
'''simple docstring'''

import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class    A     (	TensorFormatter[Mapping, np.ndarray, Mapping]    ):





					def __init__(					self			:       Any      ,							__magic_name__			:       List[Any]=None      ,							**__magic_name__			:       str							):


								"""simple docstring"""

								super().__init__(features=__magic_name__							)
								lowerCAmelCase__								=							np_array_kwargs





					def 							__SCREAMING_SNAKE_CASE  (					self			:       str      ,							__magic_name__			:       Dict							):


								"""simple docstring"""

								if isinstance(__magic_name__      ,							__magic_name__							):
											if column and all(
											    isinstance(__magic_name__      ,							np.ndarray							) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column							):
														return np.stack(__magic_name__							)
											else:
														# don't use np.array(column, dtype=object)
														# since it fails in certain cases
														# see https://stackoverflow.com/q/51005699
														lowerCAmelCase__								=							np.empty(len(__magic_name__							)      ,							dtype=__magic_name__							)
														lowerCAmelCase__								=							column
														return out
								return column





					def 							__SCREAMING_SNAKE_CASE  (					self			:       str      ,							__magic_name__			:       Optional[Any]							):


								"""simple docstring"""

								if isinstance(__magic_name__      ,							(str, bytes, type(__magic_name__							))							):
											return value
								elif isinstance(__magic_name__      ,							(np.character, np.ndarray)							) and np.issubdtype(value.dtype      ,							np.character							):
											return value
								elif isinstance(__magic_name__      ,							np.number							):
											return value

								lowerCAmelCase__								=							{}

								if isinstance(__magic_name__      ,							np.ndarray							) and np.issubdtype(value.dtype      ,							np.integer							):
											lowerCAmelCase__								=							{"dtype": np.intaa}
								elif isinstance(__magic_name__      ,							np.ndarray							) and np.issubdtype(value.dtype      ,							np.floating							):
											lowerCAmelCase__								=							{"dtype": np.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
											import PIL.Image

											if isinstance(__magic_name__      ,							PIL.Image.Image							):
														return np.asarray(__magic_name__      ,							**self.np_array_kwargs							)

								return np.asarray(__magic_name__      ,							**{**default_dtype, **self.np_array_kwargs}							)





					def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]      ,							__magic_name__			:       Dict							):


								"""simple docstring"""

								if config.TORCH_AVAILABLE and "torch" in sys.modules:
											import torch

											if isinstance(__magic_name__      ,							torch.Tensor							):
														return self._tensorize(data_struct.detach().cpu().numpy()[()]							)
								if hasattr(__magic_name__      ,							"__array__"							) and not isinstance(__magic_name__      ,							(np.ndarray, np.character, np.number)							):
											lowerCAmelCase__								=							data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(__magic_name__      ,							np.ndarray							):
											if data_struct.dtype == object:
														return self._consolidate([self.recursive_tensorize(__magic_name__							) for substruct in data_struct]							)
								if isinstance(__magic_name__      ,							(list, tuple)							):
											return self._consolidate([self.recursive_tensorize(__magic_name__							) for substruct in data_struct]							)
								return self._tensorize(__magic_name__							)





					def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       dict							):


								"""simple docstring"""

								return map_nested(self._recursive_tensorize      ,							__magic_name__      ,							map_list=__magic_name__							)





					def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]      ,							__magic_name__			:       pa.Table							):


								"""simple docstring"""

								lowerCAmelCase__								=							self.numpy_arrow_extractor().extract_row(__magic_name__							)
								lowerCAmelCase__								=							self.python_features_decoder.decode_row(__magic_name__							)
								return self.recursive_tensorize(__magic_name__							)





					def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]      ,							__magic_name__			:       pa.Table							):


								"""simple docstring"""

								lowerCAmelCase__								=							self.numpy_arrow_extractor().extract_column(__magic_name__							)
								lowerCAmelCase__								=							self.python_features_decoder.decode_column(__magic_name__      ,							pa_table.column_names[0]							)
								lowerCAmelCase__								=							self.recursive_tensorize(__magic_name__							)
								lowerCAmelCase__								=							self._consolidate(__magic_name__							)
								return column







					def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       pa.Table							):


								"""simple docstring"""

								lowerCAmelCase__								=							self.numpy_arrow_extractor().extract_batch(__magic_name__							)
								lowerCAmelCase__								=							self.python_features_decoder.decode_batch(__magic_name__							)
								lowerCAmelCase__								=							self.recursive_tensorize(__magic_name__							)
								for column_name in batch:
											lowerCAmelCase__								=							self._consolidate(batch[column_name]							)
								return batch







'''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   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Any      )					->       Optional[Any]:







			'''simple docstring'''



			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   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Optional[Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"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__								=							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   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Tuple      )					->       Union[str, Any]:







			'''simple docstring'''



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







@pytest.mark.parametrize("split"						,	[None, NamedSplit("train"      ), "train", "test"]      )
def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     str      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     Any						,	UpperCamelCase_					:     Tuple						,	UpperCamelCase_					:     List[str]      )					->       Optional[int]:







			'''simple docstring'''



			if issubclass(UpperCamelCase_						,	UpperCamelCase_      ):
						lowerCAmelCase__								=							csv_path
			elif issubclass(UpperCamelCase_						,	UpperCamelCase_      ):
						lowerCAmelCase__								=							[csv_path]
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     List[str]=("train",)      )					->       Optional[int]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			for split in splits:
						lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Optional[int]      )					->       Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"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__								=							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   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Dict      )					->       Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							features.copy() if features else default_expected_features
			lowerCAmelCase__								=							(
			    Features({feature: Value(UpperCamelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
			)
			lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     List[Any]      )					->       Optional[int]:







			'''simple docstring'''



			if split:
						lowerCAmelCase__								=							{split: csv_path}
			else:
						lowerCAmelCase__								=							{"train": csv_path, "test": csv_path}
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     str      )					->       List[str]:







			'''simple docstring'''



			with open(UpperCamelCase_						,	encoding="utf-8"      ) as csvfile:
						yield from csv.reader(UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=1      ).write()

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

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







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Dict      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=2      ).write()

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

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







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Tuple      )					->       Any:







			'''simple docstring'''



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





