






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCamelCase__  :					Dict  						=    """\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n"""

UpperCamelCase__  :					Tuple  						=    logging.getLogger(__name__)







@dataclass
class  lowerCamelCase_   :
							SCREAMING_SNAKE_CASE_     =	field(
							    default='roberta-large-mnli'       ,  metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}   )
							SCREAMING_SNAKE_CASE_     =	field(
							    default='This example is {}.'       ,  metadata={
							        'help': (
							            'Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'
							            'where class name is inserted.'
							        )
							    }       ,  )
							SCREAMING_SNAKE_CASE_     =	field(
							    default=32       ,  metadata={'help': 'Batch size for generating teacher predictions.'}   )
							SCREAMING_SNAKE_CASE_     =	field(
							    default=__lowercase       ,  metadata={
							        'help': (
							            'Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'
							            'multi-class multi-label classification).'
							        )
							    }       ,  )
							SCREAMING_SNAKE_CASE_     =	field(
							    default=1.0       ,  metadata={'help': 'Temperature applied to teacher softmax for distillation.'}   )







@dataclass
class  lowerCamelCase_   :
							SCREAMING_SNAKE_CASE_     =	field(
							    default='distilbert-base-uncased'       ,  metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}   )







@dataclass
class  lowerCamelCase_   :
							SCREAMING_SNAKE_CASE_     =	field(metadata={'help': 'Text file with one unlabeled instance per line.'}   )
							SCREAMING_SNAKE_CASE_     =	field(metadata={'help': 'Text file with one class name per line.'}   )
							SCREAMING_SNAKE_CASE_     =	field(
							    default=__lowercase       ,  metadata={'help': 'Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'}       ,  )







@dataclass
class  lowerCamelCase_   (		__lowercase   ):
							SCREAMING_SNAKE_CASE_     =	field(
							    default=__lowercase       ,  metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}       ,  )
							SCREAMING_SNAKE_CASE_     =	field(
							    default=32       ,  metadata={'help': 'Batch size per GPU/TPU core/CPU for training.'}   )
							SCREAMING_SNAKE_CASE_     =	field(
							    default=1_28       ,  metadata={'help': 'Batch size per GPU/TPU core/CPU for evaluation.'}   )
							SCREAMING_SNAKE_CASE_     =	field(default=1.0       ,  metadata={'help': 'Total number of training epochs to perform.'}   )
							SCREAMING_SNAKE_CASE_     =	field(default=__lowercase       ,  metadata={'help': 'Whether to run training of student model.'}   )
							SCREAMING_SNAKE_CASE_     =	field(
							    default=__lowercase       ,  metadata={
							        'help': (
							            'Whether to evaluate the agreement of the final student predictions and the teacher predictions'
							            'after training.'
							        )
							    }       ,  )
							SCREAMING_SNAKE_CASE_     =	field(
							    default=0       ,  metadata={
							        'help': (
							            'Limit the total amount of checkpoints.'
							            'Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'
							        )
							    }       ,  )







class  lowerCamelCase_   (		__lowercase   ):
							def        SCREAMING_SNAKE_CASE_       (  self						:    Tuple							,__lowerCamelCase						:    Optional[Any]							,__lowerCamelCase						:    Union[str, Any]							,__lowerCamelCase						:    Union[str, Any]=False    ):





								'''simple docstring'''



								a				=	inputs["labels"]
								a				=	model(inputs['''input_ids''']							,attention_mask=inputs['''attention_mask''']    )
								a				=	outputs[0]

								a				=	-torch.sum(target_p * logits.log_softmax(dim=-1    )							,axis=-1    ).mean()

								if return_outputs:
									return loss, outputs

								return loss





def      SCREAMING_SNAKE_CASE__  (      snake_case_     )      ->     List[Any]:


	"""simple docstring"""



	a				=	[]
	with open(__lowerCamelCase,     '''r'''     ) as f:
		for line in f:
			a				=	line.strip()
			if len(__lowerCamelCase     ) > 0:
				lines.append(__lowerCamelCase     )
	return lines





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_,     snake_case_     )      ->     Dict:


	"""simple docstring"""



	a				=	[]
	a				=	[]
	for example in examples:
		for name in class_names:
			premises.append(__lowerCamelCase     )
			hypotheses.append(hypothesis_template.format(__lowerCamelCase     )     )
	return premises, hypotheses





def      SCREAMING_SNAKE_CASE__  (      snake_case_     )      ->     int:


	"""simple docstring"""



	for label, ind in config.labelaid.items():
		if label.lower().startswith('''entail'''     ):
			return ind
	logger.warning('''Could not identify entailment dimension from teacher config label2id. Setting to -1.'''     )
	return -1





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_,     )      ->     str:


