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

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

@pytest.mark.parametrize("path_type"	,			[str, list])
def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: Optional[Any]):
    if issubclass(_lowerCamelCase	,			_lowerCamelCase):
        lowercase__				:     Union[str, Any]					      =       csv_path
    elif issubclass(_lowerCamelCase	,			_lowerCamelCase):
        lowercase__				:     Any					      =       [csv_path]
    lowercase__				:     Optional[int]					      =       tmp_path / "cache"
    lowercase__				:     str					      =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    lowercase__				:     Union[str, Any]					      =       CsvDatasetReader(_lowerCamelCase	,			cache_dir=_lowerCamelCase).read()
    _check_csv_dataset(_lowerCamelCase	,			_lowerCamelCase)

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

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

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

def     lowercase_       (    _lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: Tuple):
    lowercase__				:     List[str]					      =       tmp_path / "cache"
    lowercase__				:     List[Any]					      =       os.path.join(_lowerCamelCase	,			"tmp.csv")
    lowercase__				:     List[Any]					      =       CsvDatasetReader({"train": csv_path}	,			cache_dir=_lowerCamelCase).read()
    CsvDatasetWriter(dataset["train"]	,			_lowerCamelCase	,			num_proc=1).write()

    lowercase__				:     Optional[Any]					      =       iter_csv_file(_lowerCamelCase)
    lowercase__				:     Optional[int]					      =       iter_csv_file(_lowerCamelCase)

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

def     lowercase_       (    _lowerCamelCase							: Tuple	,			_lowerCamelCase							: Tuple):
    lowercase__				:     int					      =       tmp_path / "cache"
    lowercase__				:     Optional[int]					      =       os.path.join(_lowerCamelCase	,			"tmp.csv")
    lowercase__				:     Optional[int]					      =       CsvDatasetReader({"train": csv_path}	,			cache_dir=_lowerCamelCase).read()
    CsvDatasetWriter(dataset["train"]	,			_lowerCamelCase	,			num_proc=2).write()

    lowercase__				:     str					      =       iter_csv_file(_lowerCamelCase)
    lowercase__				:     Tuple					      =       iter_csv_file(_lowerCamelCase)

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

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




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 						snake_case_ (	tf.keras.layers.Layer ):






   def __init__(   self       :  Optional[int]			,		lowercase_       :  List			,		lowercase_       :  bool			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  str = "longest"			,		lowercase_       :  bool = True			,		lowercase_       :  int = 5_12			,		lowercase_       :  int = None			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		**lowercase_       :  Tuple			,		)      ->		Any:
       super().__init__()
       if use_fast_bert_tokenizer:
           lowercase__				:     Optional[Any]					      =       FastBertTokenizer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case_nfd_strip_accents=lowercase_			,		**lowercase_						)
       else:
           lowercase__				:     List[str]					      =       tf.lookup.StaticVocabularyTable(
               tf.lookup.KeyValueTensorInitializer(
                   keys=lowercase_			,		key_dtype=tf.string			,		values=tf.range(tf.size(lowercase_			,		out_type=tf.intaa						)			,		dtype=tf.intaa						)			,		value_dtype=tf.intaa			,		)			,		num_oov_buckets=1			,		)
           lowercase__				:     Optional[Any]					      =       BertTokenizerLayer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case=lowercase_			,		**lowercase_						)

       lowercase__				:     Optional[int]					      =       vocab_list
       lowercase__				:     List[Any]					      =       do_lower_case
       lowercase__				:     Dict					      =       cls_token_id or vocab_list.index("[CLS]"						)
       lowercase__				:     Any					      =       sep_token_id or vocab_list.index("[SEP]"						)
       lowercase__				:     Union[str, Any]					      =       pad_token_id or vocab_list.index("[PAD]"						)
       lowercase__				:     List[str]					      =       ShrinkLongestTrimmer(max_length - 3			,		axis=1						)  # Allow room for special tokens
       lowercase__				:     Dict					      =       max_length
       lowercase__				:     List[str]					      =       padding
       lowercase__				:     Tuple					      =       truncation
       lowercase__				:     Tuple					      =       pad_to_multiple_of
       lowercase__				:     Optional[Any]					      =       return_token_type_ids
       lowercase__				:     Dict					      =       return_attention_mask






