
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__SCREAMING_SNAKE_CASE     :				str             =       logging.get_logger(__name__)


@dataclass
class 						__magic_name__				:
		_lowerCAmelCase						=		42
		_lowerCAmelCase						=		42
		_lowerCAmelCase						=		None
		_lowerCAmelCase						=		None





		def  _A							(							self      :		int			):
							return json.dumps(dataclasses.asdict(self			)  ,					indent=2			) + "\n"



@dataclass(frozen=snake_case      )
class 						__magic_name__				:
		_lowerCAmelCase						=		42
		_lowerCAmelCase						=		None
		_lowerCAmelCase						=		None
		_lowerCAmelCase						=		None





		def  _A							(							self      :		Union[str, Any]			):
							return json.dumps(dataclasses.asdict(self			)			) + "\n"



class 						__magic_name__				:





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Dict			):
							raise NotImplementedError()





		def  _A							(							self      :		Dict  ,					lowerCamelCase__      :		Tuple			):
							raise NotImplementedError()





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Dict			):
							raise NotImplementedError()





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		Dict			):
							raise NotImplementedError()





		def  _A							(							self      :		Tuple			):
							raise NotImplementedError()





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		Any			):
							if len(self.get_labels()			) > 1:
												lowerCAmelCase						:   str							=       self.get_labels()[int(example.label			)]
							return example





		@classmethod
		def  _A							(							cls      :		List[Any]  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		Tuple=None			):
							with open(lowerCamelCase__  ,					'''r'''  ,					encoding='''utf-8-sig'''			) as f:
												return list(csv.reader(lowerCamelCase__  ,					delimiter='''\t'''  ,					quotechar=lowerCamelCase__			)			)



class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		Any  ,					lowerCamelCase__      :		List[Any]=None  ,					lowerCamelCase__      :		Optional[int]=None  ,					lowerCamelCase__      :		Any="classification"  ,					lowerCamelCase__      :		List[str]=False			):
							lowerCAmelCase						:   Union[str, Any]							=       [] if labels is None else labels
							lowerCAmelCase						:   str							=       [] if examples is None else examples
							lowerCAmelCase						:   Any							=       mode
							lowerCAmelCase						:   List[str]							=       verbose





		def __len__(							self      :		Dict			):
							return len(self.examples			)





		def __getitem__(							self      :		int  ,					lowerCamelCase__      :		Dict			):
							if isinstance(lowerCamelCase__  ,					lowerCamelCase__			):
												return SingleSentenceClassificationProcessor(labels=self.labels  ,					examples=self.examples[idx]			)
							return self.examples[idx]





		@classmethod
		def  _A							(							cls      :		List[str]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[Any]=""  ,					lowerCamelCase__      :		List[Any]=0  ,					lowerCamelCase__      :		Union[str, Any]=1  ,					lowerCamelCase__      :		Union[str, Any]=None  ,					lowerCamelCase__      :		str=False  ,					**lowerCamelCase__      :		Any			):
							lowerCAmelCase						:   Optional[Any]							=       cls(**lowerCamelCase__			)
							processor.add_examples_from_csv(
							    lowerCamelCase__  ,					split_name=lowerCamelCase__  ,					column_label=lowerCamelCase__  ,					column_text=lowerCamelCase__  ,					column_id=lowerCamelCase__  ,					skip_first_row=lowerCamelCase__  ,					overwrite_labels=lowerCamelCase__  ,					overwrite_examples=lowerCamelCase__  ,					)
							return processor





		@classmethod
		def  _A							(							cls      :		Dict  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Union[str, Any]=None  ,					**lowerCamelCase__      :		Dict			):
							lowerCAmelCase						:   List[str]							=       cls(**lowerCamelCase__			)
							processor.add_examples(lowerCamelCase__  ,					labels=lowerCamelCase__			)
							return processor





