




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
		from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DPTImageProcessor

class 	__lowerCAmelCase			:
							def __init__(		self		:List[Any]	,   __magic_name__		:str	,   __magic_name__		:Optional[Any]=2	,   __magic_name__		:Union[str, Any]=32	,   __magic_name__		:List[Any]=16	,   __magic_name__		:str=3	,   __magic_name__		:Any=True	,   __magic_name__		:Union[str, Any]=True	,   __magic_name__		:List[str]=32	,   __magic_name__		:Optional[int]=4	,   __magic_name__		:Union[str, Any]=[0, 1, 2, 3]	,   __magic_name__		:Optional[Any]=4	,   __magic_name__		:List[Any]=37	,   __magic_name__		:Dict="gelu"	,   __magic_name__		:List[Any]=0.1	,   __magic_name__		:Tuple=0.1	,   __magic_name__		:int=0.02	,   __magic_name__		:Any=3	,   __magic_name__		:Optional[Any]=False	,   __magic_name__		:int=None	,   ):







								'''simple docstring'''







								a		       =							parent
								a		       =							batch_size
								a		       =							image_size
								a		       =							patch_size
								a		       =							num_channels
								a		       =							is_training
								a		       =							use_labels
								a		       =							hidden_size
								a		       =							num_hidden_layers
								a		       =							backbone_out_indices
								a		       =							num_attention_heads
								a		       =							intermediate_size
								a		       =							hidden_act
								a		       =							hidden_dropout_prob
								a		       =							attention_probs_dropout_prob
								a		       =							initializer_range
								a		       =							num_labels
								a		       =							scope
								a		       =							is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								a		       =							(image_size // patch_size) ** 2
								a		       =							num_patches + 1
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

								a		       =							None
								if self.use_labels:
									a		       =							ids_tensor([self.batch_size, self.image_size, self.image_size]	,   self.num_labels       )

								a		       =							self.get_config()

								return config, pixel_values, labels
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								return DPTConfig(
								    image_size=self.image_size	,   patch_size=self.patch_size	,   num_channels=self.num_channels	,   hidden_size=self.hidden_size	,   num_hidden_layers=self.num_hidden_layers	,   backbone_out_indices=self.backbone_out_indices	,   num_attention_heads=self.num_attention_heads	,   intermediate_size=self.intermediate_size	,   hidden_act=self.hidden_act	,   hidden_dropout_prob=self.hidden_dropout_prob	,   attention_probs_dropout_prob=self.attention_probs_dropout_prob	,   is_decoder=__magic_name__	,   initializer_range=self.initializer_range	,   is_hybrid=self.is_hybrid	,   )
							def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:str	,   __magic_name__		:Any	,   __magic_name__		:Dict       ):







								'''simple docstring'''







								a		       =							DPTModel(config=__magic_name__       )
								model.to(__magic_name__       )
								model.eval()
								a		       =							model(__magic_name__       )
								self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)       )
							def    lowerCamelCase__							(		self		:Dict	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:str	,   __magic_name__		:List[Any]       ):







								'''simple docstring'''







								a		       =							self.num_labels
								a		       =							DPTForDepthEstimation(__magic_name__       )
								model.to(__magic_name__       )
								model.eval()
								a		       =							model(__magic_name__       )
								self.parent.assertEqual(result.predicted_depth.shape	,   (self.batch_size, self.image_size, self.image_size)       )
							def    lowerCamelCase__							(		self		:Any	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:List[str]	,   __magic_name__		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							self.num_labels
								a		       =							DPTForSemanticSegmentation(__magic_name__       )
								model.to(__magic_name__       )
								model.eval()
								a		       =							model(__magic_name__	,   labels=__magic_name__       )
								self.parent.assertEqual(
								    result.logits.shape	,   (self.batch_size, self.num_labels, self.image_size, self.image_size)       )





							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								a		       =							self.prepare_config_and_inputs()
								a       ,				a       ,				a		       =							config_and_inputs
								a		       =							{"""pixel_values""": pixel_values}
								return config, inputs_dict







@require_torch
class 	__lowerCAmelCase			(			__magic_name__   , __magic_name__   , unittest.TestCase				):
							UpperCamelCase__        =						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							UpperCamelCase__        =						(
							    {
							        '''depth-estimation''': DPTForDepthEstimation,
							        '''feature-extraction''': DPTModel,
							        '''image-segmentation''': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a		       =							DPTModelTester(self       )
								a		       =							ConfigTester(self	,   config_class=__magic_name__	,   has_text_modality=__magic_name__	,   hidden_size=37       )
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							@unittest.skip(reason="""DPT does not use inputs_embeds"""       )
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								pass
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a       ,				a		       =							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a		       =							model_class(__magic_name__       )
									self.assertIsInstance(model.get_input_embeddings()	,   (nn.Module)       )
									a		       =							model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(__magic_name__	,   nn.Linear       )       )
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a       ,				a		       =							self.model_tester.prepare_config_and_inputs_for_common()

