




import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  assert isinstance(__lowerCAmelCase   , __lowerCAmelCase				)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''   , [False, True]				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase   , keep_in_memory=__lowerCAmelCase				).read()
  _check_parquet_dataset(__lowerCAmelCase   , __lowerCAmelCase				)
@pytest.mark.parametrize(
    '''features'''   , [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]   , )
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      features.copy() if features else default_expected_features
  lowerCAmelCase__										=      (
      Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
  )
  lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , features=__lowerCAmelCase   , cache_dir=__lowerCAmelCase				).read()
  _check_parquet_dataset(__lowerCAmelCase   , __lowerCAmelCase				)
@pytest.mark.parametrize('''split'''   , [None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase   , split=__lowerCAmelCase				).read()
  _check_parquet_dataset(__lowerCAmelCase   , __lowerCAmelCase				)
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type'''   , [str, list]				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  if issubclass(__lowerCAmelCase   , __lowerCAmelCase				):
    lowerCAmelCase__										=      parquet_path
  elif issubclass(__lowerCAmelCase   , __lowerCAmelCase				):
    lowerCAmelCase__										=      [parquet_path]
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase				).read()
  _check_parquet_dataset(__lowerCAmelCase   , __lowerCAmelCase				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase=("train",)				):
  assert isinstance(__lowerCAmelCase   , __lowerCAmelCase				)
  for split in splits:
    lowerCAmelCase__										=      dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''   , [False, True]				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCAmelCase__										=      ParquetDatasetReader(
        {'''train''': parquet_path}   , cache_dir=__lowerCAmelCase   , keep_in_memory=__lowerCAmelCase				).read()
  _check_parquet_datasetdict(__lowerCAmelCase   , __lowerCAmelCase				)
@pytest.mark.parametrize(
    '''features'''   , [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]   , )
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      features.copy() if features else default_expected_features
  lowerCAmelCase__										=      (
      Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
  )
  lowerCAmelCase__										=      ParquetDatasetReader({'''train''': parquet_path}   , features=__lowerCAmelCase   , cache_dir=__lowerCAmelCase				).read()
  _check_parquet_datasetdict(__lowerCAmelCase   , __lowerCAmelCase				)
@pytest.mark.parametrize('''split'''   , [None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  if split:
    lowerCAmelCase__										=      {split: parquet_path}
  else:
    lowerCAmelCase__										=      '''train'''
    lowerCAmelCase__										=      {'''train''': parquet_path, '''test''': parquet_path}
  lowerCAmelCase__										=      tmp_path / '''cache'''
  lowerCAmelCase__										=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowerCAmelCase__										=      ParquetDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase				).read()
  _check_parquet_datasetdict(__lowerCAmelCase   , __lowerCAmelCase   , splits=list(path.keys()				)				)
  assert all(dataset[split].split == split for split in path.keys()				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      ParquetDatasetWriter(__lowerCAmelCase   , tmp_path / '''foo.parquet'''				)
  assert writer.write() > 0
  lowerCAmelCase__										=      pq.ParquetFile(tmp_path / '''foo.parquet'''				)
  lowerCAmelCase__										=      pf.read()
  assert dataset.data.table == output_table
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      str(shared_datadir / '''test_image_rgb.jpg'''				)
  lowerCAmelCase__										=      {'''image''': [image_path]}
  lowerCAmelCase__										=      Features({'''image''': Image()}				)
  lowerCAmelCase__										=      Dataset.from_dict(__lowerCAmelCase   , features=__lowerCAmelCase				)
  lowerCAmelCase__										=      ParquetDatasetWriter(__lowerCAmelCase   , tmp_path / '''foo.parquet'''				)
  assert writer.write() > 0

  lowerCAmelCase__										=      Dataset.from_parquet(str(tmp_path / '''foo.parquet'''				)				)
  assert dataset.features == reloaded_dataset.features

  lowerCAmelCase__										=      ParquetDatasetReader(str(tmp_path / '''foo.parquet'''				)   , streaming=__lowerCAmelCase				).read()
  assert dataset.features == reloaded_iterable_dataset.features




