
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase    					=   logging.getLogger()

def      A (       ):
						snake_case       :     Dict =    argparse.ArgumentParser()
						parser.add_argument('''-f'''       )
						snake_case       :     Any =    parser.parse_args()
						return args.f

def      A (       A_							:					str       ):
						snake_case       :     str =    {}
						snake_case       :     Union[str, Any] =    os.path.join(A_    ,							'''all_results.json'''       )
						if os.path.exists(A_       ):
												with open(A_    ,							'''r'''       ) as f:
																		snake_case       :     List[str] =    json.load(A_       )
						else:
												raise ValueError(F"""can't find {path}"""       )
						return results

def      A (       ):
						snake_case       :     Any =    torch.cuda.is_available() and torch_device == '''cuda'''
						return is_using_cuda and is_apex_available()


UpperCAmelCase    					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 						a   (				__magic_name__    ):




				@classmethod
				def 	__snake_case							(							cls	:				Tuple      ):
										# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
										snake_case       :     int =    tempfile.mkdtemp()
										snake_case       :     Optional[Any] =    os.path.join(cls.tmpdir,    '''default_config.yml'''      )
										write_basic_config(save_location=cls.configPath      )
										snake_case       :     Tuple =    ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]




				@classmethod
				def 	__snake_case							(							cls	:				Optional[int]      ):
										shutil.rmtree(cls.tmpdir      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[Any] =    F"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

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

										run_command(self._launch_args + testargs      )
										snake_case       :     int =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.75      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''glue_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     int =    self.get_auto_remove_tmp_dir()
										snake_case       :     str =    F"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										if torch.cuda.device_count() > 1:
																# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																return

										run_command(self._launch_args + testargs      )
										snake_case       :     List[str] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertLess(result['''perplexity'''],    1_00      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''clm_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     Union[str, Any] =    self.get_auto_remove_tmp_dir()
										snake_case       :     int =    F"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     List[Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertLess(result['''perplexity'''],    42      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''mlm_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Dict      ):
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										snake_case       :     Any =    7 if get_gpu_count() > 1 else 2

										snake_case       :     Optional[int] =    self.get_auto_remove_tmp_dir()
										snake_case       :     str =    F"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Optional[int] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.75      )
										self.assertLess(result['''train_loss'''],    0.5      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''ner_no_trainer'''      )      )      )




				@unittest.skip(reason='''Fix me @muellerzr'''      )
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     Tuple =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[int] =    F"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     str =    get_results(SCREAMING_SNAKE_CASE_      )
										# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
										self.assertGreaterEqual(result['''eval_f1'''],    28      )
										self.assertGreaterEqual(result['''eval_exact'''],    28      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''qa_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     List[Any] =    F"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.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}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Union[str, Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.8      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''swag_no_trainer'''      )      )      )




				@slow
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				str      ):
										snake_case       :     str =    self.get_auto_remove_tmp_dir()
										snake_case       :     Any =    F"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     str =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_rouge1'''],    10      )
										self.assertGreaterEqual(result['''eval_rouge2'''],    2      )
										self.assertGreaterEqual(result['''eval_rougeL'''],    7      )
										self.assertGreaterEqual(result['''eval_rougeLsum'''],    7      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''summarization_no_trainer'''      )      )      )




				@slow
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[int] =    F"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/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}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Optional[int] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_bleu'''],    30      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''translation_no_trainer'''      )      )      )




				@slow
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     Any =    logging.StreamHandler(sys.stdout      )
										logger.addHandler(SCREAMING_SNAKE_CASE_      )

										snake_case       :     Union[str, Any] =    self.get_auto_remove_tmp_dir()
										snake_case       :     Dict =    F"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     List[Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_overall_accuracy'''],    0.10      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     str =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[Any] =    F"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

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

										run_command(self._launch_args + testargs      )
										snake_case       :     Tuple =    get_results(SCREAMING_SNAKE_CASE_      )
										# The base model scores a 25%
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.6      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''step_1'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''image_classification_no_trainer'''      )      )      )