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

									a		       =							["""pixel_values"""]
									self.assertListEqual(arg_names[:1]	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								for model_class in self.all_model_classes:
									if model_class.__name__ == "DPTForDepthEstimation":
										continue

									a       ,				a		       =							self.model_tester.prepare_config_and_inputs_for_common()
									a		       =							True

									if model_class in get_values(__magic_name__       ):
										continue

									a		       =							model_class(__magic_name__       )
									model.to(__magic_name__       )
									model.train()
									a		       =							self._prepare_for_class(__magic_name__	,   __magic_name__	,   return_labels=__magic_name__       )
									a		       =							model(**__magic_name__       ).loss
									loss.backward()
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								for model_class in self.all_model_classes:
									if model_class.__name__ == "DPTForDepthEstimation":
										continue

									a       ,				a		       =							self.model_tester.prepare_config_and_inputs_for_common()
									a		       =							False
									a		       =							True

									if model_class in get_values(__magic_name__       ) or not model_class.supports_gradient_checkpointing:
										continue
									a		       =							model_class(__magic_name__       )
									model.to(__magic_name__       )
									model.gradient_checkpointing_enable()
									model.train()
									a		       =							self._prepare_for_class(__magic_name__	,   __magic_name__	,   return_labels=__magic_name__       )
									a		       =							model(**__magic_name__       ).loss
									loss.backward()
							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a       ,				a		       =							self.model_tester.prepare_config_and_inputs_for_common()

								a		       =							_config_zero_init(__magic_name__       )
								for model_class in self.all_model_classes:
									a		       =							model_class(config=__magic_name__       )
									# Skip the check for the backbone
									a		       =							[]
									for name, module in model.named_modules():
										if module.__class__.__name__ == "DPTViTHybridEmbeddings":
											a		       =							[F'{name}.{key}' for key in module.state_dict().keys()]
											break

									for name, param in model.named_parameters():
										if param.requires_grad:
											if name in backbone_params:
												continue
											self.assertIn(
											    ((param.data.mean() * 1E9).round() / 1E9).item()	,   [0.0, 1.0]	,   msg=F'Parameter {name} of model {model_class} seems not properly initialized'	,   )
							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								pass





							@slow
							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a		       =							DPTModel.from_pretrained(__magic_name__       )
									self.assertIsNotNone(__magic_name__       )







def 	__A	(			)   -> List[str]:
	a		       =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image



@require_torch
@require_vision
@slow
class 	__lowerCAmelCase			(			unittest.TestCase				):
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							DPTImageProcessor.from_pretrained("""Intel/dpt-large"""       )
								a		       =							DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""       ).to(__magic_name__       )

								a		       =							prepare_img()
								a		       =							image_processor(images=__magic_name__	,   return_tensors="""pt"""       ).to(__magic_name__       )

								# forward pass
								with torch.no_grad():
									a		       =							model(**__magic_name__       )
									a		       =							outputs.predicted_depth

								# verify the predicted depth
								a		       =							torch.Size((1, 384, 384)       )
								self.assertEqual(predicted_depth.shape	,   __magic_name__       )

								a		       =							torch.tensor(
								    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]       ).to(__magic_name__       )

								self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,   __magic_name__	,   atol=1E-4       )       )
							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								a		       =							DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""       )
								a		       =							DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""       ).to(__magic_name__       )

								a		       =							prepare_img()
								a		       =							image_processor(images=__magic_name__	,   return_tensors="""pt"""       ).to(__magic_name__       )

								# forward pass
								with torch.no_grad():
									a		       =							model(**__magic_name__       )

								# verify the logits
								a		       =							torch.Size((1, 150, 480, 480)       )
								self.assertEqual(outputs.logits.shape	,   __magic_name__       )

								a		       =							torch.tensor(
								    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]       ).to(__magic_name__       )

								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]	,   __magic_name__	,   atol=1E-4       )       )





							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""       )
								a		       =							DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""       ).to(__magic_name__       )

								a		       =							prepare_img()
								a		       =							image_processor(images=__magic_name__	,   return_tensors="""pt"""       ).to(__magic_name__       )

								# forward pass
								with torch.no_grad():
									a		       =							model(**__magic_name__       )

								a		       =							outputs.logits.detach().cpu()

								a		       =							image_processor.post_process_semantic_segmentation(outputs=__magic_name__	,   target_sizes=[(500, 300)]       )
								a		       =							torch.Size((500, 300)       )
								self.assertEqual(segmentation[0].shape	,   __magic_name__       )

								a		       =							image_processor.post_process_semantic_segmentation(outputs=__magic_name__       )
								a		       =							torch.Size((480, 480)       )
								self.assertEqual(segmentation[0].shape	,   __magic_name__       )







import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

class 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Dict	,   __magic_name__		:Union[str, Any]=False       ):







