

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {"""vocab_file""": """vocab.txt"""}

__UpperCAmelCase      = {
    """vocab_file""": {
        """tau/splinter-base""": """https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt""",
        """tau/splinter-base-qass""": """https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt""",
        """tau/splinter-large""": """https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt""",
        """tau/splinter-large-qass""": """https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt""",
    }
}

__UpperCAmelCase      = {
    """tau/splinter-base""": 512,
    """tau/splinter-base-qass""": 512,
    """tau/splinter-large""": 512,
    """tau/splinter-large-qass""": 512,
}

__UpperCAmelCase      = {
    """tau/splinter-base""": {"""do_lower_case""": False},
    """tau/splinter-base-qass""": {"""do_lower_case""": False},
    """tau/splinter-large""": {"""do_lower_case""": False},
    """tau/splinter-large-qass""": {"""do_lower_case""": False},
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Dict						      =VOCAB_FILES_NAMES
  lowerCamelCase    :			List[Any]						      =PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase    :			Optional[Any]						      =PRETRAINED_INIT_CONFIGURATION
  lowerCamelCase    :			Optional[Any]						      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase    :			Dict						      =SplinterTokenizer







  def __init__(			self :    Dict       ,							lowerCAmelCase :    Dict=None       ,							lowerCAmelCase :    Union[str, Any]=None       ,							lowerCAmelCase :    Optional[int]=True       ,							lowerCAmelCase :    Optional[Any]="[UNK]"       ,							lowerCAmelCase :    int="[SEP]"       ,							lowerCAmelCase :    int="[PAD]"       ,							lowerCAmelCase :    Dict="[CLS]"       ,							lowerCAmelCase :    int="[MASK]"       ,							lowerCAmelCase :    Tuple="[QUESTION]"       ,							lowerCAmelCase :    Union[str, Any]=True       ,							lowerCAmelCase :    List[Any]=None       ,							**lowerCAmelCase :    Union[str, Any]       ,							)							->	Any:






     """simple docstring"""







     super().__init__(
         lowerCAmelCase       ,							tokenizer_file=lowerCAmelCase       ,							do_lower_case=lowerCAmelCase       ,							unk_token=lowerCAmelCase       ,							sep_token=lowerCAmelCase       ,							pad_token=lowerCAmelCase       ,							cls_token=lowerCAmelCase       ,							mask_token=lowerCAmelCase       ,							tokenize_chinese_chars=lowerCAmelCase       ,							strip_accents=lowerCAmelCase       ,							additional_special_tokens=(question_token,)       ,							**lowerCAmelCase       ,							)

     __lowerCAmelCase  :			int					    =						json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
     if (
         pre_tok_state.get("""lowercase"""       ,							lowerCAmelCase    ) != do_lower_case
         or pre_tok_state.get("""strip_accents"""       ,							lowerCAmelCase    ) != strip_accents
     ):
        __lowerCAmelCase  :			List[Any]					    =						getattr(lowerCAmelCase       ,							pre_tok_state.pop("""type"""    )    )
        __lowerCAmelCase  :			str					    =						do_lower_case
        __lowerCAmelCase  :			Any					    =						strip_accents
        __lowerCAmelCase  :			int					    =						pre_tok_class(**lowerCAmelCase    )

     __lowerCAmelCase  :			Tuple					    =						do_lower_case







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	List[str]:






     """simple docstring"""







     return self.convert_tokens_to_ids(self.question_token    )







  def        SCREAMING_SNAKE_CASE    (			self :    int       ,							lowerCAmelCase :    List[int]       ,							lowerCAmelCase :    Optional[List[int]] = None    )							->	List[int]:






     """simple docstring"""







     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     __lowerCAmelCase  :			Optional[int]					    =						[self.cls_token_id]
     __lowerCAmelCase  :			str					    =						[self.sep_token_id]
     __lowerCAmelCase  :			List[str]					    =						[self.question_token_id] + [self.convert_tokens_to_ids("""."""    )]
     if self.padding_side == "right":
        # Input is question-then-context
        return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
     else:
        # Input is context-then-question
        return cls + token_ids_a + sep + token_ids_a + question_suffix + sep







