

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor







class  UpperCamelCase_      (	unittest.TestCase      ):


  """simple docstring"""



  def __init__(						self     :					Optional[Any]  ,	A_     :					Optional[Any]  ,	A_     :					List[str]=7  ,	A_     :					str=3  ,	A_     :					Tuple=18  ,	A_     :					Union[str, Any]=30  ,	A_     :					Optional[Any]=400  ,	A_     :					Tuple=True  ,	A_     :					Optional[int]=None  ,	A_     :					List[str]=True  ,	A_     :					Dict=None  ,	A_     :					Dict=True  ,	A_     :					str=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,	A_     :					List[str]=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,	A_     :					List[Any]=True  ,	)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    size if size is not None else {"""shortest_edge""": 20}
      SCREAMING_SNAKE_CASE__         =    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
      SCREAMING_SNAKE_CASE__         =    parent
      SCREAMING_SNAKE_CASE__         =    batch_size
      SCREAMING_SNAKE_CASE__         =    num_channels
      SCREAMING_SNAKE_CASE__         =    image_size
      SCREAMING_SNAKE_CASE__         =    min_resolution
      SCREAMING_SNAKE_CASE__         =    max_resolution
      SCREAMING_SNAKE_CASE__         =    do_resize
      SCREAMING_SNAKE_CASE__         =    size
      SCREAMING_SNAKE_CASE__         =    do_center_crop
      SCREAMING_SNAKE_CASE__         =    crop_size
      SCREAMING_SNAKE_CASE__         =    do_normalize
      SCREAMING_SNAKE_CASE__         =    image_mean
      SCREAMING_SNAKE_CASE__         =    image_std
      SCREAMING_SNAKE_CASE__         =    do_convert_rgb



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Optional[Any]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }







  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					str=False  ,	A_     :					Optional[Any]=False  ,	A_     :					str=False						)       ->			Union[str, Any]:
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
          SCREAMING_SNAKE_CASE__         =    []
          for i in range(self.batch_size						):
              image_inputs.append(
                  np.random.randint(
                      255  ,	size=(self.num_channels, self.max_resolution, self.max_resolution)  ,	dtype=np.uinta						)						)
      else:
          SCREAMING_SNAKE_CASE__         =    []
          for i in range(self.batch_size						):
              SCREAMING_SNAKE_CASE__         =    np.random.choice(np.arange(self.min_resolution  ,	self.max_resolution						)  ,	2						)
              image_inputs.append(np.random.randint(255  ,	size=(self.num_channels, width, height)  ,	dtype=np.uinta						)						)

      if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          SCREAMING_SNAKE_CASE__         =    [Image.fromarray(np.moveaxis(lowercase_  ,	0  ,	-1						)						) for x in image_inputs]

      if torchify:
          SCREAMING_SNAKE_CASE__         =    [torch.from_numpy(lowercase_						) for x in image_inputs]

      return image_inputs







@require_torch
@require_vision
class  UpperCamelCase_      (	_UpperCAmelCase		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Any					    =   CLIPImageProcessor if is_vision_available() else None



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    CLIPImageProcessingTester(self						)



  @property
  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			int:
      return self.image_processor_tester.prepare_image_processor_dict()



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(lowercase_  ,	"do_resize"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"size"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"do_center_crop"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"center_crop"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"do_normalize"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"image_mean"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"image_std"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"do_convert_rgb"						)						)



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 20}						)
      self.assertEqual(image_processor.crop_size  ,	{"height": 18, "width": 18}						)

      SCREAMING_SNAKE_CASE__         =    self.image_processing_class.from_dict(self.image_processor_dict  ,	size=42  ,	crop_size=84						)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 42}						)
      self.assertEqual(image_processor.crop_size  ,	{"height": 84, "width": 84}						)



  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			Tuple:
      pass



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Optional[Any]:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      SCREAMING_SNAKE_CASE__         =    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_						)
      for image in image_inputs:
          self.assertIsInstance(lowercase_  ,	Image.Image						)

