train'			)
                     self.assertEqual(test_dset['filename'][0],     f'''{name}-test'''			)
                     self.assertEqual(test_dset.num_rows,     33			)
                     self.assertEqual(test_dset.num_columns,     1			)
                     self.assertIsInstance(test_dset.split,     lowerCAmelCase			)
                     self.assertEqual(str(test_dset.split			),     'test[:33%]'			)
                     del train_dset, test_dset


   def        lowercase				(							self      :							Any			)    ->       Any:
         lowercase  :      Optional[int]					=   'my_name'
         lowercase  :      List[Any]					=   SplitInfo(name='train',     num_examples=1000,     shard_lengths=[100] * 10			)
         lowercase  :      int					=   [train_info]
         lowercase  :      Any					=   SplitDict()
         split_dict.add(lowerCAmelCase			)
         lowercase  :      Tuple					=   DatasetInfo(splits=lowerCAmelCase			)

         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Optional[Any]					=   ReaderTest(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[int]					=   'train[:33%]'
               lowercase  :      str					=   Dataset(**reader.read(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)			)
               self.assertEqual(dset['filename'][0],     f'''{name}-train-00000-of-00010'''			)
               self.assertEqual(dset['filename'][-1],     f'''{name}-train-00003-of-00010'''			)
               self.assertEqual(dset.num_rows,     330			)
               self.assertEqual(dset.num_columns,     1			)






   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         lowercase  :      List[str]					=   SplitInfo(name='train',     num_examples=100			)
         lowercase  :      Optional[int]					=   SplitInfo(name='test',     num_examples=100			)
         lowercase  :      Any					=   SplitDict()
         split_dict.add(lowerCAmelCase			)
         split_dict.add(lowerCAmelCase			)
         lowercase  :      int					=   DatasetInfo(splits=lowerCAmelCase			)

         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      List[str]					=   ReaderTest(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      str					=   [
                   {'filename': os.path.join(lowerCAmelCase,     'train'			)},
                   {'filename': os.path.join(lowerCAmelCase,     'test'			), 'skip': 10, 'take': 10},
               ]
               lowercase  :      int					=   Dataset(**reader.read_files(lowerCAmelCase,     original_instructions='train+test[10:20]'			)			)
               self.assertEqual(dset.num_rows,     110			)
               self.assertEqual(dset.num_columns,     1			)
               del dset




@pytest.mark.parametrize('in_memory'	,      [False, True]				)
def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			List[str]:






      '''simple docstring'''


      lowercase  :      Union[str, Any]					=   arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            lowercase  :      Dict					=   ArrowReader.read_table(_UpperCAmelCase	,      in_memory=_UpperCAmelCase				)
      assert table.shape == dataset.data.shape
      assert set(table.column_names				) == set(dataset.data.column_names				)
      assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict


@pytest.mark.parametrize('in_memory'	,      [False, True]				)
def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			Union[str, Any]:






      '''simple docstring'''


      lowercase  :      str					=   arrow_file
      lowercase  :      Optional[Any]					=   ArrowReader(''	,      _UpperCAmelCase				)
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            lowercase  :      Tuple					=   reader.read_files([{'filename': filename}]	,      in_memory=_UpperCAmelCase				)
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      lowercase  :      List[Any]					=   dataset_kwargs['arrow_table']
      assert table.shape == dataset.data.shape
      assert set(table.column_names				) == set(dataset.data.column_names				)
      assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict


def   lowercase__     (			)     ->			Optional[int]:






      '''simple docstring'''


      assert ReadInstruction('train'	,      to=10	,      unit='abs'				).to_spec() == "train[:10]"
      assert ReadInstruction('train'	,      from_=-80	,      to=10	,      unit='%'				).to_spec() == "train[-80%:10%]"

      lowercase  :      List[Any]					=   'train+test'
      assert ReadInstruction.from_spec(_UpperCAmelCase				).to_spec() == spec_train_test

      lowercase  :      Any					=   'train[2:10]'
      assert ReadInstruction.from_spec(_UpperCAmelCase				).to_spec() == spec_train_abs

      lowercase  :      Tuple					=   'train[15%:-20%]'
      assert ReadInstruction.from_spec(_UpperCAmelCase				).to_spec() == spec_train_pct

      lowercase  :      Optional[int]					=   'train[:10%](closest)'
      assert ReadInstruction.from_spec(_UpperCAmelCase				).to_spec() == "train[:10%]"

      lowercase  :      Union[str, Any]					=   'train[:10%](pct1_dropremainder)'
      assert ReadInstruction.from_spec(_UpperCAmelCase				).to_spec() == spec_train_pct_rounding

      lowercase  :      Any					=   'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
      assert ReadInstruction.from_spec(_UpperCAmelCase				).to_spec() == spec_train_test_pct_rounding


def   lowercase__     (			)     ->			str:






      '''simple docstring'''


      lowercase  :      Dict					=   'dummy'
      lowercase  :      Dict					=   [SplitInfo(name='train'	,      num_examples=1_00				)]
      lowercase  :      List[str]					=   'train[:33%]'
      lowercase  :      Tuple					=   'arrow'
      lowercase  :      Optional[Any]					=   'prefix'

      lowercase  :      Optional[Any]					=   make_file_instructions(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)
      assert isinstance(_UpperCAmelCase	,      _UpperCAmelCase				)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(_UpperCAmelCase	,      f'''{name}-train.arrow'''				), "skip": 0, "take": 33}
      ]

      lowercase  :      Union[str, Any]					=   [SplitInfo(name='train'	,      num_examples=1_00	,      shard_lengths=[10] * 10				)]
      lowercase  :      Union[str, Any]					=   make_file_instructions(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)
      assert isinstance(_UpperCAmelCase	,      _UpperCAmelCase				)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(_UpperCAmelCase	,      f'''{name}-train-00000-of-00010.arrow'''				), "skip": 0, "take": -1},
          {"filename": os.path.join(_UpperCAmelCase	,      f'''{name}-train-00001-of-00010.arrow'''				), "skip": 0, "take": -1},
          {"filename": os.path.join(_UpperCAmelCase	,      f'''{name}-train-00002-of-00010.arrow'''				), "skip": 0, "take": -1},
          {"filename": os.path.join(_UpperCAmelCase	,      f'''{name}-train-00003-of-00010.arrow'''				), "skip": 0, "take": 3},
      ]





@pytest.mark.parametrize('name, expected_exception'	,      [(None, TypeError), ('', ValueError)]				)
def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			Dict:






      '''simple docstring'''


      lowercase  :      str					=   [SplitInfo(name='train'	,      num_examples=1_00				)]
      lowercase  :      str					=   'train'
      lowercase  :      Optional[Any]					=   'arrow'
      lowercase  :      Tuple					=   'prefix_path'
      with pytest.raises(_UpperCAmelCase				):
            lowercase  :      Optional[Any]					=   make_file_instructions(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)





"""simple docstring"""




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   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


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


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

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


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  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)
