





'''simple docstring'''






import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A :			List[Any]							   =      logging.get_logger(__name__)





def    lowercase_   (		lowercase__    ) ->List[str]:
						_snake_case: Tuple	       =		ConvNextVaConfig()

						if "atto" in checkpoint_url:
												_snake_case: Any	       =		[2, 2, 6, 2]
												_snake_case: Optional[int]	       =		[40, 80, 160, 320]
						if "femto" in checkpoint_url:
												_snake_case: List[str]	       =		[2, 2, 6, 2]
												_snake_case: Union[str, Any]	       =		[48, 96, 192, 384]
						if "pico" in checkpoint_url:
												_snake_case: Optional[Any]	       =		[2, 2, 6, 2]
												_snake_case: Optional[int]	       =		[64, 128, 256, 512]
						if "nano" in checkpoint_url:
												_snake_case: int	       =		[2, 2, 8, 2]
												_snake_case: Union[str, Any]	       =		[80, 160, 320, 640]
						if "tiny" in checkpoint_url:
												_snake_case: Dict	       =		[3, 3, 9, 3]
												_snake_case: Union[str, Any]	       =		[96, 192, 384, 768]
						if "base" in checkpoint_url:
												_snake_case: Dict	       =		[3, 3, 27, 3]
												_snake_case: Dict	       =		[128, 256, 512, 1024]
						if "large" in checkpoint_url:
												_snake_case: int	       =		[3, 3, 27, 3]
												_snake_case: Any	       =		[192, 384, 768, 1536]
						if "huge" in checkpoint_url:
												_snake_case: List[Any]	       =		[3, 3, 27, 3]
												_snake_case: Tuple	       =		[352, 704, 1408, 2816]

						_snake_case: List[str]	       =		1000
						_snake_case: List[str]	       =		'imagenet-1k-id2label.json'
						_snake_case: List[Any]	       =		(1, 1000)

						_snake_case: List[Any]	       =		'huggingface/label-files'
						_snake_case: Any	       =		num_labels
						_snake_case: Any	       =		json.load(open(hf_hub_download(lowercase__					,	lowercase__					,	repo_type='dataset'    )					,	'r'    )    )
						_snake_case: int	       =		{int(lowercase__    ): v for k, v in idalabel.items()}

						_snake_case: List[Any]	       =		idalabel
						_snake_case: List[str]	       =		{v: k for k, v in idalabel.items()}
						_snake_case: Dict	       =		hidden_sizes
						_snake_case: Optional[Any]	       =		depths

						return config, expected_shape





def    lowercase_   (		lowercase__    ) ->Optional[int]:
						if "downsample_layers.0.0" in name:
												_snake_case: int	       =		name.replace('downsample_layers.0.0'					,	'embeddings.patch_embeddings'    )
						if "downsample_layers.0.1" in name:
												_snake_case: List[str]	       =		name.replace('downsample_layers.0.1'					,	'embeddings.norm'    )  # we rename to layernorm later on
						if "downsample_layers.1.0" in name:
												_snake_case: Optional[Any]	       =		name.replace('downsample_layers.1.0'					,	'stages.1.downsampling_layer.0'    )
						if "downsample_layers.1.1" in name:
												_snake_case: List[Any]	       =		name.replace('downsample_layers.1.1'					,	'stages.1.downsampling_layer.1'    )
						if "downsample_layers.2.0" in name:
												_snake_case: List[str]	       =		name.replace('downsample_layers.2.0'					,	'stages.2.downsampling_layer.0'    )
						if "downsample_layers.2.1" in name:
												_snake_case: int	       =		name.replace('downsample_layers.2.1'					,	'stages.2.downsampling_layer.1'    )
						if "downsample_layers.3.0" in name:
												_snake_case: List[Any]	       =		name.replace('downsample_layers.3.0'					,	'stages.3.downsampling_layer.0'    )
						if "downsample_layers.3.1" in name:
												_snake_case: List[str]	       =		name.replace('downsample_layers.3.1'					,	'stages.3.downsampling_layer.1'    )
						if "stages" in name and "downsampling_layer" not in name:
												# stages.0.0. for instance should be renamed to stages.0.layers.0.
												_snake_case: List[str]	       =		name[: len('stages.0'    )] + '.layers' + name[len('stages.0'    ) :]
						if "gamma" in name:
												_snake_case: List[Any]	       =		name.replace('gamma'					,	'weight'    )
						if "beta" in name:
												_snake_case: Optional[Any]	       =		name.replace('beta'					,	'bias'    )
						if "stages" in name:
												_snake_case: Union[str, Any]	       =		name.replace('stages'					,	'encoder.stages'    )
						if "norm" in name:
												_snake_case: int	       =		name.replace('norm'					,	'layernorm'    )
						if "head" in name:
												_snake_case: str	       =		name.replace('head'					,	'classifier'    )

						return name





def    lowercase_   (		) ->Tuple:
						_snake_case: Dict	       =		'http://images.cocodataset.org/val2017/000000039769.jpg'
						_snake_case: int	       =		Image.open(requests.get(lowercase__					,	stream=lowercase__    ).raw    )
						return im





def    lowercase_   (		lowercase__    ) ->List[str]:
						if "224" in checkpoint_url:
												_snake_case: Optional[int]	       =		224
												_snake_case: Union[str, Any]	       =		224 / 256
						elif "384" in checkpoint_url:
												_snake_case: Tuple	       =		384
												_snake_case: str	       =		None
						else:
												_snake_case: str	       =		512
												_snake_case: Union[str, Any]	       =		None

						return ConvNextImageProcessor(
						    size=lowercase__					,	crop_pct=lowercase__					,	image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]					,	image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]					,	resample=PILImageResampling.BICUBIC					,	)





