



"""simple docstring"""





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

					if is_vision_available():
										from transformers import MaskaFormerImageProcessor

if is_vision_available():
					from PIL import Image



class    _lowerCAmelCase    :





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=2							,    _lowercase=True							,    _lowercase=False							,    _lowercase=1_0							,    _lowercase=3							,    _lowercase=3_2 * 8							,    _lowercase=3_2 * 8							,    _lowercase=4							,    _lowercase=6_4							,    ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : int						=			parent
								snake_case_   : Optional[int]						=			batch_size
								snake_case_   : List[str]						=			is_training
								snake_case_   : int						=			use_auxiliary_loss
								snake_case_   : List[str]						=			num_queries
								snake_case_   : Tuple						=			num_channels
								snake_case_   : Any						=			min_size
								snake_case_   : List[Any]						=			max_size
								snake_case_   : List[str]						=			num_labels
								snake_case_   : Optional[Any]						=			hidden_dim
								snake_case_   : Any						=			hidden_dim





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Any						=			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
								    _lowercase   )

								snake_case_   : Union[str, Any]						=			torch.ones([self.batch_size, self.min_size, self.max_size]							,    device=_lowercase   )

								snake_case_   : int						=			(
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]							,    device=_lowercase   ) > 0.5
								).float()
								snake_case_   : Any						=			(torch.rand((self.batch_size, self.num_labels)							,    device=_lowercase   ) > 0.5).long()

								snake_case_   : Optional[int]						=			self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			MaskaFormerConfig(
								    hidden_size=self.hidden_dim							,    )
								snake_case_   : List[Any]						=			self.num_queries
								snake_case_   : Union[str, Any]						=			self.num_labels

								snake_case_   : Union[str, Any]						=			[1, 1, 1, 1]
								snake_case_   : str						=			self.num_channels

								snake_case_   : Any						=			6_4
								snake_case_   : int						=			1_2_8
								snake_case_   : Optional[Any]						=			self.hidden_dim
								snake_case_   : Any						=			self.hidden_dim
								snake_case_   : str						=			self.hidden_dim
								return config





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : Tuple						=			self.prepare_config_and_inputs()
								snake_case_   : Any						=			{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
								return config, inputs_dict





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			output.encoder_hidden_states
								snake_case_   : Dict						=			output.pixel_decoder_hidden_states
								snake_case_   : List[str]						=			output.transformer_decoder_hidden_states

								self.parent.assertTrue(len(_lowercase   )							,    len(config.backbone_config.depths   )   )
								self.parent.assertTrue(len(_lowercase   )							,    len(config.backbone_config.depths   )   )
								self.parent.assertTrue(len(_lowercase   )							,    config.decoder_layers   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase=False   ) ->						int:







								'''simple docstring'''

								with torch.no_grad():
														snake_case_   : Dict						=			MaskaFormerModel(config=_lowercase   )
														model.to(_lowercase   )
														model.eval()

														snake_case_   : Optional[int]						=			model(pixel_values=_lowercase							,    pixel_mask=_lowercase   )
														snake_case_   : Optional[Any]						=			model(_lowercase							,    output_hidden_states=_lowercase   )

								self.parent.assertEqual(
								    output.transformer_decoder_last_hidden_state.shape							,    (self.batch_size, self.num_queries, self.hidden_dim)							,    )
								# let's ensure the other two hidden state exists
								self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None   )
								self.parent.assertTrue(output.encoder_last_hidden_state is not None   )

								if output_hidden_states:
														self.check_output_hidden_state(_lowercase							,    _lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						str:







								'''simple docstring'''

								snake_case_   : List[str]						=			MaskaFormerForUniversalSegmentation(config=_lowercase   )
								model.to(_lowercase   )
								model.eval()

								def comm_check_on_output(_lowercase   ):
														# let's still check that all the required stuff is there
														self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None   )
														self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None   )
														self.parent.assertTrue(result.encoder_last_hidden_state is not None   )
														# okay, now we need to check the logits shape
														# due to the encoder compression, masks have a //4 spatial size
														self.parent.assertEqual(
														    result.masks_queries_logits.shape							,    (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)							,    )
														# + 1 for null class
														self.parent.assertEqual(
														    result.class_queries_logits.shape							,    (self.batch_size, self.num_queries, self.num_labels + 1)   )

								with torch.no_grad():
														snake_case_   : List[str]						=			model(pixel_values=_lowercase							,    pixel_mask=_lowercase   )
														snake_case_   : List[Any]						=			model(_lowercase   )

														comm_check_on_output(_lowercase   )

														snake_case_   : Optional[Any]						=			model(
														    pixel_values=_lowercase							,    pixel_mask=_lowercase							,    mask_labels=_lowercase							,    class_labels=_lowercase   )

								comm_check_on_output(_lowercase   )

								self.parent.assertTrue(result.loss is not None   )
								self.parent.assertEqual(result.loss.shape							,    torch.Size([1]   )   )