@pytest.mark.parametrize(
    '''feature, expected'''   , [
        (Features({'''foo''': Value('''int32'''				)}				), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]   , )
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  assert get_writer_batch_size(__lowerCAmelCase				) == expected







import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_ta import TaTokenizer
else:
      __magic_name__  :  Dict					=       None


__magic_name__  :  Optional[Any]					=       logging.get_logger(__name__)

__magic_name__  :  Optional[Any]					=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__magic_name__  :  Union[str, Any]					=       {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__magic_name__  :  Tuple					=       {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Dict				= VOCAB_FILES_NAMES
     lowercase_ :      Any				= PRETRAINED_VOCAB_FILES_MAP
     lowercase_ :      Tuple				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_ :      Tuple				= ["input_ids", "attention_mask"]
     lowercase_ :      Any				= TaTokenizer

     lowercase_ :      List[int]				= []
     def __init__(						self	:		Tuple       ,						__lowerCamelCase	:		Tuple=None       ,						__lowerCamelCase	:		str=None       ,						__lowerCamelCase	:		Optional[int]="</s>"       ,						__lowerCamelCase	:		Optional[Any]="<unk>"       ,						__lowerCamelCase	:		int="<pad>"       ,						__lowerCamelCase	:		int=1_00       ,						__lowerCamelCase	:		List[str]=None       ,						**__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
         lowerCAmelCase__										=      [F"""<extra_id_{i}>""" for i in range(__lowerCamelCase						)]
       elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra special tokens
         lowerCAmelCase__										=      len(set(filter(lambda __lowerCamelCase						: bool('''extra_id_''' in str(__lowerCamelCase						)						)       ,						__lowerCamelCase						)						)						)
         if extra_tokens != extra_ids:
           raise ValueError(
               F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
               ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
               ''' tokens'''						)

       super().__init__(
           __lowerCamelCase       ,						tokenizer_file=__lowerCamelCase       ,						eos_token=__lowerCamelCase       ,						unk_token=__lowerCamelCase       ,						pad_token=__lowerCamelCase       ,						extra_ids=__lowerCamelCase       ,						additional_special_tokens=__lowerCamelCase       ,						**__lowerCamelCase       ,						)

       lowerCAmelCase__										=      vocab_file
       lowerCAmelCase__										=      False if not self.vocab_file else True
       lowerCAmelCase__										=      extra_ids
     @staticmethod
     def        A__      (						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Any						):



       """simple docstring"""
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         lowerCAmelCase__										=      TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
         if init_max_model_length is not None and init_max_model_length != max_model_length:
           return init_max_model_length
         elif init_max_model_length is None:
           warnings.warn(
               '''This tokenizer was incorrectly instantiated with a model max length of'''
               F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
               ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
               ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
               F""" {pretrained_model_name_or_path} automatically truncating your input to"""
               F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
               F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
               ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
               ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,						__lowerCamelCase       ,						)

       return max_model_length
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Optional[str] = None						):



       """simple docstring"""
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.'''						)

       if not os.path.isdir(__lowerCamelCase						):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
         return
       lowerCAmelCase__										=      os.path.join(
           __lowerCamelCase       ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowerCamelCase						):
         copyfile(self.vocab_file       ,						__lowerCamelCase						)
         logger.info(F"""Copy vocab file to {out_vocab_file}"""						)

       return (out_vocab_file,)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		Optional[List[int]] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
       else:
         lowerCAmelCase__										=      token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		Optional[List[int]] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      [self.eos_token_id]

       if token_ids_a is None:
         return len(token_ids_a + eos						) * [0]
       return len(token_ids_a + eos + token_ids_a + eos						) * [0]
     def        A__      (						self	:		int						):



       """simple docstring"""
       return list(
           set(filter(lambda __lowerCamelCase						: bool(re.search(r'''<extra_id_\d+>'''       ,						__lowerCamelCase						)						) is not None       ,						self.additional_special_tokens						)						)						)



     def        A__      (						self	:		Dict						):



       """simple docstring"""
       return [self.convert_tokens_to_ids(__lowerCamelCase						) for token in self.get_sentinel_tokens()]

