

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  ):




	def __init__(						self		:  Optional[int]						,		lowerCAmelCase		:  Dict						,		lowerCAmelCase		:  Optional[Any]=7						,		lowerCAmelCase		:  Any=3						,		lowerCAmelCase		:  Any=18						,		lowerCAmelCase		:  Dict=30						,		lowerCAmelCase		:  Optional[Any]=400						,		lowerCAmelCase		:  List[Any]=True						,		lowerCAmelCase		:  Dict=None						,		lowerCAmelCase		:  Tuple=True						,		lowerCAmelCase		:  int=None						,		lowerCAmelCase		:  Optional[int]=True						,		lowerCAmelCase		:  Optional[Any]=[0.48_145_466, 0.4_578_275, 0.40_821_073]						,		lowerCAmelCase		:  List[str]=[0.26_862_954, 0.26_130_258, 0.27_577_711]						,		lowerCAmelCase		:  Optional[int]=True						,		):


		'''simple docstring'''







		UpperCAmelCase_				 =	size if size is not None else {"shortest_edge": 20}
		UpperCAmelCase_				 =	crop_size if crop_size is not None else {"height": 18, "width": 18}
		UpperCAmelCase_				 =	parent
		UpperCAmelCase_				 =	batch_size
		UpperCAmelCase_				 =	num_channels
		UpperCAmelCase_				 =	image_size
		UpperCAmelCase_				 =	min_resolution
		UpperCAmelCase_				 =	max_resolution
		UpperCAmelCase_				 =	do_resize
		UpperCAmelCase_				 =	size
		UpperCAmelCase_				 =	do_center_crop
		UpperCAmelCase_				 =	crop_size
		UpperCAmelCase_				 =	do_normalize
		UpperCAmelCase_				 =	image_mean
		UpperCAmelCase_				 =	image_std
		UpperCAmelCase_				 =	do_convert_rgb




	def 					__A    (						self		:  int							):


		'''simple docstring'''







		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 					__A    (						self		:  List[Any]						,		lowerCAmelCase		:  Dict=False						,		lowerCAmelCase		:  List[Any]=False						,		lowerCAmelCase		:  Tuple=False							):


		'''simple docstring'''







		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		if equal_resolution:
			UpperCAmelCase_				 =	[]
			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:
			UpperCAmelCase_				 =	[]
			for i in range(self.batch_size							):
				UpperCAmelCase_				 =	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
			UpperCAmelCase_				 =	[Image.fromarray(np.moveaxis(__lowerCamelCase						,		0						,		-1							)							) for x in image_inputs]

		if torchify:
			UpperCAmelCase_				 =	[torch.from_numpy(__lowerCamelCase							) for x in image_inputs]

		return image_inputs


@require_torch
@require_vision
class   __UpperCamelCase			(    __lowercase   ,   unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__			 =   CLIPImageProcessor if is_vision_available() else None




	def 					__A    (						self		:  Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_				 =	CLIPImageProcessingTester(self							)




	@property
	def 					__A    (						self		:  Any							):


		'''simple docstring'''







		return self.image_processor_tester.prepare_image_processor_dict()




	def 					__A    (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_				 =	self.image_processing_class(**self.image_processor_dict							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"do_resize"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"size"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"do_center_crop"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"center_crop"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"do_normalize"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"image_mean"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"image_std"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"do_convert_rgb"							)							)




	def 					__A    (						self		:  Tuple							):


		'''simple docstring'''







		UpperCAmelCase_				 =	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}							)

		UpperCAmelCase_				 =	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 					__A    (						self		:  List[Any]							):


		'''simple docstring'''







		pass




	def 					__A    (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_				 =	self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		UpperCAmelCase_				 =	self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase						,		Image.Image							)

		# Test not batched input
		UpperCAmelCase_				 =	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
		UpperCAmelCase_				 =	image_processing(__lowerCamelCase						,		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 					__A    (						self		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_				 =	self.image_processing_class(**self.image_processor_dict							)
		# create random numpy tensors
		UpperCAmelCase_				 =	self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase						,		numpify=__lowerCamelCase							)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase						,		np.ndarray							)

		# Test not batched input
		UpperCAmelCase_				 =	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
		UpperCAmelCase_				 =	image_processing(__lowerCamelCase						,		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 					__A    (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_				 =	self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		UpperCAmelCase_				 =	self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase						,		torchify=__lowerCamelCase							)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase						,		torch.Tensor							)

		# Test not batched input
		UpperCAmelCase_				 =	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
		UpperCAmelCase_				 =	image_processing(__lowerCamelCase						,		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			(    __lowercase   ,   unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__			 =   CLIPImageProcessor if is_vision_available() else None




	def 					__A    (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_				 =	CLIPImageProcessingTester(self						,		num_channels=4							)
		UpperCAmelCase_				 =	3




	@property
	def 					__A    (						self		:  Optional[Any]							):


		'''simple docstring'''







		return self.image_processor_tester.prepare_image_processor_dict()




	def 					__A    (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_				 =	self.image_processing_class(**self.image_processor_dict							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"do_resize"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"size"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"do_center_crop"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"center_crop"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"do_normalize"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"image_mean"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"image_std"							)							)
		self.assertTrue(hasattr(__lowerCamelCase						,		"do_convert_rgb"							)							)




	def 					__A    (						self		:  List[str]							):