@torch.no_grad()
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    ) ->Union[str, Any]:
						print('Downloading original model from checkpoint...'    )
						# define ConvNeXTV2 configuration based on URL
						_snake_case       ,    _snake_case: List[Any]	       =		get_convnextva_config(lowercase__    )
						# load original state_dict from URL
						_snake_case: List[str]	       =		torch.hub.load_state_dict_from_url(lowercase__    )['model']

						print('Converting model parameters...'    )
						# rename keys
						for key in state_dict.copy().keys():
												_snake_case: List[str]	       =		state_dict.pop(lowercase__    )
												_snake_case: Dict	       =		val
						# add prefix to all keys expect classifier head
						for key in state_dict.copy().keys():
												_snake_case: List[str]	       =		state_dict.pop(lowercase__    )
												if not key.startswith('classifier'    ):
																		_snake_case: Optional[Any]	       =		'convnextv2.' + key
												_snake_case: Union[str, Any]	       =		val

						# load HuggingFace model
						_snake_case: List[Any]	       =		ConvNextVaForImageClassification(lowercase__    )
						model.load_state_dict(lowercase__    )
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						_snake_case: str	       =		convert_preprocessor(lowercase__    )
						_snake_case: int	       =		preprocessor(images=prepare_img()					,	return_tensors='pt'    )
						_snake_case: Optional[int]	       =		model(**lowercase__    ).logits

						# note: the logits below were obtained without center cropping
						if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
												_snake_case: List[Any]	       =		torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
												_snake_case: Tuple	       =		torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
												_snake_case: Union[str, Any]	       =		torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
												_snake_case: List[Any]	       =		torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
												_snake_case: str	       =		torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
												_snake_case: Any	       =		torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
												_snake_case: Optional[Any]	       =		torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
												_snake_case: List[Any]	       =		torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
												_snake_case: Tuple	       =		torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
												_snake_case: str	       =		torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
												_snake_case: List[Any]	       =		torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
												_snake_case: Optional[int]	       =		torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
												_snake_case: Optional[Any]	       =		torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
												_snake_case: Tuple	       =		torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
												_snake_case: List[Any]	       =		torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
												_snake_case: str	       =		torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
												_snake_case: List[Any]	       =		torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]    )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
												_snake_case: List[Any]	       =		torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]    )
						else:
												raise ValueError(F'''Unknown URL: {checkpoint_url}'''    )

						assert torch.allclose(logits[0, :5]					,	lowercase__					,	atol=1e-3    )
						assert logits.shape == expected_shape
						print('Model outputs match the original results!'    )

						if save_model:
												print('Saving model to local...'    )
												# Create folder to save model
												if not os.path.isdir(lowercase__    ):
																		os.mkdir(lowercase__    )

												model.save_pretrained(lowercase__    )
												preprocessor.save_pretrained(lowercase__    )

						_snake_case: int	       =		'convnextv2'
						if "atto" in checkpoint_url:
												model_name += "-atto"
						if "femto" in checkpoint_url:
												model_name += "-femto"
						if "pico" in checkpoint_url:
												model_name += "-pico"
						if "nano" in checkpoint_url:
												model_name += "-nano"
						elif "tiny" in checkpoint_url:
												model_name += "-tiny"
						elif "base" in checkpoint_url:
												model_name += "-base"
						elif "large" in checkpoint_url:
												model_name += "-large"
						elif "huge" in checkpoint_url:
												model_name += "-huge"
						if "22k" in checkpoint_url and "1k" not in checkpoint_url:
												model_name += "-22k"
						elif "22k" in checkpoint_url and "1k" in checkpoint_url:
												model_name += "-22k-1k"
						elif "1k" in checkpoint_url:
												model_name += "-1k"
						if "224" in checkpoint_url:
												model_name += "-224"
						elif "384" in checkpoint_url:
												model_name += "-384"
						elif "512" in checkpoint_url:
												model_name += "-512"

						if push_to_hub:
												print(F'''Pushing {model_name} to the hub...'''    )
												model.push_to_hub(lowercase__    )
												preprocessor.push_to_hub(lowercase__    )


if __name__ == "__main__":
		A :			List[Any]							   =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_url',
		    default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
		    type=str,
		    help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default='model',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--save_model', action='store_true', help='Save model to local')
		parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')

		A :			Optional[Any]							   =      parser.parse_args()
		convert_convnextva_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
		)












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





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





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: Optional[Any]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=1    ).write()

						_snake_case: Optional[int]	       =		iter_csv_file(lowercase__    )
						_snake_case: List[Any]	       =		iter_csv_file(lowercase__    )

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





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						_snake_case: List[str]	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: List[str]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=2    ).write()

						_snake_case: Optional[Any]	       =		iter_csv_file(lowercase__    )
						_snake_case: Tuple	       =		iter_csv_file(lowercase__    )

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





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Any:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: Any	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: str	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						with pytest.raises(lowercase__    ):
												CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=0    )





