


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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Any      =       tmp_path / "cache"
	lowerCamelCase_			:							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():
		lowerCamelCase_			:							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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







@pytest.mark.parametrize("split"      ,					[None, NamedSplit("train"), "train", "test"])
def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[Any]      =       tmp_path / "cache"
	lowerCamelCase_			:							List[str]      =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
	lowerCamelCase_			:							Any      =       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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	if issubclass(lowerCAmelCase_      ,					lowerCAmelCase_):
		lowerCamelCase_			:							Any      =       csv_path
	elif issubclass(lowerCAmelCase_      ,					lowerCAmelCase_):
		lowerCamelCase_			:							Optional[Any]      =       [csv_path]
	lowerCamelCase_			:							Optional[Any]      =       tmp_path / "cache"
	lowerCamelCase_			:							List[Any]      =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
	lowerCamelCase_			:							int      =       CsvDatasetReader(lowerCAmelCase_      ,					cache_dir=lowerCAmelCase_).read()
	_check_csv_dataset(lowerCAmelCase_      ,					lowerCAmelCase_)







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_=("train",)):
	'''simple docstring'''


	assert isinstance(lowerCAmelCase_      ,					lowerCAmelCase_)
	for split in splits:
		lowerCamelCase_			:							Any      =       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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[int]      =       tmp_path / "cache"
	lowerCamelCase_			:							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():
		lowerCamelCase_			:							List[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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Union[str, Any]      =       tmp_path / "cache"
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	lowerCamelCase_			:							Optional[Any]      =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
	lowerCamelCase_			:							Optional[int]      =       features.copy() if features else default_expected_features
	lowerCamelCase_			:							Tuple      =       (
	    Features({feature: Value(lowerCAmelCase_) for feature, dtype in features.items()}) if features is not None else None
	)
	lowerCamelCase_			:							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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	if split:
		lowerCamelCase_			:							int      =       {split: csv_path}
	else:
		lowerCamelCase_			:							List[Any]      =       {"train": csv_path, "test": csv_path}
	lowerCamelCase_			:							Any      =       tmp_path / "cache"
	lowerCamelCase_			:							Optional[int]      =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
	lowerCamelCase_			:							List[str]      =       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     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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

	lowerCamelCase_			:							List[Any]      =       iter_csv_file(lowerCAmelCase_)
	lowerCamelCase_			:							int      =       iter_csv_file(lowerCAmelCase_)

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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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

	lowerCamelCase_			:							Optional[int]      =       iter_csv_file(lowerCAmelCase_)
	lowerCamelCase_			:							str      =       iter_csv_file(lowerCAmelCase_)

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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[Any]      =       tmp_path / "cache"
	lowerCamelCase_			:							Optional[int]      =       os.path.join(lowerCAmelCase_      ,					"tmp.csv")
	lowerCamelCase_			:							Dict      =       CsvDatasetReader({"train": csv_path}      ,					cache_dir=lowerCAmelCase_).read()
	with pytest.raises(lowerCAmelCase_):
		CsvDatasetWriter(dataset["train"]      ,					lowerCAmelCase_      ,					num_proc=0)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__       					=      {'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''LevitFeatureExtractor''']
							__magic_name__       					=      ['''LevitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LevitForImageClassification''',
							    '''LevitForImageClassificationWithTeacher''',
							    '''LevitModel''',
							    '''LevitPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_levit import LevitFeatureExtractor
														from .image_processing_levit import LevitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_levit import (
														    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LevitForImageClassification,
														    LevitForImageClassificationWithTeacher,
														    LevitModel,
														    LevitPreTrainedModel,
														)
else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

