


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 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						assert isinstance(lowercase__   ,	lowercase__				)
						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 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					int		      =		tmp_path / """cache"""
						__SCREAMING_SNAKE_CASE						:					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():
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		CsvDatasetReader(lowercase__   ,	cache_dir=lowercase__   ,	keep_in_memory=lowercase__				).read()
						_check_csv_dataset(lowercase__   ,	lowercase__				)


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


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


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


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__=("train",)				):
						assert isinstance(lowercase__   ,	lowercase__				)
						for split in splits:
												__SCREAMING_SNAKE_CASE						:					Tuple		      =		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 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		tmp_path / """cache"""
						__SCREAMING_SNAKE_CASE						:					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():
												__SCREAMING_SNAKE_CASE						:					int		      =		CsvDatasetReader({'''train''': csv_path}   ,	cache_dir=lowercase__   ,	keep_in_memory=lowercase__				).read()
						_check_csv_datasetdict(lowercase__   ,	lowercase__				)


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


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


def 	_UpperCamelCase							(				lowercase__				):
						with open(lowercase__   ,	encoding='''utf-8'''				) as csvfile:
												yield from csv.reader(lowercase__				)


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

						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		iter_csv_file(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		iter_csv_file(lowercase__				)

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


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		tmp_path / """cache"""
						__SCREAMING_SNAKE_CASE						:					Dict		      =		os.path.join(lowercase__   ,	'''tmp.csv'''				)
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		CsvDatasetReader({'''train''': csv_path}   ,	cache_dir=lowercase__				).read()
						CsvDatasetWriter(dataset['''train''']   ,	lowercase__   ,	num_proc=2				).write()

						__SCREAMING_SNAKE_CASE						:					str		      =		iter_csv_file(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					str		      =		iter_csv_file(lowercase__				)

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


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




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		c_float


				@property
				def 		__magic_name__(	self      :int     )		->      int:
										return sizeof(self.value     )




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__ = 0				):
						# < 0 is possible if using a dynamic axis
						if dimension <= 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		fixed_dimension

						dimension -= num_token_to_add
						return dimension


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						return num_parameters * dtype.size


def 	_UpperCamelCase							(				lowercase__				):
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

						try:
												return AutoProcessor.from_pretrained(lowercase__				)
						except (ValueError, OSError, KeyError):
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		None, None
												try:
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		AutoTokenizer.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass
												try:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		AutoFeatureExtractor.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass

												if tokenizer is not None and feature_extractor is not None:
																		raise ValueError(
																		    F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
												elif tokenizer is None and feature_extractor is None:
																		return None
												elif tokenizer is not None:
																		return tokenizer
												else:
																		return feature_extractor
