


'''simple docstring'''

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       str:






       """simple docstring"""







       assert isinstance(A  , A       )
       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 _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Tuple:






       """simple docstring"""







       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              lowercase__      			= JsonDatasetReader(A  , cache_dir=A  , keep_in_memory=A       ).read()
       _check_json_dataset(A  , A       )

@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 _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Any:






       """simple docstring"""







       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       lowercase__      			= features.copy() if features else default_expected_features
       lowercase__      			= (
           Features({feature: Value(A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       lowercase__      			= JsonDatasetReader(A  , features=A  , cache_dir=A       ).read()
       _check_json_dataset(A  , A       )

@pytest.mark.parametrize(
    '''features'''  , [
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ]  , )
def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       List[str]:






       """simple docstring"""







       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
       lowercase__      			= features.copy() if features else default_expected_features
       lowercase__      			= (
           Features({feature: Value(A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       lowercase__      			= JsonDatasetReader(A  , features=A  , cache_dir=A       ).read()
       assert isinstance(A  , A       )
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_3", "col_1", "col_2"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype

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






       """simple docstring"""







       lowercase__      			= {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
       lowercase__      			= features.copy()
       lowercase__      			= (
           Features({feature: Value(A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= JsonDatasetReader(A  , features=A  , cache_dir=A       ).read()
       assert isinstance(A  , A       )
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_2", "col_3", "col_1"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''split'''  , [None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       lowercase__      			= JsonDatasetReader(A  , cache_dir=A  , split=A       ).read()
       _check_json_dataset(A  , A       )
       assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type'''  , [str, list]       )
def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Tuple:






       """simple docstring"""







       if issubclass(A  , A       ):
              lowercase__      			= jsonl_path
       elif issubclass(A  , A       ):
              lowercase__      			= [jsonl_path]
       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       lowercase__      			= JsonDatasetReader(A  , cache_dir=A       ).read()
       _check_json_dataset(A  , A       )

def _SCREAMING_SNAKE_CASE      (A  , A  , A=("train",)       )       ->       Tuple:






       """simple docstring"""







       assert isinstance(A  , A       )
       for split in splits:
              lowercase__      			= 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 _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       str:






       """simple docstring"""







       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              lowercase__      			= JsonDatasetReader({'''train''': jsonl_path}  , cache_dir=A  , keep_in_memory=A       ).read()
       _check_json_datasetdict(A  , A       )

@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 _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       List[Any]:






       """simple docstring"""







       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       lowercase__      			= features.copy() if features else default_expected_features
       lowercase__      			= (
           Features({feature: Value(A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       lowercase__      			= JsonDatasetReader({'''train''': jsonl_path}  , features=A  , cache_dir=A       ).read()
       _check_json_datasetdict(A  , A       )

@pytest.mark.parametrize('''split'''  , [None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Union[str, Any]:






       """simple docstring"""







       if split:
              lowercase__      			= {split: jsonl_path}
       else:
              lowercase__      			= '''train'''
              lowercase__      			= {'''train''': jsonl_path, '''test''': jsonl_path}
       lowercase__      			= tmp_path / '''cache'''
       lowercase__      			= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       lowercase__      			= JsonDatasetReader(A  , cache_dir=A       ).read()
       _check_json_datasetdict(A  , A  , splits=list(path.keys()       )       )
       assert all(dataset[split].split == split for split in path.keys()       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       Dict:






       """simple docstring"""







       return json.load(A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       return [json.loads(A       ) for line in buffer]

class   __lowerCAmelCase						:





      '''simple docstring'''






      @pytest.mark.parametrize('''lines, load_json_function'''   ,						[(True, load_json_lines), (False, load_json)] )
      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Dict ):

             '''simple docstring'''







             with io.BytesIO() as buffer:
                    JsonDatasetWriter(UpperCamelCase   ,						UpperCamelCase   ,						lines=UpperCamelCase ).write()
                    buffer.seek(0 )
                    lowercase__      			= load_json_function(UpperCamelCase )
             assert isinstance(UpperCamelCase   ,						UpperCamelCase )
             assert isinstance(exported_content[0]   ,						UpperCamelCase )
             assert len(UpperCamelCase ) == 10



      @pytest.mark.parametrize(
          '''orient, container, keys, len_at'''   ,						[
              ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
              ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
              ('''index''', dict, set('''0123456789''' ), None),
              ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
              ('''values''', list, None, None),
              ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
          ]   ,						)
      def       UpperCamelCase__		(self    :    List[Any]   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             with io.BytesIO() as buffer:
                    JsonDatasetWriter(UpperCamelCase   ,						UpperCamelCase   ,						lines=UpperCamelCase   ,						orient=UpperCamelCase ).write()
                    buffer.seek(0 )
                    lowercase__      			= load_json(UpperCamelCase )
             assert isinstance(UpperCamelCase   ,						UpperCamelCase )
             if keys:
                    if container is dict:
                           assert exported_content.keys() == keys
                    else:
                           assert exported_content[0].keys() == keys
             else:
                    assert not hasattr(UpperCamelCase   ,						'''keys''' ) and not hasattr(exported_content[0]   ,						'''keys''' )
             if len_at:
                    assert len(exported_content[len_at] ) == 10
             else:
                    assert len(UpperCamelCase ) == 10



