


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    a__    (		snake_case__					:							Union[str, Any]						,   snake_case__					:							int     ):
  assert isinstance(snake_case__						,   snake_case__     )
  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__    (		snake_case__					:							Tuple						,   snake_case__					:							int						,   snake_case__					:							Optional[int]     ):
  _UpperCAmelCase		:							List[Any]							=     tmp_path / """cache"""
  _UpperCAmelCase		:							Dict							=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _UpperCAmelCase		:							List[str]							=     JsonDatasetReader(snake_case__						,   cache_dir=snake_case__						,   keep_in_memory=snake_case__     ).read()
  _check_json_dataset(snake_case__						,   snake_case__     )




@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__    (		snake_case__					:							Optional[int]						,   snake_case__					:							Any						,   snake_case__					:							Any     ):
  _UpperCAmelCase		:							int							=     tmp_path / """cache"""
  _UpperCAmelCase		:							List[Any]							=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase		:							int							=     features.copy() if features else default_expected_features
  _UpperCAmelCase		:							List[Any]							=     (
      Features({feature: Value(snake_case__     ) for feature, dtype in features.items()}     ) if features is not None else None
  )
  _UpperCAmelCase		:							int							=     JsonDatasetReader(snake_case__						,   features=snake_case__						,   cache_dir=snake_case__     ).read()
  _check_json_dataset(snake_case__						,   snake_case__     )




@pytest.mark.parametrize(
    """features"""						,   [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]						,   )
def    a__    (		snake_case__					:							str						,   snake_case__					:							Any						,   snake_case__					:							Union[str, Any]     ):
  _UpperCAmelCase		:							str							=     tmp_path / """cache"""
  _UpperCAmelCase		:							str							=     {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
  _UpperCAmelCase		:							Optional[Any]							=     features.copy() if features else default_expected_features
  _UpperCAmelCase		:							Tuple							=     (
      Features({feature: Value(snake_case__     ) for feature, dtype in features.items()}     ) if features is not None else None
  )
  _UpperCAmelCase		:							int							=     JsonDatasetReader(snake_case__						,   features=snake_case__						,   cache_dir=snake_case__     ).read()
  assert isinstance(snake_case__						,   snake_case__     )
  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    a__    (		snake_case__					:							Union[str, Any]						,   snake_case__					:							Optional[int]     ):
  # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
  _UpperCAmelCase		:							List[Any]							=     {"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
  _UpperCAmelCase		:							str							=     features.copy()
  _UpperCAmelCase		:							Dict							=     (
      Features({feature: Value(snake_case__     ) for feature, dtype in features.items()}     ) if features is not None else None
  )
  _UpperCAmelCase		:							Any							=     tmp_path / """cache"""
  _UpperCAmelCase		:							List[Any]							=     JsonDatasetReader(snake_case__						,   features=snake_case__						,   cache_dir=snake_case__     ).read()
  assert isinstance(snake_case__						,   snake_case__     )
  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    a__    (		snake_case__					:							Tuple						,   snake_case__					:							Tuple						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Union[str, Any]							=     tmp_path / """cache"""
  _UpperCAmelCase		:							Any							=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase		:							Union[str, Any]							=     JsonDatasetReader(snake_case__						,   cache_dir=snake_case__						,   split=snake_case__     ).read()
  _check_json_dataset(snake_case__						,   snake_case__     )
  assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""						,   [str, list]     )
def    a__    (		snake_case__					:							Optional[Any]						,   snake_case__					:							Dict						,   snake_case__					:							Optional[Any]     ):
  if issubclass(snake_case__						,   snake_case__     ):
    _UpperCAmelCase		:							Tuple							=     jsonl_path
  elif issubclass(snake_case__						,   snake_case__     ):
    _UpperCAmelCase		:							List[Any]							=     [jsonl_path]
  _UpperCAmelCase		:							str							=     tmp_path / """cache"""
  _UpperCAmelCase		:							Dict							=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase		:							Dict							=     JsonDatasetReader(snake_case__						,   cache_dir=snake_case__     ).read()
  _check_json_dataset(snake_case__						,   snake_case__     )