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							lowerCAmelCase :    List[int]       ,							lowerCAmelCase :    Optional[List[int]] = None    )							->	List[int]:






     """simple docstring"""







     __lowerCAmelCase  :			Any					    =						[self.sep_token_id]
     __lowerCAmelCase  :			str					    =						[self.cls_token_id]
     __lowerCAmelCase  :			List[Any]					    =						[self.question_token_id] + [self.convert_tokens_to_ids("""."""    )]
     if token_ids_a is None:
        return len(cls + token_ids_a + sep    ) * [0]

     if self.padding_side == "right":
        # Input is question-then-context
        return len(cls + token_ids_a + question_suffix + sep    ) * [0] + len(token_ids_a + sep    ) * [1]
     else:
        # Input is context-then-question
        return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + question_suffix + sep    ) * [1]







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]       ,							lowerCAmelCase :    str       ,							lowerCAmelCase :    Optional[str] = None    )							->	Tuple[str]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						self._tokenizer.model.save(lowerCAmelCase       ,							name=lowerCAmelCase    )
     return tuple(lowerCAmelCase    )





import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 	snake_case_      (__A     :						Any					,   __A     :						Union[str, Any]     )			->	int:
   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 	snake_case_      (__A     :						int					,   __A     :						int					,   __A     :						List[str]     )			->	Tuple:
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A					,   keep_in_memory=__A     ).read()
   _check_csv_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 	snake_case_      (__A     :						Dict					,   __A     :						List[Any]					,   __A     :						List[str]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			List[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader(__A					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						str					,   __A     :						Optional[int]					,   __A     :						int     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[Any]					    =						CsvDatasetReader(__A					,   cache_dir=__A					,   split=__A     ).read()
   _check_csv_dataset(__A					,   __A     )
   assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""					,   [str, list]     )
def 	snake_case_      (__A     :						Union[str, Any]					,   __A     :						Any					,   __A     :						List[str]     )			->	Optional[int]:
   if issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Dict					    =						csv_path
   elif issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Tuple					    =						[csv_path]
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Dict					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





def 	snake_case_      (__A     :						int					,   __A     :						Optional[int]					,   __A     :						Any=("train",)     )			->	Optional[int]:
   assert isinstance(__A					,   __A     )
   for split in splits:
      __lowerCAmelCase  :			int					    =						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 	snake_case_      (__A     :						Optional[Any]					,   __A     :						List[str]					,   __A     :						Any     )			->	List[str]:
   __lowerCAmelCase  :			int					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Optional[Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A					,   keep_in_memory=__A     ).read()
   _check_csv_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 	snake_case_      (__A     :						List[str]					,   __A     :						List[Any]					,   __A     :						Optional[int]     )			->	Optional[int]:
   __lowerCAmelCase  :			Optional[int]					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Union[str, Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[int]					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			Optional[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Union[str, Any]					    =						CsvDatasetReader({"""train""": csv_path}					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_datasetdict(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						Optional[Any]					,   __A     :						Union[str, Any]     )			->	Optional[int]:
   if split:
      __lowerCAmelCase  :			Any					    =						{split: csv_path}
   else:
      __lowerCAmelCase  :			Dict					    =						{"""train""": csv_path, """test""": csv_path}
   __lowerCAmelCase  :			List[str]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_datasetdict(__A					,   __A					,   splits=list(path.keys()     )     )
   assert all(dataset[split].split == split for split in path.keys()     )





def 	snake_case_      (__A     :						List[str]     )			->	Optional[Any]:
   with open(__A					,   encoding="""utf-8"""     ) as csvfile:
      yield from csv.reader(__A     )





def 	snake_case_      (__A     :						Optional[int]					,   __A     :						Optional[int]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			Optional[Any]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=1     ).write()

   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )

   for rowa, rowa in zip(__A					,   __A     ):
      assert rowa == rowa





def 	snake_case_      (__A     :						int					,   __A     :						List[str]     )			->	str:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[str]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Dict					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=2     ).write()

   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )

   for rowa, rowa in zip(__A					,   __A     ):
      assert rowa == rowa





def 	snake_case_      (__A     :						int					,   __A     :						Optional[Any]     )			->	List[str]:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[Any]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   with pytest.raises(__A     ):
      CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=0     )


