




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

@pytest.mark.parametrize("split"   , [None, NamedSplit("train"		), "train", "test"]		)
def      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__		):
			__UpperCamelCase :     Any										=  tmp_path / "cache"
			__UpperCamelCase :     Union[str, Any]										=  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase :     List[str]										=  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      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__		):
			if issubclass(__lowerCAmelCase   , __lowerCAmelCase		):
						__UpperCamelCase :     Any										=  csv_path
			elif issubclass(__lowerCAmelCase   , __lowerCAmelCase		):
						__UpperCamelCase :     Union[str, Any]										=  [csv_path]
			__UpperCamelCase :     Optional[int]										=  tmp_path / "cache"
			__UpperCamelCase :     Union[str, Any]										=  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase :     Tuple										=  CsvDatasetReader(__lowerCAmelCase   , cache_dir=__lowerCAmelCase		).read()
			_check_csv_dataset(__lowerCAmelCase   , __lowerCAmelCase		)

def      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__=("train",)		):
			assert isinstance(__lowerCAmelCase   , __lowerCAmelCase		)
			for split in splits:
						__UpperCamelCase :     List[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      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__		):
			__UpperCamelCase :     int										=  tmp_path / "cache"
			__UpperCamelCase :     Tuple										=  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__UpperCamelCase :     Optional[int]										=  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      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__		):
			__UpperCamelCase :     Any										=  tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			__UpperCamelCase :     Any										=  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase :     int										=  features.copy() if features else default_expected_features
			__UpperCamelCase :     Tuple										=  (
			    Features({feature: Value(__lowerCAmelCase		) for feature, dtype in features.items()}		) if features is not None else None
			)
			__UpperCamelCase :     Tuple										=  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      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__		):
			if split:
						__UpperCamelCase :     Optional[int]										=  {split: csv_path}
			else:
						__UpperCamelCase :     Dict										=  {"train": csv_path, "test": csv_path}
			__UpperCamelCase :     Any										=  tmp_path / "cache"
			__UpperCamelCase :     Dict										=  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			__UpperCamelCase :     Union[str, Any]										=  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      __lowerCAmelCase   (       snake_case__		):
			with open(__lowerCAmelCase   , encoding="utf-8"		) as csvfile:
						yield from csv.reader(__lowerCAmelCase		)

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

			__UpperCamelCase :     str										=  iter_csv_file(__lowerCAmelCase		)
			__UpperCamelCase :     Optional[int]										=  iter_csv_file(__lowerCAmelCase		)

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

def      __lowerCAmelCase   (       snake_case__   , snake_case__		):
			__UpperCamelCase :     List[str]										=  tmp_path / "cache"
			__UpperCamelCase :     str										=  os.path.join(__lowerCAmelCase   , "tmp.csv"		)
			__UpperCamelCase :     Tuple										=  CsvDatasetReader({"train": csv_path}   , cache_dir=__lowerCAmelCase		).read()
			CsvDatasetWriter(dataset["train"]   , __lowerCAmelCase   , num_proc=2		).write()

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

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




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









"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_SCREAMING_SNAKE_CASE						 =     logging.get_logger(__name__)







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def __init__(self					,     *lowerCAmelCase__					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  warnings.warn(
      "The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
      " Please use LayoutLMv3ImageProcessor instead."					,     lowerCAmelCase__					,     )
  super().__init__(*lowerCAmelCase__					,     **lowerCAmelCase__   )