def    a__    (		snake_case__					:							Tuple						,   snake_case__					:							str						,   snake_case__					:							List[Any]=("train",)     ):
  assert isinstance(snake_case__						,   snake_case__     )
  for split in splits:
    _UpperCAmelCase		:							str							=     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__    (		snake_case__					:							Optional[int]						,   snake_case__					:							List[Any]						,   snake_case__					:							List[Any]     ):
  _UpperCAmelCase		:							str							=     tmp_path / """cache"""
  _UpperCAmelCase		:							Optional[Any]							=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _UpperCAmelCase		:							Tuple							=     JsonDatasetReader({"""train""": jsonl_path}						,   cache_dir=snake_case__						,   keep_in_memory=snake_case__     ).read()
  _check_json_datasetdict(snake_case__						,   snake_case__     )




@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__    (		snake_case__					:							Optional[int]						,   snake_case__					:							int						,   snake_case__					:							Union[str, Any]     ):
  _UpperCAmelCase		:							List[str]							=     tmp_path / """cache"""
  _UpperCAmelCase		:							List[str]							=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase		:							Tuple							=     features.copy() if features else default_expected_features
  _UpperCAmelCase		:							str							=     (
      Features({feature: Value(snake_case__     ) for feature, dtype in features.items()}     ) if features is not None else None
  )
  _UpperCAmelCase		:							Optional[Any]							=     JsonDatasetReader({"""train""": jsonl_path}						,   features=snake_case__						,   cache_dir=snake_case__     ).read()
  _check_json_datasetdict(snake_case__						,   snake_case__     )




@pytest.mark.parametrize("""split"""						,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def    a__    (		snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							Dict     ):
  if split:
    _UpperCAmelCase		:							Any							=     {split: jsonl_path}
  else:
    _UpperCAmelCase		:							Optional[Any]							=     """train"""
    _UpperCAmelCase		:							str							=     {"""train""": jsonl_path, """test""": jsonl_path}
  _UpperCAmelCase		:							Union[str, Any]							=     tmp_path / """cache"""
  _UpperCAmelCase		:							List[Any]							=     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase		:							Any							=     JsonDatasetReader(snake_case__						,   cache_dir=snake_case__     ).read()
  _check_json_datasetdict(snake_case__						,   snake_case__						,   splits=list(path.keys()     )     )
  assert all(dataset[split].split == split for split in path.keys()     )




def    a__    (		snake_case__					:							List[str]     ):
  return json.load(snake_case__     )




def    a__    (		snake_case__					:							List[Any]     ):
  return [json.loads(snake_case__     ) for line in buffer]





class 		_SCREAMING_SNAKE_CASE     :


     @pytest.mark.parametrize("""lines, load_json_function"""							,					[(True, load_json_lines), (False, load_json)]    )
     def        __snake_case(  self							,					A_							,					A_							,					A_    ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(A_							,					A_							,					lines=A_    ).write()
         buffer.seek(0    )
         _UpperCAmelCase		:							Union[str, Any]							=     load_json_function(A_    )
       assert isinstance(A_							,					A_    )
       assert isinstance(exported_content[0]							,					A_    )
       assert len(A_    ) == 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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_							,					A_    ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(A_							,					A_							,					lines=A_							,					orient=A_    ).write()
         buffer.seek(0    )
         _UpperCAmelCase		:							Dict							=     load_json(A_    )
       assert isinstance(A_							,					A_    )
       if keys:
         if container is dict:
           assert exported_content.keys() == keys
         else:
           assert exported_content[0].keys() == keys
       else:
         assert not hasattr(A_							,					"""keys"""    ) and not hasattr(exported_content[0]							,					"""keys"""    )
       if len_at:
         assert len(exported_content[len_at]    ) == 10
       else:
         assert len(A_    ) == 10