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		int=""  ,					lowerCamelCase__      :		Optional[int]=0  ,					lowerCamelCase__      :		int=1  ,					lowerCamelCase__      :		int=None  ,					lowerCamelCase__      :		int=False  ,					lowerCamelCase__      :		Dict=False  ,					lowerCamelCase__      :		Optional[int]=False  ,					):
							lowerCAmelCase						:   Tuple							=       self._read_tsv(lowerCamelCase__			)
							if skip_first_row:
												lowerCAmelCase						:   List[Any]							=       lines[1:]
							lowerCAmelCase						:   str							=       []
							lowerCAmelCase						:   Optional[Any]							=       []
							lowerCAmelCase						:   Optional[int]							=       []
							for i, line in enumerate(lowerCamelCase__			):
												texts.append(line[column_text]			)
												labels.append(line[column_label]			)
												if column_id is not None:
																	ids.append(line[column_id]			)
												else:
																	lowerCAmelCase						:   str							=       f'''{split_name}-{i}''' if split_name else str(lowerCamelCase__			)
																	ids.append(lowerCamelCase__			)

							return self.add_examples(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					overwrite_labels=lowerCamelCase__  ,					overwrite_examples=lowerCamelCase__			)





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		List[Any]=None  ,					lowerCamelCase__      :		List[str]=None  ,					lowerCamelCase__      :		Union[str, Any]=False  ,					lowerCamelCase__      :		Optional[Any]=False			):
							if labels is not None and len(lowerCamelCase__			) != len(lowerCamelCase__			):
												raise ValueError(
												    f'''Text and labels have mismatched lengths {len(lowerCamelCase__			)} and {len(lowerCamelCase__			)}'''			)
							if ids is not None and len(lowerCamelCase__			) != len(lowerCamelCase__			):
												raise ValueError(f'''Text and ids have mismatched lengths {len(lowerCamelCase__			)} and {len(lowerCamelCase__			)}'''			)
							if ids is None:
												lowerCAmelCase						:   Tuple							=       [None] * len(lowerCamelCase__			)
							if labels is None:
												lowerCAmelCase						:   Optional[int]							=       [None] * len(lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       []
							lowerCAmelCase						:   List[Any]							=       set()
							for text_or_text_and_label, label, guid in zip(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			):
												if isinstance(lowerCamelCase__  ,					(tuple, list)			) and label is None:
																	lowerCAmelCase		,							lowerCAmelCase						:   Optional[Any]							=       text_or_text_and_label
												else:
																	lowerCAmelCase						:   Dict							=       text_or_text_and_label
												added_labels.add(lowerCamelCase__			)
												examples.append(InputExample(guid=lowerCamelCase__  ,					text_a=lowerCamelCase__  ,					text_b=lowerCamelCase__  ,					label=lowerCamelCase__			)			)

							# Update examples
							if overwrite_examples:
												lowerCAmelCase						:   str							=       examples
							else:
												self.examples.extend(lowerCamelCase__			)

							# Update labels
							if overwrite_labels:
												lowerCAmelCase						:   Any							=       list(lowerCamelCase__			)
							else:
												lowerCAmelCase						:   Any							=       list(set(self.labels			).union(lowerCamelCase__			)			)

							return self.examples





		def  _A							(							self      :		Dict  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		Any=None  ,					lowerCamelCase__      :		str=False  ,					lowerCamelCase__      :		Optional[int]=0  ,					lowerCamelCase__      :		Optional[int]=True  ,					lowerCamelCase__      :		Tuple=None  ,					):
							if max_length is None:
												lowerCAmelCase						:   Any							=       tokenizer.max_len

							lowerCAmelCase						:   int							=       {label: i for i, label in enumerate(self.labels			)}

							lowerCAmelCase						:   str							=       []
							for ex_index, example in enumerate(self.examples			):
												if ex_index % 1_0_0_0_0 == 0:
																	logger.info(f'''Tokenizing example {ex_index}'''			)

												lowerCAmelCase						:   Union[str, Any]							=       tokenizer.encode(
												    example.text_a  ,					add_special_tokens=lowerCamelCase__  ,					max_length=min(lowerCamelCase__  ,					tokenizer.max_len			)  ,					)
												all_input_ids.append(lowerCamelCase__			)

							lowerCAmelCase						:   Union[str, Any]							=       max(len(lowerCamelCase__			) for input_ids in all_input_ids			)

							lowerCAmelCase						:   Tuple							=       []
							for ex_index, (input_ids, example) in enumerate(zip(lowerCamelCase__  ,					self.examples			)			):
												if ex_index % 1_0_0_0_0 == 0:
																	logger.info(f'''Writing example {ex_index}/{len(self.examples			)}'''			)
												# The mask has 1 for real tokens and 0 for padding tokens. Only real
												# tokens are attended to.
												lowerCAmelCase						:   Union[str, Any]							=       [1 if mask_padding_with_zero else 0] * len(lowerCamelCase__			)

