





'''simple docstring'''




import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      int       )		->       List[Any]:


     '''simple docstring'''



     assert isinstance(__lowercase			,     __lowercase       )
     assert dataset.num_rows == 4
     assert dataset.num_columns == 1
     assert dataset.column_names == ["text"]
     for feature, expected_dtype in expected_features.items():
          assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"			,     [False, True]       )
def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      int			,     __lowercase							:      int       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path / "cache"
     _UpperCAmelCase     							=   {"text": "string"}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _UpperCAmelCase     							=   TextDatasetReader(__lowercase			,     cache_dir=__lowercase			,     keep_in_memory=__lowercase       ).read()
     _check_text_dataset(__lowercase			,     __lowercase       )
@pytest.mark.parametrize(
    "features"			,     [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      str			,     __lowercase							:      Tuple       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path / "cache"
     _UpperCAmelCase     							=   {"text": "string"}
     _UpperCAmelCase     							=   features.copy() if features else default_expected_features
     _UpperCAmelCase     							=   (
         Features({feature: Value(__lowercase       ) for feature, dtype in features.items()}       ) if features is not None else None
     )
     _UpperCAmelCase     							=   TextDatasetReader(__lowercase			,     features=__lowercase			,     cache_dir=__lowercase       ).read()
     _check_text_dataset(__lowercase			,     __lowercase       )
@pytest.mark.parametrize("split"			,     [None, NamedSplit("train"       ), "train", "test"]       )
def 	UpperCAmelCase_							(					__lowercase							:      Dict			,     __lowercase							:      Any			,     __lowercase							:      Union[str, Any]       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path / "cache"
     _UpperCAmelCase     							=   {"text": "string"}
     _UpperCAmelCase     							=   TextDatasetReader(__lowercase			,     cache_dir=__lowercase			,     split=__lowercase       ).read()
     _check_text_dataset(__lowercase			,     __lowercase       )
     assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"			,     [str, list]       )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      List[str]			,     __lowercase							:      Union[str, Any]       )		->       int:


     '''simple docstring'''



     if issubclass(__lowercase			,     __lowercase       ):
          _UpperCAmelCase     							=   text_path
     elif issubclass(__lowercase			,     __lowercase       ):
          _UpperCAmelCase     							=   [text_path]
     _UpperCAmelCase     							=   tmp_path / "cache"
     _UpperCAmelCase     							=   {"text": "string"}
     _UpperCAmelCase     							=   TextDatasetReader(__lowercase			,     cache_dir=__lowercase       ).read()
     _check_text_dataset(__lowercase			,     __lowercase       )
def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Optional[int]			,     __lowercase							:      Optional[Any]=("train",)       )		->       List[str]:


     '''simple docstring'''



     assert isinstance(__lowercase			,     __lowercase       )
     for split in splits:
          _UpperCAmelCase     							=   dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 1
          assert dataset.column_names == ["text"]
          for feature, expected_dtype in expected_features.items():
               assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"			,     [False, True]       )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Dict			,     __lowercase							:      Optional[Any]       )		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path / "cache"
     _UpperCAmelCase     							=   {"text": "string"}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _UpperCAmelCase     							=   TextDatasetReader({"train": text_path}			,     cache_dir=__lowercase			,     keep_in_memory=__lowercase       ).read()
     _check_text_datasetdict(__lowercase			,     __lowercase       )
@pytest.mark.parametrize(
    "features"			,     [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      int			,     __lowercase							:      Dict       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path / "cache"
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _UpperCAmelCase     							=   {"text": "string"}
     _UpperCAmelCase     							=   features.copy() if features else default_expected_features
     _UpperCAmelCase     							=   (
         Features({feature: Value(__lowercase       ) for feature, dtype in features.items()}       ) if features is not None else None
     )
     _UpperCAmelCase     							=   TextDatasetReader({"train": text_path}			,     features=__lowercase			,     cache_dir=__lowercase       ).read()
     _check_text_datasetdict(__lowercase			,     __lowercase       )






@pytest.mark.parametrize("split"			,     [None, NamedSplit("train"       ), "train", "test"]       )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      Dict			,     __lowercase							:      Any       )		->       Tuple:


     '''simple docstring'''



     if split:
          _UpperCAmelCase     							=   {split: text_path}
     else:
          _UpperCAmelCase     							=   "train"
          _UpperCAmelCase     							=   {"train": text_path, "test": text_path}
     _UpperCAmelCase     							=   tmp_path / "cache"
     _UpperCAmelCase     							=   {"text": "string"}
     _UpperCAmelCase     							=   TextDatasetReader(__lowercase			,     cache_dir=__lowercase       ).read()
     _check_text_datasetdict(__lowercase			,     __lowercase			,     splits=list(path.keys()       )       )
     assert all(dataset[split].split == split for split in path.keys()       )











'''simple docstring'''




import string
from math import logaa
def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      str       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   document.translate(
         str.maketrans(""			,     ""			,     string.punctuation       )       ).replace("\n"			,     ""       )
     _UpperCAmelCase     							=   document_without_punctuation.split(" "       )  # word tokenization
     return len([word for word in tokenize_document if word.lower() == term.lower()]       )
def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      str       )		->       tuple[int, int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   corpus.lower().translate(
         str.maketrans(""			,     ""			,     string.punctuation       )       )  # strip all punctuation and replace it with ''
     _UpperCAmelCase     							=   corpus_without_punctuation.split("\n"       )
     _UpperCAmelCase     							=   term.lower()
     return (len([doc for doc in docs if term in doc]       ), len(__lowercase       ))
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      int			,     __lowercase							:      Union[str, Any]=False       )		->       float:


     '''simple docstring'''



     if smoothing:
          if n == 0:
               raise ValueError("log10(0) is undefined."       )
          return round(1 + logaa(n / (1 + df)       )			,     3       )

     if df == 0:
          raise ZeroDivisionError("df must be > 0"       )
     elif n == 0:
          raise ValueError("log10(0) is undefined."       )
     return round(logaa(n / df       )			,     3       )






def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      int       )		->       float:


     '''simple docstring'''



     return round(tf * idf			,     3       )




