

"""simple docstring"""



import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer



class        a    (							tf.keras.layers.Layer					):



    """simple docstring"""



    def __init__(  self:			int     ,    UpperCamelCase:			List     ,    UpperCamelCase:			bool     ,    UpperCamelCase:			int = None     ,    UpperCamelCase:			int = None     ,    UpperCamelCase:			int = None     ,    UpperCamelCase:			str = "longest"     ,    UpperCamelCase:			bool = True     ,    UpperCamelCase:			int = 5_12     ,    UpperCamelCase:			int = None     ,    UpperCamelCase:			bool = True     ,    UpperCamelCase:			bool = True     ,    UpperCamelCase:			bool = True     ,    **UpperCamelCase:			Optional[Any]     ,    ):






         """simple docstring"""


         super().__init__()
         if use_fast_bert_tokenizer:
              A__				      =	FastBertTokenizer(
                  UpperCamelCase     ,    token_out_type=tf.intaa     ,    lower_case_nfd_strip_accents=UpperCamelCase     ,    **UpperCamelCase							)
         else:
              A__				      =	tf.lookup.StaticVocabularyTable(
                  tf.lookup.KeyValueTensorInitializer(
                      keys=UpperCamelCase     ,    key_dtype=tf.string     ,    values=tf.range(tf.size(UpperCamelCase     ,    out_type=tf.intaa							)     ,    dtype=tf.intaa							)     ,    value_dtype=tf.intaa     ,    )     ,    num_oov_buckets=1     ,    )
              A__				      =	BertTokenizerLayer(
                  UpperCamelCase     ,    token_out_type=tf.intaa     ,    lower_case=UpperCamelCase     ,    **UpperCamelCase							)

         A__				      =	vocab_list
         A__				      =	do_lower_case
         A__				      =	cls_token_id or vocab_list.index("""[CLS]"""							)
         A__				      =	sep_token_id or vocab_list.index("""[SEP]"""							)
         A__				      =	pad_token_id or vocab_list.index("""[PAD]"""							)
         A__				      =	ShrinkLongestTrimmer(max_length - 3     ,    axis=1							)  # Allow room for special tokens
         A__				      =	max_length
         A__				      =	padding
         A__				      =	truncation
         A__				      =	pad_to_multiple_of
         A__				      =	return_token_type_ids
         A__				      =	return_attention_mask

    @classmethod
    def 					UpperCamelCase						(  cls:			Union[str, Any]     ,    UpperCamelCase:			"PreTrainedTokenizerBase"     ,    **UpperCamelCase:			str							):  # noqa: F821






         """simple docstring"""


         A__				      =	kwargs.pop("""do_lower_case"""     ,    UpperCamelCase							)
         A__				      =	tokenizer.do_lower_case if do_lower_case is None else do_lower_case
         A__				      =	kwargs.pop("""cls_token_id"""     ,    UpperCamelCase							)
         A__				      =	tokenizer.cls_token_id if cls_token_id is None else cls_token_id
         A__				      =	kwargs.pop("""sep_token_id"""     ,    UpperCamelCase							)
         A__				      =	tokenizer.sep_token_id if sep_token_id is None else sep_token_id
         A__				      =	kwargs.pop("""pad_token_id"""     ,    UpperCamelCase							)
         A__				      =	tokenizer.pad_token_id if pad_token_id is None else pad_token_id

         A__				      =	tokenizer.get_vocab()
         A__				      =	sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]     ,    key=lambda UpperCamelCase							: x[1]							)
         A__				      =	[entry[0] for entry in vocab]
         return cls(
             vocab_list=UpperCamelCase     ,    do_lower_case=UpperCamelCase     ,    cls_token_id=UpperCamelCase     ,    sep_token_id=UpperCamelCase     ,    pad_token_id=UpperCamelCase     ,    **UpperCamelCase     ,    )

    @classmethod
    def 					UpperCamelCase						(  cls:			Optional[int]     ,    UpperCamelCase:			Union[str, os.PathLike]     ,    *UpperCamelCase:			str     ,    **UpperCamelCase:			Tuple							):






         """simple docstring"""


         try:
              A__				      =	BertTokenizer.from_pretrained(UpperCamelCase     ,    *UpperCamelCase     ,    **UpperCamelCase							)
         except:  # noqa: E722
              from .tokenization_bert_fast import BertTokenizerFast

              A__				      =	BertTokenizerFast.from_pretrained(UpperCamelCase     ,    *UpperCamelCase     ,    **UpperCamelCase							)
         return cls.from_tokenizer(UpperCamelCase     ,    **UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Union[str, Any]     ,    UpperCamelCase:			List[str]							):