												# Zero-pad up to the sequence length.
												lowerCAmelCase						:   List[Any]							=       batch_length - len(lowerCamelCase__			)
												if pad_on_left:
																	lowerCAmelCase						:   Dict							=       ([pad_token] * padding_length) + input_ids
																	lowerCAmelCase						:   List[str]							=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
												else:
																	lowerCAmelCase						:   Union[str, Any]							=       input_ids + ([pad_token] * padding_length)
																	lowerCAmelCase						:   int							=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

												if len(lowerCamelCase__			) != batch_length:
																	raise ValueError(f'''Error with input length {len(lowerCamelCase__			)} vs {batch_length}'''			)
												if len(lowerCamelCase__			) != batch_length:
																	raise ValueError(f'''Error with input length {len(lowerCamelCase__			)} vs {batch_length}'''			)

												if self.mode == "classification":
																	lowerCAmelCase						:   Tuple							=       label_map[example.label]
												elif self.mode == "regression":
																	lowerCAmelCase						:   str							=       float(example.label			)
												else:
																	raise ValueError(self.mode			)

												if ex_index < 5 and self.verbose:
																	logger.info('''*** Example ***'''			)
																	logger.info(f'''guid: {example.guid}'''			)
																	logger.info(f'''input_ids: {" ".join([str(lowerCamelCase__			) for x in input_ids]			)}'''			)
																	logger.info(f'''attention_mask: {" ".join([str(lowerCamelCase__			) for x in attention_mask]			)}'''			)
																	logger.info(f'''label: {example.label} (id = {label})'''			)

												features.append(InputFeatures(input_ids=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					label=lowerCamelCase__			)			)

							if return_tensors is None:
												return features
							elif return_tensors == "tf":
												if not is_tf_available():
																	raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''			)
												import tensorflow as tf

												def gen():
																	for ex in features:
																						yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

												lowerCAmelCase						:   Optional[int]							=       tf.data.Dataset.from_generator(
												    lowerCamelCase__  ,					({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)  ,					({'''input_ids''': tf.TensorShape([None]			), '''attention_mask''': tf.TensorShape([None]			)}, tf.TensorShape([]			))  ,					)
												return dataset
							elif return_tensors == "pt":
												if not is_torch_available():
																	raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''			)
												import torch
												from torch.utils.data import TensorDataset

												lowerCAmelCase						:   Tuple							=       torch.tensor([f.input_ids for f in features]  ,					dtype=torch.long			)
												lowerCAmelCase						:   List[Any]							=       torch.tensor([f.attention_mask for f in features]  ,					dtype=torch.long			)
												if self.mode == "classification":
																	lowerCAmelCase						:   Any							=       torch.tensor([f.label for f in features]  ,					dtype=torch.long			)
												elif self.mode == "regression":
																	lowerCAmelCase						:   Dict							=       torch.tensor([f.label for f in features]  ,					dtype=torch.float			)

												lowerCAmelCase						:   List[str]							=       TensorDataset(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)
												return dataset
							else:
												raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''			)


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__				(  snake_case      ):





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		List[Any]=False			):
							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   List[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()
							lowerCAmelCase						:   int							=       pa.Table.from_pydict({'''filename''': [Path(lowerCamelCase__			).name] * 1_0_0}			)
							if take == -1:
												lowerCAmelCase						:   Optional[int]							=       len(lowerCamelCase__			) - skip
							if skip is not None and take is not None:
												lowerCAmelCase						:   List[str]							=       pa_table.slice(lowerCamelCase__  ,					lowerCamelCase__			)
							return pa_table



class 						__magic_name__				(  snake_case      ):





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   List[str]							=       '''my_name'''
							lowerCAmelCase						:   List[str]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   str							=       SplitInfo(name='''test'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   int							=       [train_info, test_info]
							lowerCAmelCase						:   Union[str, Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       DatasetInfo(splits=lowerCamelCase__			)