@require_torch
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
		_lowerCamelCase			  =       {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			MaskaFormerModelTester(self   )
								snake_case_   : List[Any]						=			ConfigTester(self							,    config_class=_lowercase							,    has_text_modality=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								self.config_tester.run_common_tests()





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_			,		snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(_lowercase							,    **_lowercase							,    output_hidden_states=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Any						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowercase   )





		@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""   )
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								pass





		@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""   )
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								pass





		@unittest.skip(reason="""Mask2Former is not a generative model"""   )
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								pass





		@unittest.skip(reason="""Mask2Former does not use token embeddings"""   )
		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								pass





		@require_torch_multi_gpu
		@unittest.skip(
		    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""   )
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								pass





		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								pass





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_			,		snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														snake_case_   : Any						=			model_class(_lowercase   )
														snake_case_   : Any						=			inspect.signature(model.forward   )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														snake_case_   : str						=			[*signature.parameters.keys()]

														snake_case_   : Tuple						=			["""pixel_values"""]
														self.assertListEqual(arg_names[:1]							,    _lowercase   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
														snake_case_   : Tuple						=			MaskaFormerModel.from_pretrained(_lowercase   )
														self.assertIsNotNone(_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			(self.model_tester.min_size,) * 2
								snake_case_   : Union[str, Any]						=			{
								    """pixel_values""": torch.randn((2, 3, *size)							,    device=_lowercase   ),
								    """mask_labels""": torch.randn((2, 1_0, *size)							,    device=_lowercase   ),
								    """class_labels""": torch.zeros(2							,    1_0							,    device=_lowercase   ).long(),
								}
								snake_case_   : int						=			self.model_tester.get_config()

								snake_case_   : int						=			MaskaFormerForUniversalSegmentation(_lowercase   ).to(_lowercase   )
								snake_case_   : List[str]						=			model(**_lowercase   )
								self.assertTrue(outputs.loss is not None   )





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_			,		snake_case_   : Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(_lowercase							,    **_lowercase							,    output_hidden_states=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_			,		snake_case_   : int						=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														snake_case_   : str						=			model_class(_lowercase   ).to(_lowercase   )
														snake_case_   : Optional[int]						=			model(**_lowercase							,    output_attentions=_lowercase   )
														self.assertTrue(outputs.attentions is not None   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								if not self.model_tester.is_training:
														return

								snake_case_   : int						=			self.all_model_classes[1]
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs()

								snake_case_   : Optional[Any]						=			model_class(_lowercase   )
								model.to(_lowercase   )
								model.train()

								snake_case_   : Optional[Any]						=			model(_lowercase							,    mask_labels=_lowercase							,    class_labels=_lowercase   ).loss
								loss.backward()





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : str						=			self.all_model_classes[1]
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs()
								snake_case_   : str						=			True
								snake_case_   : List[str]						=			True

								snake_case_   : str						=			model_class(_lowercase   ).to(_lowercase   )
								model.train()

								snake_case_   : Dict						=			model(_lowercase							,    mask_labels=_lowercase							,    class_labels=_lowercase   )

								snake_case_   : Optional[Any]						=			outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								snake_case_   : int						=			outputs.pixel_decoder_hidden_states[0]
								pixel_decoder_hidden_states.retain_grad()

								snake_case_   : Optional[int]						=			outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								snake_case_   : Optional[int]						=			outputs.attentions[0]
								attentions.retain_grad()

								outputs.loss.backward(retain_graph=_lowercase   )

								self.assertIsNotNone(encoder_hidden_states.grad   )
								self.assertIsNotNone(pixel_decoder_hidden_states.grad   )
								self.assertIsNotNone(transformer_decoder_hidden_states.grad   )
								self.assertIsNotNone(attentions.grad   )