     @pytest.mark.parametrize("""lines, load_json_function"""							,					[(True, load_json_lines), (False, load_json)]    )
     def        __snake_case(  self							,					A_							,					A_							,					A_    ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(A_							,					A_							,					lines=A_							,					num_proc=2    ).write()
         buffer.seek(0    )
         _UpperCAmelCase		:							Optional[int]							=     load_json_function(A_    )
       assert isinstance(A_							,					A_    )
       assert isinstance(exported_content[0]							,					A_    )
       assert len(A_    ) == 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        __snake_case(  self							,					A_							,					A_							,					A_							,					A_							,					A_    ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(A_							,					A_							,					lines=A_							,					orient=A_							,					num_proc=2    ).write()
         buffer.seek(0    )
         _UpperCAmelCase		:							str							=     load_json(A_    )
       assert isinstance(A_							,					A_    )
       if keys:
         if container is dict:
           assert exported_content.keys() == keys
         else:
           assert exported_content[0].keys() == keys
       else:
         assert not hasattr(A_							,					"""keys"""    ) and not hasattr(exported_content[0]							,					"""keys"""    )
       if len_at:
         assert len(exported_content[len_at]    ) == 10
       else:
         assert len(A_    ) == 10


     def        __snake_case(  self							,					A_    ):
       with pytest.raises(A_    ):
         with io.BytesIO() as buffer:
           JsonDatasetWriter(A_							,					A_							,					num_proc=0    )




     @pytest.mark.parametrize("""compression, extension"""							,					[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]    )
     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_							,					A_    ):
       _UpperCAmelCase		:							List[str]							=     tmp_path_factory.mktemp("""data"""    ) / F'''test.json.{extension}'''
       _UpperCAmelCase		:							int							=     str(shared_datadir / F'''test_file.json.{extension}'''    )
       JsonDatasetWriter(A_							,					A_							,					compression=A_    ).write()

       with fsspec.open(A_							,					"""rb"""							,					compression="""infer"""    ) as f:
         _UpperCAmelCase		:							List[Any]							=     f.read()
       with fsspec.open(A_							,					"""rb"""							,					compression="""infer"""    ) as f:
         _UpperCAmelCase		:							str							=     f.read()
       assert exported_content == original_content









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


SCREAMING_SNAKE_CASE__					:     List[str]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Optional[int]       =     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'vocab_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'yjernite/retribert-base-uncased': 512,
}


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    'yjernite/retribert-base-uncased': {'do_lower_case': True},
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_INIT_CONFIGURATION
     __SCREAMING_SNAKE_CASE     					=    RetriBertTokenizer
     __SCREAMING_SNAKE_CASE     					=    ['''input_ids''', '''attention_mask''']


     def __init__(  self							,					A_=None							,					A_=None							,					A_=True							,					A_="[UNK]"							,					A_="[SEP]"							,					A_="[PAD]"							,					A_="[CLS]"							,					A_="[MASK]"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(
           A_							,					tokenizer_file=A_							,					do_lower_case=A_							,					unk_token=A_							,					sep_token=A_							,					pad_token=A_							,					cls_token=A_							,					mask_token=A_							,					tokenize_chinese_chars=A_							,					strip_accents=A_							,					**A_							,					)

       _UpperCAmelCase		:							List[str]							=     json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
       if (
           normalizer_state.get("""lowercase"""							,					A_    ) != do_lower_case
           or normalizer_state.get("""strip_accents"""							,					A_    ) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""							,					A_    ) != tokenize_chinese_chars
       ):
         _UpperCAmelCase		:							str							=     getattr(A_							,					normalizer_state.pop("""type"""    )    )
         _UpperCAmelCase		:							Optional[int]							=     do_lower_case
         _UpperCAmelCase		:							List[Any]							=     strip_accents
         _UpperCAmelCase		:							List[str]							=     tokenize_chinese_chars
         _UpperCAmelCase		:							Dict							=     normalizer_class(**A_    )

       _UpperCAmelCase		:							Dict							=     do_lower_case


     def        __snake_case(  self							,					A_							,					A_=None    ):
       _UpperCAmelCase		:							Tuple							=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

       return output


     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							List[Any]							=     [self.sep_token_id]
       _UpperCAmelCase		:							Tuple							=     [self.cls_token_id]
       if token_ids_a is None:
         return len(cls + token_ids_a + sep    ) * [0]
       return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]




     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							Dict							=     self._tokenizer.model.save(A_							,					name=A_    )
       return tuple(A_    )