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

												lowerCAmelCase						:   int							=       '''test[:33%]'''
												lowerCAmelCase						:   Dict							=       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			)

												lowerCAmelCase						:   str							=       ['''train''', '''test[:33%]''']
												lowerCAmelCase						:   Tuple							=       [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''			)]
												for instructions in [instructionsa, instructionsa]:
																	lowerCAmelCase						:   Optional[Any]							=       [reader.read(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			) for instr in instructions]
																	lowerCAmelCase		,							lowerCAmelCase						:   Tuple							=       (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  _A							(							self      :		str			):
							lowerCAmelCase						:   Optional[Any]							=       '''my_name'''
							lowerCAmelCase						:   List[Any]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0_0  ,					shard_lengths=[1_0_0] * 1_0			)
							lowerCAmelCase						:   Optional[int]							=       [train_info]
							lowerCAmelCase						:   Union[str, Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   Optional[Any]							=       DatasetInfo(splits=lowerCamelCase__			)

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

												lowerCAmelCase						:   Dict							=       '''train[:33%]'''
												lowerCAmelCase						:   int							=       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  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Union[str, Any]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   int							=       SplitInfo(name='''test'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   Optional[Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       DatasetInfo(splits=lowerCamelCase__			)

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

												lowerCAmelCase						:   int							=       [
												    {'''filename''': os.path.join(lowerCamelCase__  ,					'''train'''			)},
												    {'''filename''': os.path.join(lowerCamelCase__  ,					'''test'''			), '''skip''': 1_0, '''take''': 1_0},
												]
												lowerCAmelCase						:   Optional[int]							=       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       UpperCAmelCase__							(    __magic_name__  :       Any							,					__magic_name__  :       Dict							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					lowerCAmelCase						:   Dict							=       arrow_file
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										lowerCAmelCase						:   Any							=       ArrowReader.read_table(__magic_name__							,					in_memory=__magic_name__					)
					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       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       arrow_file
					lowerCAmelCase						:   Union[str, Any]							=       ArrowReader(''''''							,					__magic_name__					)
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										lowerCAmelCase						:   Union[str, Any]							=       reader.read_files([{'''filename''': filename}]							,					in_memory=__magic_name__					)
					assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
					lowerCAmelCase						:   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       UpperCAmelCase__							(    ):


					'''simple docstring'''

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

					lowerCAmelCase						:   Tuple							=       '''train+test'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_test

					lowerCAmelCase						:   List[Any]							=       '''train[2:10]'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_abs

					lowerCAmelCase						:   Optional[Any]							=       '''train[15%:-20%]'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_pct

					lowerCAmelCase						:   Tuple							=       '''train[:10%](closest)'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == "train[:10%]"

					lowerCAmelCase						:   Optional[int]							=       '''train[:10%](pct1_dropremainder)'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_pct_rounding

					lowerCAmelCase						:   Optional[int]							=       '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_test_pct_rounding
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       '''dummy'''
					lowerCAmelCase						:   Optional[Any]							=       [SplitInfo(name='''train'''							,					num_examples=1_00					)]
					lowerCAmelCase						:   Optional[int]							=       '''train[:33%]'''
					lowerCAmelCase						:   Dict							=       '''arrow'''
					lowerCAmelCase						:   Optional[int]							=       '''prefix'''

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

					lowerCAmelCase						:   Dict							=       [SplitInfo(name='''train'''							,					num_examples=1_00							,					shard_lengths=[10] * 10					)]
					lowerCAmelCase						:   str							=       make_file_instructions(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)
					assert isinstance(__magic_name__							,					__magic_name__					)
					assert file_instructions.num_examples == 33
					assert file_instructions.file_instructions == [
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00000-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00001-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00002-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00003-of-00010.arrow'''					), "skip": 0, "take": 3},
					]
@pytest.mark.parametrize('''name, expected_exception'''							,					[(None, TypeError), ('''''', ValueError)]					)
def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       [SplitInfo(name='''train'''							,					num_examples=1_00					)]
					lowerCAmelCase						:   List[Any]							=       '''train'''
					lowerCAmelCase						:   Tuple							=       '''arrow'''
					lowerCAmelCase						:   List[Any]							=       '''prefix_path'''
					with pytest.raises(__magic_name__					):
										lowerCAmelCase						:   Optional[Any]							=       make_file_instructions(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)