__lowerCAmelCase       :					List[str]      					=					1e-4
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : str						=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
						return image






@require_vision
@slow
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@cached_property
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								return "facebook/mask2former-swin-small-coco-instance"





		@cached_property
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints   ) if is_vision_available() else None





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(_lowercase   )
								snake_case_   : List[str]						=			self.default_image_processor
								snake_case_   : List[str]						=			prepare_img()
								snake_case_   : Any						=			image_processor(_lowercase							,    return_tensors="""pt"""   ).to(_lowercase   )
								snake_case_   : List[Any]						=			inputs["""pixel_values"""].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0   )
								# check size
								self.assertEqual(_lowercase							,    (1, 3, 3_8_4, 3_8_4)   )

								with torch.no_grad():
														snake_case_   : Union[str, Any]						=			model(**_lowercase   )

								snake_case_   : Optional[int]						=			torch.tensor(
								    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]   ).to(_lowercase   )
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )

								snake_case_   : List[str]						=			torch.tensor(
								    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]   ).to(_lowercase   )
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )

								snake_case_   : List[str]						=			torch.tensor(
								    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]   ).to(_lowercase   )
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(_lowercase   ).eval()
								snake_case_   : str						=			self.default_image_processor
								snake_case_   : Any						=			prepare_img()
								snake_case_   : str						=			image_processor(_lowercase							,    return_tensors="""pt"""   ).to(_lowercase   )
								snake_case_   : int						=			inputs["""pixel_values"""].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0   )
								# check size
								self.assertEqual(_lowercase							,    (1, 3, 3_8_4, 3_8_4)   )

								with torch.no_grad():
														snake_case_   : List[str]						=			model(**_lowercase   )
								# masks_queries_logits
								snake_case_   : Tuple						=			outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape							,    (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
								snake_case_   : Union[str, Any]						=			[
								    [-8.7839, -9.0056, -8.8121],
								    [-7.4104, -7.0313, -6.5401],
								    [-6.6105, -6.3427, -6.4675],
								]
								snake_case_   : int						=			torch.tensor(_lowercase   ).to(_lowercase   )
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )
								# class_queries_logits
								snake_case_   : Dict						=			outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape							,    (1, model.config.num_queries, model.config.num_labels + 1)   )
								snake_case_   : int						=			torch.tensor(
								    [
								        [1.8324, -8.0835, -4.1922],
								        [0.8450, -9.0050, -3.6053],
								        [0.3045, -7.7293, -3.0275],
								    ]   ).to(_lowercase   )
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(_lowercase   ).eval()
								snake_case_   : Union[str, Any]						=			self.default_image_processor

								snake_case_   : List[Any]						=			image_processor(
								    [np.zeros((3, 8_0_0, 1_3_3_3)   ), np.zeros((3, 8_0_0, 1_3_3_3)   )]							,    segmentation_maps=[np.zeros((3_8_4, 3_8_4)   ).astype(np.floataa   ), np.zeros((3_8_4, 3_8_4)   ).astype(np.floataa   )]							,    return_tensors="""pt"""							,    )

								snake_case_   : List[Any]						=			inputs["""pixel_values"""].to(_lowercase   )
								snake_case_   : Optional[int]						=			[el.to(_lowercase   ) for el in inputs["""mask_labels"""]]
								snake_case_   : Any						=			[el.to(_lowercase   ) for el in inputs["""class_labels"""]]

								with torch.no_grad():
														snake_case_   : Optional[Any]						=			model(**_lowercase   )

								self.assertTrue(outputs.loss is not None   )






"""simple docstring"""





import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

import pyarrow as pa
import pyarrow.parquet as pq
import pytest

import datasets
import datasets.config
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : int						=			1_0
						snake_case_   : Any						=			datasets.Features(
						    {
						        """tokens""": datasets.Sequence(datasets.Value("""string"""    )    ),
						        """labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""]    )    ),
						        """answers""": datasets.Sequence(
						            {
						                """text""": datasets.Value("""string"""    ),
						                """answer_start""": datasets.Value("""int32"""    ),
						            }    ),
						        """id""": datasets.Value("""int64"""    ),
						    }    )
						snake_case_   : Tuple						=			datasets.Dataset.from_dict(
						    {
						        """tokens""": [["""foo"""] * 5] * n,
						        """labels""": [[1] * 5] * n,
						        """answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
						        """id""": list(range(__UpperCamelCase    )    ),
						    }					, features=__UpperCamelCase					, )
						return dataset
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						snake_case_   : str						=			str(tmp_path_factory.mktemp("""data"""    ) / """file.arrow"""    )
						dataset.map(cache_file_name=__UpperCamelCase    )
						return filename