								'''simple docstring'''







								a       ,				a       ,				a		       =							(
								    filename_skip_take["""filename"""],
								    filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
								    filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
								)
								open(os.path.join(__magic_name__       )	,   """wb"""       ).close()
								a		       =							pa.Table.from_pydict({"""filename""": [Path(__magic_name__       ).name] * 100}       )
								if take == -1:
									a		       =							len(__magic_name__       ) - skip
								if skip is not None and take is not None:
									a		       =							pa_table.slice(__magic_name__	,   __magic_name__       )
								return pa_table







class 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							[train_info, test_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							"""test[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									self.assertEqual(dset["""filename"""][0]	,   F'{name}-test'       )
									self.assertEqual(dset.num_rows	,   33       )
									self.assertEqual(dset.num_columns	,   1       )

									a		       =							["""train""", """test[:33%]"""]
									a		       =							[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
									for instructions in [instructionsa, instructionsa]:
										a		       =							[reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       ) for instr in instructions]
										a       ,				a		       =							(Dataset(**__magic_name__       ) for dataset_kwargs in datasets_kwargs)
										self.assertEqual(train_dset["""filename"""][0]	,   F'{name}-train'       )
										self.assertEqual(train_dset.num_rows	,   100       )
										self.assertEqual(train_dset.num_columns	,   1       )
										self.assertIsInstance(train_dset.split	,   __magic_name__       )
										self.assertEqual(str(train_dset.split       )	,   """train"""       )
										self.assertEqual(test_dset["""filename"""][0]	,   F'{name}-test'       )
										self.assertEqual(test_dset.num_rows	,   33       )
										self.assertEqual(test_dset.num_columns	,   1       )
										self.assertIsInstance(test_dset.split	,   __magic_name__       )
										self.assertEqual(str(test_dset.split       )	,   """test[:33%]"""       )
										del train_dset, test_dset
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=1000	,   shard_lengths=[100] * 10       )
								a		       =							[train_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							"""train[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									self.assertEqual(dset["""filename"""][0]	,   F'{name}-train-00000-of-00010'       )
									self.assertEqual(dset["""filename"""][-1]	,   F'{name}-train-00003-of-00010'       )
									self.assertEqual(dset.num_rows	,   330       )
									self.assertEqual(dset.num_columns	,   1       )





							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							[
									    {"""filename""": os.path.join(__magic_name__	,   """train"""       )},
									    {"""filename""": os.path.join(__magic_name__	,   """test"""       ), """skip""": 10, """take""": 10},
									]
									a		       =							Dataset(**reader.read_files(__magic_name__	,   original_instructions="""train+test[10:20]"""       )       )
									self.assertEqual(dset.num_rows	,   110       )
									self.assertEqual(dset.num_columns	,   1       )
									del dset







@pytest.mark.parametrize("""in_memory"""							,    [False, True]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	a		       =							arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							ArrowReader.read_table(__lowerCamelCase							,    in_memory=__lowerCamelCase					)
	assert table.shape == dataset.data.shape
	assert set(table.column_names					) == set(dataset.data.column_names					)
	assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict





@pytest.mark.parametrize("""in_memory"""							,    [False, True]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							arrow_file
	a		       =							ArrowReader(""""""							,    __lowerCamelCase					)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							reader.read_files([{"""filename""": filename}]							,    in_memory=__lowerCamelCase					)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	a		       =							dataset_kwargs["""arrow_table"""]
	assert table.shape == dataset.data.shape
	assert set(table.column_names					) == set(dataset.data.column_names					)
	assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict





def 	__A	(			)   -> int:
	assert ReadInstruction("""train"""							,    to=10							,    unit="""abs"""					).to_spec() == "train[:10]"
	assert ReadInstruction("""train"""							,    from_=-80							,    to=10							,    unit="""%"""					).to_spec() == "train[-80%:10%]"

	a		       =							"""train+test"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_test

	a		       =							"""train[2:10]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_abs

	a		       =							"""train[15%:-20%]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct

	a		       =							"""train[:10%](closest)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == "train[:10%]"

	a		       =							"""train[:10%](pct1_dropremainder)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct_rounding

	a		       =							"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_test_pct_rounding





def 	__A	(			)   -> Any:
	a		       =							"""dummy"""
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train[:33%]"""
	a		       =							"""arrow"""
	a		       =							"""prefix"""

	a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
	assert isinstance(__lowerCamelCase							,    __lowerCamelCase					)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train.arrow'					), "skip": 0, "take": 33}
	]

	a		       =							[SplitInfo(name="""train"""							,    num_examples=100							,    shard_lengths=[10] * 10					)]
	a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
	assert isinstance(__lowerCamelCase							,    __lowerCamelCase					)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00000-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00001-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00002-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00003-of-00010.arrow'					), "skip": 0, "take": 3},
	]





@pytest.mark.parametrize("""name, expected_exception"""							,    [(None, TypeError), ("""""", ValueError)]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> int:
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train"""
	a		       =							"""arrow"""
	a		       =							"""prefix_path"""
	with pytest.raises(__lowerCamelCase					):
		a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)