		'''simple docstring'''







		pass




	def 					__A    (						self		:  Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_				 =	self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		UpperCAmelCase_				 =	self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase						,		Image.Image							)

		# Test not batched input
		UpperCAmelCase_				 =	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
		UpperCAmelCase_				 =	image_processing(__lowerCamelCase						,		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"],
		    )						,		)






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__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=False							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							(
                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(__lowerCamelCase							)			,  "wb"							).close()
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							pa.Table.from_pydict({"filename": [Path(__lowerCamelCase							).name] * 1_0_0}							)
            if take == -1:
                 _SCREAMING_SNAKE_CASE				:		int  			=							len(__lowerCamelCase							) - skip
            if skip is not None and take is not None:
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							pa_table.slice(__lowerCamelCase			,  __lowerCamelCase							)
            return pa_table





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							"my_name"
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							SplitInfo(name="train"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							SplitInfo(name="test"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[train_info, test_info]
            _SCREAMING_SNAKE_CASE				:		str  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		str  			=							DatasetInfo(splits=__lowerCamelCase							)

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

                 _SCREAMING_SNAKE_CASE				:		Dict  			=							"test[:33%]"
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							Dataset(**reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)							)
                 self.assertEqual(dset["filename"][0]			,  F"""{name}-test"""							)
                 self.assertEqual(dset.num_rows			,  3_3							)
                 self.assertEqual(dset.num_columns			,  1							)

                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							["train", "test[:33%]"]
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
                 for instructions in [instructionsa, instructionsa]:
                      _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							) for instr in instructions]
                      _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Any  			=							(Dataset(**__lowerCamelCase							) for dataset_kwargs in datasets_kwargs)
                      self.assertEqual(train_dset["filename"][0]			,  F"""{name}-train"""							)
                      self.assertEqual(train_dset.num_rows			,  1_0_0							)
                      self.assertEqual(train_dset.num_columns			,  1							)
                      self.assertIsInstance(train_dset.split			,  __lowerCamelCase							)
                      self.assertEqual(str(train_dset.split							)			,  "train"							)
                      self.assertEqual(test_dset["filename"][0]			,  F"""{name}-test"""							)
                      self.assertEqual(test_dset.num_rows			,  3_3							)
                      self.assertEqual(test_dset.num_columns			,  1							)
                      self.assertIsInstance(test_dset.split			,  __lowerCamelCase							)
                      self.assertEqual(str(test_dset.split							)			,  "test[:33%]"							)
                      del train_dset, test_dset




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							"my_name"
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							SplitInfo(name="train"			,  num_examples=1_0_0_0			,  shard_lengths=[1_0_0] * 1_0							)
            _SCREAMING_SNAKE_CASE				:		str  			=							[train_info]
            _SCREAMING_SNAKE_CASE				:		Dict  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		int  			=							"train[:33%]"
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							Dataset(**reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)							)
                 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			,  3_3_0							)
                 self.assertEqual(dset.num_columns			,  1							)







       def 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							SplitInfo(name="train"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							SplitInfo(name="test"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[
                     {"filename": os.path.join(__lowerCamelCase			,  "train"							)},
                     {"filename": os.path.join(__lowerCamelCase			,  "test"							), "skip": 1_0, "take": 1_0},
                 ]
                 _SCREAMING_SNAKE_CASE				:		Any  			=							Dataset(**reader.read_files(__lowerCamelCase			,  original_instructions="train+test[10:20]"							)							)
                 self.assertEqual(dset.num_rows			,  1_1_0							)
                 self.assertEqual(dset.num_columns			,  1							)
                 del dset






@pytest.mark.parametrize("in_memory",				[False, True]		)
def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Dict  			=							arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							arrow_file
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							ArrowReader("",				__lowerCamelCase		)
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _SCREAMING_SNAKE_CASE				:		Dict  			=							reader.read_files([{"filename": filename}],				in_memory=__lowerCamelCase		)
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							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__	():
     assert ReadInstruction("train",				to=10,				unit="abs"		).to_spec() == "train[:10]"
     assert ReadInstruction("train",				from_=-80,				to=10,				unit="%"		).to_spec() == "train[-80%:10%]"

     _SCREAMING_SNAKE_CASE				:		str  			=							"train+test"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_test

     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"train[2:10]"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_abs

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

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

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

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






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"dummy"
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[SplitInfo(name="train",				num_examples=100		)]
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"train[:33%]"
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"arrow"
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"prefix"

     _SCREAMING_SNAKE_CASE				:		Any  			=							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}
     ]

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[SplitInfo(name="train",				num_examples=100,				shard_lengths=[10] * 10		)]
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[SplitInfo(name="train",				num_examples=100		)]
     _SCREAMING_SNAKE_CASE				:		int  			=							"train"
     _SCREAMING_SNAKE_CASE				:		int  			=							"arrow"
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							"prefix_path"
     with pytest.raises(__lowerCamelCase		):
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							make_file_instructions(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)