      # Test not batched input
      SCREAMING_SNAKE_CASE__         =    image_processing(image_inputs[0]  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      SCREAMING_SNAKE_CASE__         =    image_processing(lowercase_  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Optional[int]:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE__         =    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_  ,	numpify=lowercase_						)
      for image in image_inputs:
          self.assertIsInstance(lowercase_  ,	np.ndarray						)

      # Test not batched input
      SCREAMING_SNAKE_CASE__         =    image_processing(image_inputs[0]  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      SCREAMING_SNAKE_CASE__         =    image_processing(lowercase_  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)







  def   _UpperCamelCase     (						self     :					int						)       ->			Dict:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE__         =    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_  ,	torchify=lowercase_						)
      for image in image_inputs:
          self.assertIsInstance(lowercase_  ,	torch.Tensor						)

      # Test not batched input
      SCREAMING_SNAKE_CASE__         =    image_processing(image_inputs[0]  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      SCREAMING_SNAKE_CASE__         =    image_processing(lowercase_  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)







@require_torch
@require_vision
class  UpperCamelCase_      (	_UpperCAmelCase		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   CLIPImageProcessor if is_vision_available() else None



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    CLIPImageProcessingTester(self  ,	num_channels=4						)
      SCREAMING_SNAKE_CASE__         =    3



  @property
  def   _UpperCamelCase     (						self     :					str						)       ->			Any:
      return self.image_processor_tester.prepare_image_processor_dict()



  def   _UpperCamelCase     (						self     :					str						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(lowercase_  ,	"do_resize"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"size"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"do_center_crop"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"center_crop"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"do_normalize"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"image_mean"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"image_std"						)						)
      self.assertTrue(hasattr(lowercase_  ,	"do_convert_rgb"						)						)



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Union[str, Any]:
      pass







  def   _UpperCamelCase     (						self     :					str						)       ->			str:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      SCREAMING_SNAKE_CASE__         =    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_						)
      for image in image_inputs:
          self.assertIsInstance(lowercase_  ,	Image.Image						)

      # Test not batched input
      SCREAMING_SNAKE_CASE__         =    image_processing(image_inputs[0]  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      SCREAMING_SNAKE_CASE__         =    image_processing(lowercase_  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)



'''simple docstring'''




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        __magic_name__		(       _UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Dict							,     lowercase_							:    List[str]=False       ):
										lowercase_		,		lowercase_		,		lowercase_   :     str									=			(
										    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(lowercase_       )							,     """wb"""       ).close()
										lowercase_   :     Union[str, Any]									=			pa.Table.from_pydict({"""filename""": [Path(lowercase_       ).name] * 100}       )
										if take == -1:
																	lowercase_   :     Optional[int]									=			len(lowercase_       ) - skip
										if skip is not None and take is not None:
																	lowercase_   :     Any									=			pa_table.slice(lowercase_							,     lowercase_       )
										return pa_table





class        __magic_name__		(       _UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     Union[str, Any]									=			"""my_name"""
										lowercase_   :     Optional[Any]									=			SplitInfo(name="""train"""							,     num_examples=100       )
										lowercase_   :     Dict									=			SplitInfo(name="""test"""							,     num_examples=100       )
										lowercase_   :     str									=			[train_info, test_info]
										lowercase_   :     Any									=			SplitDict()
										split_dict.add(lowercase_       )
										split_dict.add(lowercase_       )
										lowercase_   :     Union[str, Any]									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Tuple									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     Optional[Any]									=			"""test[:33%]"""
																	lowercase_   :     List[Any]									=			Dataset(**reader.read(lowercase_							,     lowercase_							,     lowercase_       )       )
																	self.assertEqual(dset["""filename"""][0]							,     f'''{name}-test'''       )
																	self.assertEqual(dset.num_rows							,     33       )
																	self.assertEqual(dset.num_columns							,     1       )

																	lowercase_   :     str									=			["""train""", """test[:33%]"""]
																	lowercase_   :     Any									=			[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
																	for instructions in [instructionsa, instructionsa]:
																								lowercase_   :     Dict									=			[reader.read(lowercase_							,     lowercase_							,     lowercase_       ) for instr in instructions]
																								lowercase_		,		lowercase_   :     List[Any]									=			(Dataset(**lowercase_       ) 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							,     lowercase_       )
																								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							,     lowercase_       )
																								self.assertEqual(str(test_dset.split       )							,     """test[:33%]"""       )
																								del train_dset, test_dset