'''simple docstring'''





import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

import pyarrow as pa
import pyarrow.parquet as pq
import pytest

import datasets
import datasets.config

@pytest.fixture(scope='''session'''       )
def      A (       ):
						snake_case       :     Dict =    10
						snake_case       :     List[Any] =    datasets.Features(
						    {
						        '''tokens''': datasets.Sequence(datasets.Value('''string'''       )       ),
						        '''labels''': datasets.Sequence(datasets.ClassLabel(names=['''negative''', '''positive''']       )       ),
						        '''answers''': datasets.Sequence(
						            {
						                '''text''': datasets.Value('''string'''       ),
						                '''answer_start''': datasets.Value('''int32'''       ),
						            }       ),
						        '''id''': datasets.Value('''int64'''       ),
						    }       )
						snake_case       :     Dict =    datasets.Dataset.from_dict(
						    {
						        '''tokens''': [['''foo'''] * 5] * n,
						        '''labels''': [[1] * 5] * n,
						        '''answers''': [{'''answer_start''': [97], '''text''': ['''1976''']}] * 10,
						        '''id''': list(range(A_       )       ),
						    }    ,							features=A_    ,							)
						return dataset

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[Any]    ,							A_							:					Optional[Any]       ):
						snake_case       :     Any =    str(tmp_path_factory.mktemp('''data'''       ) / '''file.arrow'''       )
						dataset.map(cache_file_name=A_       )
						return filename


# FILE_CONTENT + files


UpperCAmelCase    					=   "\\n    Text data.\n    Second line of data."

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]       ):
						snake_case       :     Any =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt'''
						snake_case       :     Union[str, Any] =    FILE_CONTENT
						with open(A_    ,							'''w'''       ) as f:
												f.write(A_       )
						return filename

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[Any]       ):
						import bza

						snake_case       :     Tuple =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.bz2'''
						snake_case       :     Union[str, Any] =    bytes(A_    ,							'''utf-8'''       )
						with bza.open(A_    ,							'''wb'''       ) as f:
												f.write(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[Any]       ):
						import gzip

						snake_case       :     Any =    str(tmp_path_factory.mktemp('''data'''       ) / '''file.txt.gz'''       )
						snake_case       :     Any =    bytes(A_    ,							'''utf-8'''       )
						with gzip.open(A_    ,							'''wb'''       ) as f:
												f.write(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[int]       ):
						if datasets.config.LZ4_AVAILABLE:
												import lza.frame

												snake_case       :     Union[str, Any] =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.lz4'''
												snake_case       :     Tuple =    bytes(A_    ,							'''utf-8'''       )
												with lza.frame.open(A_    ,							'''wb'''       ) as f:
																		f.write(A_       )
												return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Union[str, Any]    ,							A_							:					str       ):
						if datasets.config.PY7ZR_AVAILABLE:
												import pyazr

												snake_case       :     Optional[Any] =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.7z'''
												with pyazr.SevenZipFile(A_    ,							'''w'''       ) as archive:
																		archive.write(A_    ,							arcname=os.path.basename(A_       )       )
												return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]    ,							A_							:					Optional[int]       ):
						import tarfile

						snake_case       :     Union[str, Any] =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.tar'''
						with tarfile.TarFile(A_    ,							'''w'''       ) as f:
												f.add(A_    ,							arcname=os.path.basename(A_       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Any       ):
						import lzma

						snake_case       :     Any =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.xz'''
						snake_case       :     Union[str, Any] =    bytes(A_    ,							'''utf-8'''       )
						with lzma.open(A_    ,							'''wb'''       ) as f:
												f.write(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[int]    ,							A_							:					Union[str, Any]       ):
						import zipfile

						snake_case       :     List[str] =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.basename(A_       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]       ):
						if datasets.config.ZSTANDARD_AVAILABLE:
												import zstandard as zstd

												snake_case       :     Tuple =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.zst'''
												snake_case       :     List[str] =    bytes(A_    ,							'''utf-8'''       )
												with zstd.open(A_    ,							'''wb'''       ) as f:
																		f.write(A_       )
												return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str       ):
						snake_case       :     Optional[int] =    tmp_path_factory.mktemp('''data'''       ) / '''file.xml'''
						snake_case       :     Any =    textwrap.dedent(
						    '''\
    <?xml version="1.0" encoding="UTF-8" ?>
    <tmx version="1.4">
      <header segtype="sentence" srclang="ca" />
      <body>
        <tu>
          <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>
          <tuv xml:lang="en"><seg>Content 1</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>
          <tuv xml:lang="en"><seg>Content 2</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>
          <tuv xml:lang="en"><seg>Content 3</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>
          <tuv xml:lang="en"><seg>Content 4</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>
          <tuv xml:lang="en"><seg>Content 5</seg></tuv>
        </tu>
      </body>
    </tmx>'''       )
						with open(A_    ,							'''w'''       ) as f:
												f.write(A_       )
						return filename