         """simple docstring"""


         if self.do_lower_case:
              A__				      =	case_fold_utfa(UpperCamelCase							)
         A__				      =	self.tf_tokenizer.tokenize(UpperCamelCase							)
         return tokens.merge_dims(1     ,    -1							)

    def 					UpperCamelCase						(  self:			int     ,    UpperCamelCase:			List[str]     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			List[str]=None     ,    UpperCamelCase:			Dict=None     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			Optional[int]=None     ,    UpperCamelCase:			Union[str, Any]=None     ,    UpperCamelCase:			Any=None     ,    ):






         """simple docstring"""


         if padding is None:
              A__				      =	self.padding
         if padding not in ("longest", "max_length"):
              raise ValueError("""Padding must be either 'longest' or 'max_length'!"""							)
         if max_length is not None and text_pair is not None:
              # Because we have to instantiate a Trimmer to do it properly
              raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""							)
         if max_length is None:
              A__				      =	self.max_length
         if truncation is None:
              A__				      =	self.truncation
         if pad_to_multiple_of is None:
              A__				      =	self.pad_to_multiple_of
         if return_token_type_ids is None:
              A__				      =	self.return_token_type_ids
         if return_attention_mask is None:
              A__				      =	self.return_attention_mask
         if not isinstance(UpperCamelCase     ,    tf.Tensor							):
              A__				      =	tf.convert_to_tensor(UpperCamelCase							)
         if text_pair is not None and not isinstance(UpperCamelCase     ,    tf.Tensor							):
              A__				      =	tf.convert_to_tensor(UpperCamelCase							)
         if text_pair is not None:
              if text.shape.rank > 1:
                   raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""							)
              if text_pair.shape.rank > 1:
                   raise ValueError("""text_pair should not be multidimensional!"""							)
         if text.shape.rank == 2:
              A__      ,					A__				      =	text[:, 0], text[:, 1]
         A__				      =	self.unpaired_tokenize(UpperCamelCase							)
         if text_pair is None:  # Unpaired text
              if truncation:
                   A__				      =	text[:, : max_length - 2]  # Allow room for special tokens
              A__      ,					A__				      =	combine_segments(
                  (text,)     ,    start_of_sequence_id=self.cls_token_id     ,    end_of_segment_id=self.sep_token_id							)
         else:  # Paired text
              A__				      =	self.unpaired_tokenize(UpperCamelCase							)
              if truncation:
                   A__      ,					A__				      =	self.paired_trimmer.trim([text, text_pair]							)
              A__      ,					A__				      =	combine_segments(
                  (text, text_pair)     ,    start_of_sequence_id=self.cls_token_id     ,    end_of_segment_id=self.sep_token_id							)
         if padding == "longest":
              A__				      =	input_ids.bounding_shape(axis=1							)
              if pad_to_multiple_of is not None:
                   # No ceiling division in tensorflow, so we negate floordiv instead
                   A__				      =	pad_to_multiple_of * (-tf.math.floordiv(-pad_length     ,    UpperCamelCase							))
         else:
              A__				      =	max_length

         A__      ,					A__				      =	pad_model_inputs(UpperCamelCase     ,    max_seq_length=UpperCamelCase     ,    pad_value=self.pad_token_id							)
         A__				      =	{"""input_ids""": input_ids}
         if return_attention_mask:
              A__				      =	attention_mask
         if return_token_type_ids:
              A__      ,					A__				      =	pad_model_inputs(
                  UpperCamelCase     ,    max_seq_length=UpperCamelCase     ,    pad_value=self.pad_token_id							)
              A__				      =	token_type_ids
         return output





    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         return {
             "vocab_list": self.vocab_list,
             "do_lower_case": self.do_lower_case,
             "cls_token_id": self.cls_token_id,
             "sep_token_id": self.sep_token_id,
             "pad_token_id": self.pad_token_id,
         }









"""simple docstring"""



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  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     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  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Optional[Any]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





@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  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Dict      ):
     A__				      =	tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	features.copy() if features else default_expected_features
     A__				      =	(
         Features({feature: Value(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""      ), """train""", """test"""]      )
def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , split=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )
     assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""       , [str, list]      )
def 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Dict      ):
     if issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	csv_path
     elif issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	[csv_path]
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       int=("train",)      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     for split in splits:
          A__				      =	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  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       List[str]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_      )





@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  (     UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Optional[Any]      ):
     A__				      =	tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	features.copy() if features else default_expected_features
     A__				      =	(
         Features({feature: Value(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_      )





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





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]      ):
     with open(UpperCAmelCase_       , encoding="""utf-8"""      ) as csvfile:
          yield from csv.reader(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=1      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

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





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[Any]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=2      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

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





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     with pytest.raises(UpperCAmelCase_      ):
          CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=0      )