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     List[Any]									=			"""my_name"""
										lowercase_   :     int									=			SplitInfo(name="""train"""							,     num_examples=1000							,     shard_lengths=[100] * 10       )
										lowercase_   :     Tuple									=			[train_info]
										lowercase_   :     Any									=			SplitDict()
										split_dict.add(lowercase_       )
										lowercase_   :     Union[str, Any]									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Optional[int]									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     Optional[int]									=			"""train[:33%]"""
																	lowercase_   :     int									=			Dataset(**reader.read(lowercase_							,     lowercase_							,     lowercase_       )       )
																	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       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Any									=			SplitInfo(name="""train"""							,     num_examples=100       )
										lowercase_   :     Optional[int]									=			SplitInfo(name="""test"""							,     num_examples=100       )
										lowercase_   :     Union[str, Any]									=			SplitDict()
										split_dict.add(lowercase_       )
										split_dict.add(lowercase_       )
										lowercase_   :     Tuple									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Tuple									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     List[str]									=			[
																	    {"""filename""": os.path.join(lowercase_							,     """train"""       )},
																	    {"""filename""": os.path.join(lowercase_							,     """test"""       ), """skip""": 10, """take""": 10},
																	]
																	lowercase_   :     int									=			Dataset(**reader.read_files(lowercase_							,     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 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Union[str, Any]					)     -> Union[str, Any]:
							lowercase_   :     str									=			arrow_file
							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_   :     Optional[Any]									=			ArrowReader.read_table(UpperCAmelCase__ , in_memory=UpperCAmelCase__					)
							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 				lowerCamelCase      ( UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Optional[int]					)     -> Any:
							lowercase_   :     Tuple									=			arrow_file
							lowercase_   :     Optional[Any]									=			ArrowReader("""""" , UpperCAmelCase__					)
							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_   :     Dict									=			reader.read_files([{"""filename""": filename}] , in_memory=UpperCAmelCase__					)
							assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
							lowercase_   :     Optional[int]									=			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 				lowerCamelCase      ( )     -> Optional[Any]:
							assert ReadInstruction("""train""" , to=10 , unit="""abs"""					).to_spec() == "train[:10]"
							assert ReadInstruction("""train""" , from_=-80 , to=10 , unit="""%"""					).to_spec() == "train[-80%:10%]"

							lowercase_   :     Tuple									=			"""train+test"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_test

							lowercase_   :     List[Any]									=			"""train[2:10]"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_abs

							lowercase_   :     Any									=			"""train[15%:-20%]"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_pct

							lowercase_   :     Optional[int]									=			"""train[:10%](closest)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == "train[:10%]"

							lowercase_   :     List[Any]									=			"""train[:10%](pct1_dropremainder)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_pct_rounding

							lowercase_   :     Union[str, Any]									=			"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_test_pct_rounding




def 				lowerCamelCase      ( )     -> int:
							lowercase_   :     Optional[int]									=			"""dummy"""
							lowercase_   :     Union[str, Any]									=			[SplitInfo(name="""train""" , num_examples=100					)]
							lowercase_   :     Optional[int]									=			"""train[:33%]"""
							lowercase_   :     Dict									=			"""arrow"""
							lowercase_   :     int									=			"""prefix"""

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

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




@pytest.mark.parametrize("""name, expected_exception""" , [(None, TypeError), ("""""", ValueError)]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   Any					)     -> List[str]:
							lowercase_   :     str									=			[SplitInfo(name="""train""" , num_examples=100					)]
							lowercase_   :     Any									=			"""train"""
							lowercase_   :     Optional[int]									=			"""arrow"""
							lowercase_   :     str									=			"""prefix_path"""
							with pytest.raises(UpperCAmelCase__					):
														lowercase_   :     List[str]									=			make_file_instructions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)