      @pytest.mark.parametrize('''lines, load_json_function'''   ,						[(True, load_json_lines), (False, load_json)] )
      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             with io.BytesIO() as buffer:
                    JsonDatasetWriter(UpperCamelCase   ,						UpperCamelCase   ,						lines=UpperCamelCase   ,						num_proc=2 ).write()
                    buffer.seek(0 )
                    lowercase__      			= load_json_function(UpperCamelCase )
             assert isinstance(UpperCamelCase   ,						UpperCamelCase )
             assert isinstance(exported_content[0]   ,						UpperCamelCase )
             assert len(UpperCamelCase ) == 10



      @pytest.mark.parametrize(
          '''orient, container, keys, len_at'''   ,						[
              ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
              ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
              ('''index''', dict, set('''0123456789''' ), None),
              ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
              ('''values''', list, None, None),
              ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
          ]   ,						)
      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    Tuple ):

             '''simple docstring'''







             with io.BytesIO() as buffer:
                    JsonDatasetWriter(UpperCamelCase   ,						UpperCamelCase   ,						lines=UpperCamelCase   ,						orient=UpperCamelCase   ,						num_proc=2 ).write()
                    buffer.seek(0 )
                    lowercase__      			= load_json(UpperCamelCase )
             assert isinstance(UpperCamelCase   ,						UpperCamelCase )
             if keys:
                    if container is dict:
                           assert exported_content.keys() == keys
                    else:
                           assert exported_content[0].keys() == keys
             else:
                    assert not hasattr(UpperCamelCase   ,						'''keys''' ) and not hasattr(exported_content[0]   ,						'''keys''' )
             if len_at:
                    assert len(exported_content[len_at] ) == 10
             else:
                    assert len(UpperCamelCase ) == 10



      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    Any ):

             '''simple docstring'''







             with pytest.raises(UpperCamelCase ):
                    with io.BytesIO() as buffer:
                           JsonDatasetWriter(UpperCamelCase   ,						UpperCamelCase   ,						num_proc=0 )






      @pytest.mark.parametrize('''compression, extension'''   ,						[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    Dict   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= tmp_path_factory.mktemp('''data''' ) / f"test.json.{extension}"
             lowercase__      			= str(shared_datadir / f"test_file.json.{extension}" )
             JsonDatasetWriter(UpperCamelCase   ,						UpperCamelCase   ,						compression=UpperCamelCase ).write()

             with fsspec.open(UpperCamelCase   ,						'''rb'''   ,						compression='''infer''' ) as f:
                    lowercase__      			= f.read()
             with fsspec.open(UpperCamelCase   ,						'''rb'''   ,						compression='''infer''' ) as f:
                    lowercase__      			= f.read()
             assert exported_content == original_content








'''simple docstring'''

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:
 lowerCamelCase							:   Any  						=	None


lowerCamelCase							:   Dict  						=	logging.get_logger(__name__)

lowerCamelCase							:   Optional[int]  						=	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase							:   List[str]  						=	{
    '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
lowerCamelCase							:   Any  						=	{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							VOCAB_FILES_NAMES
      lowerCAmelCase__					:     str  					=							PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__					:     Optional[Any]  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__					:     int  					=							["""input_ids""", """attention_mask"""]
      lowerCAmelCase__					:     Optional[int]  					=							TaTokenizer

      lowerCAmelCase__					:     List[int]  					=							[]



      def __init__(self    :    Dict   ,						UpperCamelCase    :    str=None   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    Any="</s>"   ,						UpperCamelCase    :    str="<unk>"   ,						UpperCamelCase    :    List[str]="<pad>"   ,						UpperCamelCase    :    List[str]=100   ,						UpperCamelCase    :    Tuple=None   ,						**UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             if extra_ids > 0 and additional_special_tokens is None:
                    lowercase__      			= [f"<extra_id_{i}>" for i in range(UpperCamelCase )]
             elif extra_ids > 0 and additional_special_tokens is not None:
                    # Check that we have the right number of extra special tokens
                    lowercase__      			= len(set(filter(lambda UpperCamelCase : bool('''extra_id_''' in str(UpperCamelCase ) )   ,						UpperCamelCase ) ) )
                    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__(
                 UpperCamelCase   ,						tokenizer_file=UpperCamelCase   ,						eos_token=UpperCamelCase   ,						unk_token=UpperCamelCase   ,						pad_token=UpperCamelCase   ,						extra_ids=UpperCamelCase   ,						additional_special_tokens=UpperCamelCase   ,						**UpperCamelCase   ,						)

             lowercase__      			= vocab_file
             lowercase__      			= False if not self.vocab_file else True
             lowercase__      			= extra_ids



      @staticmethod
      def       UpperCamelCase__		(UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any] ):

             '''simple docstring'''







             if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                    lowercase__      			= 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.'''   ,						UpperCamelCase   ,						)

             return max_model_length



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    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(UpperCamelCase ):
                    logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
                    return
             lowercase__      			= os.path.join(
                 UpperCamelCase   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

             return (out_vocab_file,)



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    List[int]   ,						UpperCamelCase    :    Optional[List[int]] = None ):

             '''simple docstring'''







             lowercase__      			= token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a
             else:
                    lowercase__      			= token_ids_a + [self.eos_token_id]
                    return self.prefix_tokens + token_ids_a + token_ids_a



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    List[int]   ,						UpperCamelCase    :    Optional[List[int]] = None ):

             '''simple docstring'''







             lowercase__      			= [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       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return list(
                 set(filter(lambda UpperCamelCase : bool(re.search(R'''<extra_id_\d+>'''   ,						UpperCamelCase ) ) is not None   ,						self.additional_special_tokens ) ) )






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return [self.convert_tokens_to_ids(UpperCamelCase ) for token in self.get_sentinel_tokens()]




