
import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__SCREAMING_SNAKE_CASE     :				Any             =       [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
		import run_clm
		import run_image_classification
		import run_mlm
		import run_ner
		import run_qa as run_squad
		import run_summarization
		import run_swag
		import run_text_classification
		import run_translation


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       logging.getLogger()
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   Dict							=       argparse.ArgumentParser()
					parser.add_argument('''-f'''					)
					lowerCAmelCase						:   str							=       parser.parse_args()
					return args.f
def       UpperCAmelCase__							(    __magic_name__  :       str					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[int]							=       {}
					lowerCAmelCase						:   Optional[int]							=       os.path.join(__magic_name__							,					'''all_results.json'''					)
					if os.path.exists(__magic_name__					):
										with open(__magic_name__							,					'''r'''					) as f:
															lowerCAmelCase						:   Optional[Any]							=       json.load(__magic_name__					)
					else:
										raise ValueError(f'''can\'t find {path}'''					)
					return results
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					return bool(tf.config.list_physical_devices('''GPU'''					)					)


__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 						__magic_name__				(  snake_case      ):





		@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''			)
		def  _A							(							self      :		str			):
							lowerCAmelCase						:   Any							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   Optional[Any]							=       f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

							if is_cuda_available():
												testargs.append('''--fp16'''			)

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_text_classification.main()
												# Reset the mixed precision policy so we don't break other tests
												tf.keras.mixed_precision.set_global_policy('''float32'''			)
												lowerCAmelCase						:   Any							=       get_results(lowerCamelCase__			)
												self.assertGreaterEqual(result['''eval_accuracy''']  ,					0.7_5			)





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Tuple							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   Union[str, Any]							=       f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

							if len(tf.config.list_physical_devices('''GPU'''			)			) > 1:
												# Skipping because there are not enough batches to train the model + would need a drop_last to work.
												return

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_clm.main()
												lowerCAmelCase						:   Optional[Any]							=       get_results(lowerCamelCase__			)
												self.assertLess(result['''eval_perplexity''']  ,					1_0_0			)





		def  _A							(							self      :		List[Any]			):
							lowerCAmelCase						:   List[str]							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   Tuple							=       f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_mlm.main()
												lowerCAmelCase						:   List[str]							=       get_results(lowerCamelCase__			)
												self.assertLess(result['''eval_perplexity''']  ,					4_2			)





		def  _A							(							self      :		Optional[int]			):
							# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
							lowerCAmelCase						:   List[Any]							=       7 if get_gpu_count() > 1 else 2

							lowerCAmelCase						:   Tuple							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   Tuple							=       f'''
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_ner.main()
												lowerCAmelCase						:   Dict							=       get_results(lowerCamelCase__			)
												self.assertGreaterEqual(result['''accuracy''']  ,					0.7_5			)





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   List[Any]							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   Dict							=       f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_squad.main()
												lowerCAmelCase						:   Optional[int]							=       get_results(lowerCamelCase__			)
												self.assertGreaterEqual(result['''f1''']  ,					3_0			)
												self.assertGreaterEqual(result['''exact''']  ,					3_0			)





		def  _A							(							self      :		List[Any]			):
							lowerCAmelCase						:   Optional[Any]							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   int							=       f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_swag.main()
												lowerCAmelCase						:   List[str]							=       get_results(lowerCamelCase__			)
												self.assertGreaterEqual(result['''val_accuracy''']  ,					0.8			)





		@slow
		def  _A							(							self      :		Dict			):
							lowerCAmelCase						:   Optional[Any]							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   int							=       f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_summarization.main()
												lowerCAmelCase						:   Optional[Any]							=       get_results(lowerCamelCase__			)
												self.assertGreaterEqual(result['''rouge1''']  ,					1_0			)
												self.assertGreaterEqual(result['''rouge2''']  ,					2			)
												self.assertGreaterEqual(result['''rougeL''']  ,					7			)
												self.assertGreaterEqual(result['''rougeLsum''']  ,					7			)





		@slow
		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   List[Any]							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   Any							=       f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_translation.main()
												lowerCAmelCase						:   Tuple							=       get_results(lowerCamelCase__			)
												self.assertGreaterEqual(result['''bleu''']  ,					3_0			)





		def  _A							(							self      :		str			):
							lowerCAmelCase						:   Optional[int]							=       self.get_auto_remove_tmp_dir()
							lowerCAmelCase						:   List[str]							=       f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

							with patch.object(lowerCamelCase__  ,					'''argv'''  ,					lowerCamelCase__			):
												run_image_classification.main()
												lowerCAmelCase						:   Optional[int]							=       get_results(lowerCamelCase__			)
												self.assertGreaterEqual(result['''accuracy''']  ,					0.7			)


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__					)