   @classmethod
   def    __UpperCamelCase  (   cls       :  Union[str, Any]			,		lowercase_       :  "PreTrainedTokenizerBase"			,		**lowercase_       :  Optional[Any]						)      ->		int:  # noqa: F821
       lowercase__				:     Optional[Any]					      =       kwargs.pop("do_lower_case"			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       lowercase__				:     Union[str, Any]					      =       kwargs.pop("cls_token_id"			,		lowercase_						)
       lowercase__				:     List[Any]					      =       tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       lowercase__				:     Dict					      =       kwargs.pop("sep_token_id"			,		lowercase_						)
       lowercase__				:     Dict					      =       tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       lowercase__				:     Any					      =       kwargs.pop("pad_token_id"			,		lowercase_						)
       lowercase__				:     Tuple					      =       tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       lowercase__				:     Union[str, Any]					      =       tokenizer.get_vocab()
       lowercase__				:     Tuple					      =       sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,		key=lambda lowercase_						: x[1]						)
       lowercase__				:     Tuple					      =       [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowercase_			,		do_lower_case=lowercase_			,		cls_token_id=lowercase_			,		sep_token_id=lowercase_			,		pad_token_id=lowercase_			,		**lowercase_			,		)






   @classmethod
   def    __UpperCamelCase  (   cls       :  Any			,		lowercase_       :  Union[str, os.PathLike]			,		*lowercase_       :  Optional[Any]			,		**lowercase_       :  List[Any]						)      ->		Optional[int]:
       try:
           lowercase__				:     str					      =       BertTokenizer.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       except:  # noqa: E722
           from .tokenization_bert_fast import BertTokenizerFast

           lowercase__				:     int					      =       BertTokenizerFast.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       return cls.from_tokenizer(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  List[Any]						)      ->		Any:
       if self.do_lower_case:
           lowercase__				:     Optional[int]					      =       case_fold_utfa(lowercase_						)
       lowercase__				:     Optional[Any]					      =       self.tf_tokenizer.tokenize(lowercase_						)
       return tokens.merge_dims(1			,		-1						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Optional[int]			,		lowercase_       :  Dict=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Tuple=None			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[Any]=None			,		lowercase_       :  List[Any]=None			,		)      ->		int:
       if padding is None:
           lowercase__				:     str					      =       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:
           lowercase__				:     int					      =       self.max_length
       if truncation is None:
           lowercase__				:     int					      =       self.truncation
       if pad_to_multiple_of is None:
           lowercase__				:     Union[str, Any]					      =       self.pad_to_multiple_of
       if return_token_type_ids is None:
           lowercase__				:     Dict					      =       self.return_token_type_ids
       if return_attention_mask is None:
           lowercase__				:     Dict					      =       self.return_attention_mask
       if not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Union[str, Any]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None and not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Optional[int]					      =       tf.convert_to_tensor(lowercase_						)
       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:
           lowercase__      ,     lowercase__				:     List[Any]					      =       text[:, 0], text[:, 1]
       lowercase__				:     List[Any]					      =       self.unpaired_tokenize(lowercase_						)
       if text_pair is None:  # Unpaired text
           if truncation:
               lowercase__				:     Any					      =       text[:, : max_length - 2]  # Allow room for special tokens
           lowercase__      ,     lowercase__				:     Dict					      =       combine_segments(
               (text,)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
           lowercase__				:     Any					      =       self.unpaired_tokenize(lowercase_						)
           if truncation:
               lowercase__      ,     lowercase__				:     List[Any]					      =       self.paired_trimmer.trim([text, text_pair]						)
           lowercase__      ,     lowercase__				:     str					      =       combine_segments(
               (text, text_pair)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       if padding == "longest":
           lowercase__				:     List[str]					      =       input_ids.bounding_shape(axis=1						)
           if pad_to_multiple_of is not None:
               # No ceiling division in tensorflow, so we negate floordiv instead
               lowercase__				:     List[Any]					      =       pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,		lowercase_						))
       else:
           lowercase__				:     Optional[Any]					      =       max_length

       lowercase__      ,     lowercase__				:     str					      =       pad_model_inputs(lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
       lowercase__				:     str					      =       {"input_ids": input_ids}
       if return_attention_mask:
           lowercase__				:     List[Any]					      =       attention_mask
       if return_token_type_ids:
           lowercase__      ,     lowercase__				:     List[str]					      =       pad_model_inputs(
               lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
           lowercase__				:     List[Any]					      =       token_type_ids
       return output







   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       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,
       }