UpperCAmelCase    					=   [
    {"col_1": "0", "col_2": 0, "col_3": 0.0},
    {"col_1": "1", "col_2": 1, "col_3": 1.0},
    {"col_1": "2", "col_2": 2, "col_3": 2.0},
    {"col_1": "3", "col_2": 3, "col_3": 3.0},
]
UpperCAmelCase    					=   [
    {"col_1": "4", "col_2": 4, "col_3": 4.0},
    {"col_1": "5", "col_2": 5, "col_3": 5.0},
]
UpperCAmelCase    					=   {
    "col_1": ["0", "1", "2", "3"],
    "col_2": [0, 1, 2, 3],
    "col_3": [0.0, 1.0, 2.0, 3.0],
}

UpperCAmelCase    					=   [
    {"col_3": 0.0, "col_1": "0", "col_2": 0},
    {"col_3": 1.0, "col_1": "1", "col_2": 1},
]

UpperCAmelCase    					=   [
    {"col_1": "s0", "col_2": 0, "col_3": 0.0},
    {"col_1": "s1", "col_2": 1, "col_3": 1.0},
    {"col_1": "s2", "col_2": 2, "col_3": 2.0},
    {"col_1": "s3", "col_2": 3, "col_3": 3.0},
]

@pytest.fixture(scope='''session'''       )
def      A (       ):
						return DATA_DICT_OF_LISTS

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str       ):
						snake_case       :     str =    datasets.Dataset.from_dict(A_       )
						snake_case       :     List[Any] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.arrow'''       )
						dataset.map(cache_file_name=A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str       ):
						snake_case       :     Optional[int] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.sqlite'''       )
						with contextlib.closing(sqlitea.connect(A_       )       ) as con:
												snake_case       :     Optional[Any] =    con.cursor()
												cur.execute('''CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)'''       )
												for item in DATA:
																		cur.execute('''INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)'''    ,							tuple(item.values()       )       )
												con.commit()
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]       ):
						snake_case       :     Any =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.csv'''       )
						with open(A_    ,							'''w'''    ,							newline=''''''       ) as f:
												snake_case       :     Optional[int] =    csv.DictWriter(A_    ,							fieldnames=['''col_1''', '''col_2''', '''col_3''']       )
												writer.writeheader()
												for item in DATA:
																		writer.writerow(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[int]       ):
						snake_case       :     Optional[int] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset2.csv'''       )
						with open(A_    ,							'''w'''    ,							newline=''''''       ) as f:
												snake_case       :     int =    csv.DictWriter(A_    ,							fieldnames=['''col_1''', '''col_2''', '''col_3''']       )
												writer.writeheader()
												for item in DATA:
																		writer.writerow(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[Any]    ,							A_							:					Optional[int]       ):
						import bza