# FILE_CONTENT + files


__lowerCAmelCase       :					List[Any]      					=					'''\
    Text data.
    Second line of data.'''
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tmp_path_factory.mktemp("""data"""    ) / """file.txt"""
						snake_case_   : Optional[Any]						=			FILE_CONTENT
						with open(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase    )
						return filename
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						import bza

						snake_case_   : Dict						=			tmp_path_factory.mktemp("""data"""    ) / """file.txt.bz2"""
						snake_case_   : Any						=			bytes(__UpperCamelCase					, """utf-8"""    )
						with bza.open(__UpperCamelCase					, """wb"""    ) as f:
												f.write(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						import gzip

						snake_case_   : Tuple						=			str(tmp_path_factory.mktemp("""data"""    ) / """file.txt.gz"""    )
						snake_case_   : List[Any]						=			bytes(__UpperCamelCase					, """utf-8"""    )
						with gzip.open(__UpperCamelCase					, """wb"""    ) as f:
												f.write(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						if datasets.config.LZ4_AVAILABLE:
												import lza.frame

												snake_case_   : Any						=			tmp_path_factory.mktemp("""data"""    ) / """file.txt.lz4"""
												snake_case_   : Optional[Any]						=			bytes(__UpperCamelCase					, """utf-8"""    )
												with lza.frame.open(__UpperCamelCase					, """wb"""    ) as f:
																		f.write(__UpperCamelCase    )
												return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						if datasets.config.PY7ZR_AVAILABLE:
												import pyazr

												snake_case_   : Any						=			tmp_path_factory.mktemp("""data"""    ) / """file.txt.7z"""
												with pyazr.SevenZipFile(__UpperCamelCase					, """w"""    ) as archive:
																		archive.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
												return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						import tarfile

						snake_case_   : Tuple						=			tmp_path_factory.mktemp("""data"""    ) / """file.txt.tar"""
						with tarfile.TarFile(__UpperCamelCase					, """w"""    ) as f:
												f.add(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						import lzma

						snake_case_   : str						=			tmp_path_factory.mktemp("""data"""    ) / """file.txt.xz"""
						snake_case_   : str						=			bytes(__UpperCamelCase					, """utf-8"""    )
						with lzma.open(__UpperCamelCase					, """wb"""    ) as f:
												f.write(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						import zipfile

						snake_case_   : Optional[int]						=			tmp_path_factory.mktemp("""data"""    ) / """file.txt.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						if datasets.config.ZSTANDARD_AVAILABLE:
												import zstandard as zstd

												snake_case_   : Any						=			tmp_path_factory.mktemp("""data"""    ) / """file.txt.zst"""
												snake_case_   : Tuple						=			bytes(__UpperCamelCase					, """utf-8"""    )
												with zstd.open(__UpperCamelCase					, """wb"""    ) as f:
																		f.write(__UpperCamelCase    )
												return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			tmp_path_factory.mktemp("""data"""    ) / """file.xml"""
						snake_case_   : List[str]						=			textwrap.dedent(
						    """\
    <?xml version=\"1.0\" encoding=\"UTF-8\" ?>
    <tmx version=\"1.4\">
      <header segtype=\"sentence\" srclang=\"ca\" />
      <body>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
        </tu>
      </body>
    </tmx>"""    )
						with open(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase    )
						return filename


__lowerCAmelCase       :					List[str]      					=					[
    {'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
    {'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
    {'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
    {'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__lowerCAmelCase       :					Tuple      					=					[
    {'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
    {'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__lowerCAmelCase       :					int      					=					{
    '''col_1''': ['''0''', '''1''', '''2''', '''3'''],
    '''col_2''': [0, 1, 2, 3],
    '''col_3''': [0.0, 1.0, 2.0, 3.0],
}

__lowerCAmelCase       :					int      					=					[
    {'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
    {'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]