	"""simple docstring"""



	a				=	AutoModelForSequenceClassification.from_pretrained(__lowerCamelCase     )
	a				=	model.config
	if not no_cuda and torch.cuda.is_available():
		a				=	nn.DataParallel(model.cuda()     )
		batch_size *= len(model.device_ids     )
	a				=	AutoTokenizer.from_pretrained(__lowerCamelCase,     use_fast=__lowerCamelCase     )

	a				=	get_premise_hypothesis_pairs(__lowerCamelCase,     __lowerCamelCase,     __lowerCamelCase     )
	a				=	[]

	for i in tqdm(range(0,     len(__lowerCamelCase     ),     __lowerCamelCase     )     ):
		a				=	premises[i : i + batch_size]
		a				=	hypotheses[i : i + batch_size]

		a				=	tokenizer(
		    __lowerCamelCase,     __lowerCamelCase,     padding=__lowerCamelCase,     truncation='''only_first''',     return_tensors='''pt''',     )

		with torch.cuda.amp.autocast(enabled=__lowerCamelCase     ):
			with torch.no_grad():
				a				=	model(**__lowerCamelCase     )
		logits.append(outputs.logits.detach().cpu().float()     )

	a				=	get_entailment_id(__lowerCamelCase     )
	a				=	-1 if entail_id == 0 else 0
	a				=	torch.cat(__lowerCamelCase,     dim=0     )  # N*K x 3
	a				=	logits.reshape(len(__lowerCamelCase     ),     len(__lowerCamelCase     ),     -1     )[..., [contr_id, entail_id]]  # N x K x 2

	if multi_label:
		# softmax over (contr, entail) logits for each class independently
		a				=	(nli_logits / temperature).softmax(-1     )
	else:
		# softmax over entail logits across classes s.t. class probabilities sum to 1.
		a				=	(nli_logits / temperature).softmax(1     )

	return nli_prob[..., 1]  # N x K





def      SCREAMING_SNAKE_CASE__  (      )      ->     Optional[Any]:


	"""simple docstring"""



	a				=	HfArgumentParser(
	    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments),     description=__lowerCamelCase,     )

	if len(sys.argv     ) == 2 and sys.argv[1].endswith('''.json'''     ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		a				=	parser.parse_json_file(
		    json_file=os.path.abspath(sys.argv[1]     )     )
	else:
		a				=	parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	a				=	None
	if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
		a				=	get_last_checkpoint(training_args.output_dir     )
		if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
			raise ValueError(
			    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
			    '''Use --overwrite_output_dir to overcome.'''     )
		elif last_checkpoint is not None:
			logger.info(
			    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
			    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''     )

    # Setup logging
	logging.basicConfig(
	    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',     datefmt='''%m/%d/%Y %H:%M:%S''',     handlers=[logging.StreamHandler(sys.stdout     )],     )
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank     ) else logging.WARN     )

	# Log on each process the small summary:
	logger.warning(
	    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
	    + f"""distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}"""     )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank     ):
		utils.logging.set_verbosity_info()
		utils.logging.enable_default_handler()
		utils.logging.enable_explicit_format()

	if training_args.local_rank != -1:
		raise ValueError('''Distributed training is not currently supported.'''     )
	if training_args.tpu_num_cores is not None:
		raise ValueError('''TPU acceleration is not currently supported.'''     )

	logger.info(f"""Training/evaluation parameters {training_args}"""     )

	# Set seed before initializing model.
	set_seed(training_args.seed     )

	# 1. read in data
	a				=	read_lines(data_args.data_file     )
	a				=	read_lines(data_args.class_names_file     )

	# 2. get teacher predictions and load into dataset
	logger.info('''Generating predictions from zero-shot teacher model'''     )
	a				=	get_teacher_predictions(
	    teacher_args.teacher_name_or_path,     __lowerCamelCase,     __lowerCamelCase,     teacher_args.hypothesis_template,     teacher_args.teacher_batch_size,     teacher_args.temperature,     teacher_args.multi_label,     data_args.use_fast_tokenizer,     training_args.no_cuda,     training_args.fpaa,     )
	a				=	Dataset.from_dict(
	    {
	        '''text''': examples,
	        '''labels''': teacher_soft_preds,
	    }     )

	# 3. create student
	logger.info('''Initializing student model'''     )
	a				=	AutoModelForSequenceClassification.from_pretrained(
	    student_args.student_name_or_path,     num_labels=len(__lowerCamelCase     )     )
	a				=	AutoTokenizer.from_pretrained(student_args.student_name_or_path,     use_fast=data_args.use_fast_tokenizer     )
	a				=	dict(enumerate(__lowerCamelCase     )     )
	a				=	{label: i for i, label in enumerate(__lowerCamelCase     )}

	# 4. train student on teacher predictions
	a				=	dataset.map(__lowerCamelCase,     input_columns='''text'''     )
	dataset.set_format('''torch'''     )

	def compute_metrics(snake_case_,     snake_case_=False     ):
		a				=	p.predictions.argmax(-1     )
		a				=	p.label_ids.argmax(-1     )  # "label_ids" are actually distributions
		return {"agreement": (preds == proxy_labels).mean().item()}

	a				=	DistillationTrainer(
	    model=__lowerCamelCase,     tokenizer=__lowerCamelCase,     args=__lowerCamelCase,     train_dataset=__lowerCamelCase,     compute_metrics=__lowerCamelCase,     )

	if training_args.do_train:
		logger.info('''Training student model on teacher predictions'''     )
		trainer.train()

	if training_args.do_eval:
		a				=	trainer.evaluate(eval_dataset=__lowerCamelCase     )["eval_agreement"]
		logger.info(f"""Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%"""     )

	trainer.save_model()


if __name__ == "__main__":
				main()












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		)