						snake_case       :     Optional[int] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.csv.bz2'''
						with open(A_    ,							'''rb'''       ) as f:
												snake_case       :     Any =    f.read()
						# data = bytes(FILE_CONTENT, "utf-8")
						with bza.open(A_    ,							'''wb'''       ) as f:
												f.write(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str    ,							A_							:					int    ,							A_							:					List[Any]       ):
						snake_case       :     Optional[Any] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.csv.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.basename(A_       )       )
												f.write(A_    ,							arcname=os.path.basename(A_       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Dict    ,							A_							:					Optional[Any]    ,							A_							:					Dict       ):
						snake_case       :     str =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.csv.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.basename(csv_path.replace('''.csv'''    ,							'''.CSV'''       )       )       )
												f.write(A_    ,							arcname=os.path.basename(csva_path.replace('''.csv'''    ,							'''.CSV'''       )       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Dict    ,							A_							:					Dict    ,							A_							:					Optional[int]       ):
						snake_case       :     Dict =    tmp_path_factory.mktemp('''data'''       ) / '''dataset_with_dir.csv.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.join('''main_dir'''    ,							os.path.basename(A_       )       )       )
												f.write(A_    ,							arcname=os.path.join('''main_dir'''    ,							os.path.basename(A_       )       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Dict       ):
						snake_case       :     List[str] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.parquet'''       )
						snake_case       :     Tuple =    pa.schema(
						    {
						        '''col_1''': pa.string(),
						        '''col_2''': pa.intaa(),
						        '''col_3''': pa.floataa(),
						    }       )
						with open(A_    ,							'''wb'''       ) as f:
												snake_case       :     Dict =    pq.ParquetWriter(A_    ,							schema=A_       )
												snake_case       :     str =    pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(A_       )       )] for k in DATA[0]}    ,							schema=A_       )
												writer.write_table(A_       )
												writer.close()
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]       ):
						snake_case       :     Any =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.json'''       )
						snake_case       :     Optional[int] =    {'''data''': DATA}
						with open(A_    ,							'''w'''       ) as f:
												json.dump(A_    ,							A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str       ):
						snake_case       :     List[str] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.json'''       )
						snake_case       :     Any =    {'''data''': DATA_DICT_OF_LISTS}
						with open(A_    ,							'''w'''       ) as f:
												json.dump(A_    ,							A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[Any]       ):
						snake_case       :     Optional[Any] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.jsonl'''       )
						with open(A_    ,							'''w'''       ) as f:
												for item in DATA:
																		f.write(json.dumps(A_       ) + '''\n'''       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[Any]       ):
						snake_case       :     Optional[int] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset2.jsonl'''       )
						with open(A_    ,							'''w'''       ) as f:
												for item in DATA:
																		f.write(json.dumps(A_       ) + '''\n'''       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[Any]       ):
						snake_case       :     Dict =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset_312.jsonl'''       )
						with open(A_    ,							'''w'''       ) as f:
												for item in DATA_312:
																		f.write(json.dumps(A_       ) + '''\n'''       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					int       ):
						snake_case       :     Dict =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset-str.jsonl'''       )
						with open(A_    ,							'''w'''       ) as f:
												for item in DATA_STR:
																		f.write(json.dumps(A_       ) + '''\n'''       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[int]    ,							A_							:					Optional[int]       ):
						import gzip

						snake_case       :     Dict =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.txt.gz'''       )
						with open(A_    ,							'''rb'''       ) as orig_file:
												with gzip.open(A_    ,							'''wb'''       ) as zipped_file:
																		zipped_file.writelines(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[int]    ,							A_							:					Any       ):
						import gzip