__lowerCAmelCase       :					Any      					=					[
    {'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
    {'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
    {'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
    {'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[int]						=			datasets.Dataset.from_dict(__UpperCamelCase    )
						snake_case_   : List[str]						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.arrow"""    )
						dataset.map(cache_file_name=__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : str						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.sqlite"""    )
						with contextlib.closing(sqlitea.connect(__UpperCamelCase    )    ) as con:
												snake_case_   : Tuple						=			con.cursor()
												cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)"""    )
												for item in DATA:
																		cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)"""					, tuple(item.values()    )    )
												con.commit()
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.csv"""    )
						with open(__UpperCamelCase					, """w"""					, newline=""""""    ) as f:
												snake_case_   : Optional[Any]						=			csv.DictWriter(__UpperCamelCase					, fieldnames=["""col_1""", """col_2""", """col_3"""]    )
												writer.writeheader()
												for item in DATA:
																		writer.writerow(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset2.csv"""    )
						with open(__UpperCamelCase					, """w"""					, newline=""""""    ) as f:
												snake_case_   : str						=			csv.DictWriter(__UpperCamelCase					, fieldnames=["""col_1""", """col_2""", """col_3"""]    )
												writer.writeheader()
												for item in DATA:
																		writer.writerow(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						import bza

						snake_case_   : Tuple						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.csv.bz2"""
						with open(__UpperCamelCase					, """rb"""    ) as f:
												snake_case_   : int						=			f.read()
						# data = bytes(FILE_CONTENT, "utf-8")
						with bza.open(__UpperCamelCase					, """wb"""    ) as f:
												f.write(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			int					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : int						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.csv.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Any						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.csv.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.basename(csv_path.replace(""".csv"""					, """.CSV"""    )    )    )
												f.write(__UpperCamelCase					, arcname=os.path.basename(csva_path.replace(""".csv"""					, """.CSV"""    )    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						snake_case_   : int						=			tmp_path_factory.mktemp("""data"""    ) / """dataset_with_dir.csv.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.join("""main_dir"""					, os.path.basename(__UpperCamelCase    )    )    )
												f.write(__UpperCamelCase					, arcname=os.path.join("""main_dir"""					, os.path.basename(__UpperCamelCase    )    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : Any						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.parquet"""    )
						snake_case_   : Any						=			pa.schema(
						    {
						        """col_1""": pa.string(),
						        """col_2""": pa.intaa(),
						        """col_3""": pa.floataa(),
						    }    )
						with open(__UpperCamelCase					, """wb"""    ) as f:
												snake_case_   : Optional[int]						=			pq.ParquetWriter(__UpperCamelCase					, schema=__UpperCamelCase    )
												snake_case_   : Optional[int]						=			pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__UpperCamelCase    )    )] for k in DATA[0]}					, schema=__UpperCamelCase    )
												writer.write_table(__UpperCamelCase    )
												writer.close()
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.json"""    )
						snake_case_   : Any						=			{"""data""": DATA}
						with open(__UpperCamelCase					, """w"""    ) as f:
												json.dump(__UpperCamelCase					, __UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : Tuple						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.json"""    )
						snake_case_   : List[Any]						=			{"""data""": DATA_DICT_OF_LISTS}
						with open(__UpperCamelCase					, """w"""    ) as f:
												json.dump(__UpperCamelCase					, __UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.jsonl"""    )
						with open(__UpperCamelCase					, """w"""    ) as f:
												for item in DATA:
																		f.write(json.dumps(__UpperCamelCase    ) + """\n"""    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset2.jsonl"""    )
						with open(__UpperCamelCase					, """w"""    ) as f:
												for item in DATA:
																		f.write(json.dumps(__UpperCamelCase    ) + """\n"""    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_   : str						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset_312.jsonl"""    )
						with open(__UpperCamelCase					, """w"""    ) as f:
												for item in DATA_312:
																		f.write(json.dumps(__UpperCamelCase    ) + """\n"""    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset-str.jsonl"""    )
						with open(__UpperCamelCase					, """w"""    ) as f:
												for item in DATA_STR:
																		f.write(json.dumps(__UpperCamelCase    ) + """\n"""    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						import gzip

						snake_case_   : List[str]						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.txt.gz"""    )
						with open(__UpperCamelCase					, """rb"""    ) as orig_file:
												with gzip.open(__UpperCamelCase					, """wb"""    ) as zipped_file:
																		zipped_file.writelines(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						import gzip