						snake_case       :     List[Any] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.jsonl.gz'''       )
						with open(A_    ,							'''rb'''       ) as orig_file:
												with gzip.open(A_    ,							'''wb'''       ) as zipped_file:
																		zipped_file.writelines(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str    ,							A_							:					List[str]    ,							A_							:					Optional[int]       ):
						snake_case       :     int =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.jsonl.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.basename(A_       )       )
												f.write(A_    ,							arcname=os.path.basename(A_       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]    ,							A_							:					Any    ,							A_							:					Union[str, Any]    ,							A_							:					int       ):
						snake_case       :     List[str] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset_nested.jsonl.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.join('''nested'''    ,							os.path.basename(A_       )       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]    ,							A_							:					Dict    ,							A_							:					Any       ):
						snake_case       :     Optional[Any] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset_with_dir.jsonl.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.join('''main_dir'''    ,							os.path.basename(A_       )       )       )
												f.write(A_    ,							arcname=os.path.join('''main_dir'''    ,							os.path.basename(A_       )       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[Any]    ,							A_							:					Tuple    ,							A_							:					List[str]       ):
						snake_case       :     int =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.jsonl.tar'''
						with tarfile.TarFile(A_    ,							'''w'''       ) as f:
												f.add(A_    ,							arcname=os.path.basename(A_       )       )
												f.add(A_    ,							arcname=os.path.basename(A_       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					Tuple    ,							A_							:					Optional[Any]       ):
						snake_case       :     List[Any] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset_nested.jsonl.tar'''
						with tarfile.TarFile(A_    ,							'''w'''       ) as f:
												f.add(A_    ,							arcname=os.path.join('''nested'''    ,							os.path.basename(A_       )       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					int       ):
						snake_case       :     int =    ['''0''', '''1''', '''2''', '''3''']
						snake_case       :     Optional[int] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.txt'''       )
						with open(A_    ,							'''w'''       ) as f:
												for item in data:
																		f.write(item + '''\n'''       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]       ):
						snake_case       :     Union[str, Any] =    ['''0''', '''1''', '''2''', '''3''']
						snake_case       :     Optional[int] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset2.txt'''       )
						with open(A_    ,							'''w'''       ) as f:
												for item in data:
																		f.write(item + '''\n'''       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[int]       ):
						snake_case       :     Optional[int] =    ['''0''', '''1''', '''2''', '''3''']
						snake_case       :     str =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.abc'''
						with open(A_    ,							'''w'''       ) as f:
												for item in data:
																		f.write(item + '''\n'''       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[Any]    ,							A_							:					Any    ,							A_							:					Union[str, Any]       ):
						snake_case       :     Dict =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.text.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.basename(A_       )       )
												f.write(A_    ,							arcname=os.path.basename(A_       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]    ,							A_							:					Union[str, Any]    ,							A_							:					Optional[Any]       ):
						snake_case       :     List[Any] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset_with_dir.text.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.join('''main_dir'''    ,							os.path.basename(A_       )       )       )
												f.write(A_    ,							arcname=os.path.join('''main_dir'''    ,							os.path.basename(A_       )       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Any    ,							A_							:					Union[str, Any]    ,							A_							:					str       ):
						snake_case       :     List[Any] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.ext.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.basename('''unsupported.ext'''       )       )
												f.write(A_    ,							arcname=os.path.basename('''unsupported_2.ext'''       )       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Union[str, Any]       ):
						snake_case       :     Any =    '''\n'''.join(['''First''', '''Second\u2029with Unicode new line''', '''Third''']       )
						snake_case       :     Optional[int] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset_with_unicode_new_lines.txt'''       )
						with open(A_    ,							'''w'''    ,							encoding='''utf-8'''       ) as f:
												f.write(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       ):
						return os.path.join('''tests'''    ,							'''features'''    ,							'''data'''    ,							'''test_image_rgb.jpg'''       )

@pytest.fixture(scope='''session'''       )
def      A (       ):
						return os.path.join('''tests'''    ,							'''features'''    ,							'''data'''    ,							'''test_audio_44100.wav'''       )

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[Any]    ,							A_							:					Optional[Any]       ):
						snake_case       :     Union[str, Any] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.img.zip'''
						with zipfile.ZipFile(A_    ,							'''w'''       ) as f:
												f.write(A_    ,							arcname=os.path.basename(A_       )       )
												f.write(A_    ,							arcname=os.path.basename(A_       ).replace('''.jpg'''    ,							'''2.jpg'''       )       )
						return path





@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[Any]       ):
						snake_case       :     Dict =    tmp_path_factory.mktemp('''data_dir'''       )

						(data_dir / "subdir").mkdir()
						with open(data_dir / '''subdir''' / '''train.txt'''    ,							'''w'''       ) as f:
												f.write('''foo\n''' * 10       )
						with open(data_dir / '''subdir''' / '''test.txt'''    ,							'''w'''       ) as f:
												f.write('''bar\n''' * 10       )
						# hidden file
						with open(data_dir / '''subdir''' / '''.test.txt'''    ,							'''w'''       ) as f:
												f.write('''bar\n''' * 10       )

						# hidden directory
						(data_dir / ".subdir").mkdir()
						with open(data_dir / '''.subdir''' / '''train.txt'''    ,							'''w'''       ) as f:
												f.write('''foo\n''' * 10       )
						with open(data_dir / '''.subdir''' / '''test.txt'''    ,							'''w'''       ) as f:
												f.write('''bar\n''' * 10       )

						return data_dir