						snake_case_   : Dict						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.jsonl.gz"""    )
						with open(__UpperCamelCase					, """rb"""    ) as orig_file:
												with gzip.open(__UpperCamelCase					, """wb"""    ) as zipped_file:
																		zipped_file.writelines(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			Dict					, __UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.jsonl.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : int						=			tmp_path_factory.mktemp("""data"""    ) / """dataset_nested.jsonl.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.join("""nested"""					, os.path.basename(__UpperCamelCase    )    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tmp_path_factory.mktemp("""data"""    ) / """dataset_with_dir.jsonl.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.join("""main_dir"""					, os.path.basename(__UpperCamelCase    )    )    )
												f.write(__UpperCamelCase					, arcname=os.path.join("""main_dir"""					, os.path.basename(__UpperCamelCase    )    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : Tuple						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.jsonl.tar"""
						with tarfile.TarFile(__UpperCamelCase					, """w"""    ) as f:
												f.add(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
												f.add(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tmp_path_factory.mktemp("""data"""    ) / """dataset_nested.jsonl.tar"""
						with tarfile.TarFile(__UpperCamelCase					, """w"""    ) as f:
												f.add(__UpperCamelCase					, arcname=os.path.join("""nested"""					, os.path.basename(__UpperCamelCase    )    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : str						=			["""0""", """1""", """2""", """3"""]
						snake_case_   : Dict						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset.txt"""    )
						with open(__UpperCamelCase					, """w"""    ) as f:
												for item in data:
																		f.write(item + """\n"""    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						snake_case_   : int						=			["""0""", """1""", """2""", """3"""]
						snake_case_   : int						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset2.txt"""    )
						with open(__UpperCamelCase					, """w"""    ) as f:
												for item in data:
																		f.write(item + """\n"""    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			["""0""", """1""", """2""", """3"""]
						snake_case_   : str						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.abc"""
						with open(__UpperCamelCase					, """w"""    ) as f:
												for item in data:
																		f.write(item + """\n"""    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.text.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			tmp_path_factory.mktemp("""data"""    ) / """dataset_with_dir.text.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.join("""main_dir"""					, os.path.basename(__UpperCamelCase    )    )    )
												f.write(__UpperCamelCase					, arcname=os.path.join("""main_dir"""					, os.path.basename(__UpperCamelCase    )    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.ext.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.basename("""unsupported.ext"""    )    )
												f.write(__UpperCamelCase					, arcname=os.path.basename("""unsupported_2.ext"""    )    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			"""\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""]    )
						snake_case_   : List[str]						=			str(tmp_path_factory.mktemp("""data"""    ) / """dataset_with_unicode_new_lines.txt"""    )
						with open(__UpperCamelCase					, """w"""					, encoding="""utf-8"""    ) as f:
												f.write(__UpperCamelCase    )
						return path
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						return os.path.join("""tests"""					, """features"""					, """data"""					, """test_image_rgb.jpg"""    )
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						return os.path.join("""tests"""					, """features"""					, """data"""					, """test_audio_44100.wav"""    )
@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Any						=			tmp_path_factory.mktemp("""data"""    ) / """dataset.img.zip"""
						with zipfile.ZipFile(__UpperCamelCase					, """w"""    ) as f:
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    )    )
												f.write(__UpperCamelCase					, arcname=os.path.basename(__UpperCamelCase    ).replace(""".jpg"""					, """2.jpg"""    )    )
						return path






@pytest.fixture(scope="""session"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_   : str						=			tmp_path_factory.mktemp("""data_dir"""    )

						(data_dir / "subdir").mkdir()
						with open(data_dir / """subdir""" / """train.txt"""					, """w"""    ) as f:
												f.write("""foo\n""" * 1_0    )
						with open(data_dir / """subdir""" / """test.txt"""					, """w"""    ) as f:
												f.write("""bar\n""" * 1_0    )
						# hidden file
						with open(data_dir / """subdir""" / """.test.txt"""					, """w"""    ) as f:
												f.write("""bar\n""" * 1_0    )

						# hidden directory
						(data_dir / ".subdir").mkdir()
						with open(data_dir / """.subdir""" / """train.txt"""					, """w"""    ) as f:
												f.write("""foo\n""" * 1_0    )
						with open(data_dir / """.subdir""" / """test.txt"""					, """w"""    ) as f:
												f.write("""bar\n""" * 1_0    )

						return data_dir

