


import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil


@pytest.fixture
def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						tmp_path / '''file.csv'''
						__SCREAMING_SNAKE_CASE		:	str						     =						textwrap.dedent(
						    '''\
        header1,header2
        1,2
        10,20
        ''' )
						with open(_lowerCAmelCase       ,			'''w''' ) as f:
												f.write(_lowerCAmelCase )
						return str(_lowerCAmelCase )


@pytest.fixture
def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	int						     =						tmp_path / '''malformed_file.csv'''
						__SCREAMING_SNAKE_CASE		:	Dict						     =						textwrap.dedent(
						    '''\
        header1,header2
        1,2
        10,20,
        ''' )
						with open(_lowerCAmelCase       ,			'''w''' ) as f:
												f.write(_lowerCAmelCase )
						return str(_lowerCAmelCase )


@pytest.fixture
def 			_UpperCamelCase				( lowercase__       ,			lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						tmp_path / '''csv_with_image.csv'''
						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						textwrap.dedent(
						    F'''\
        image
        {image_file}
        ''' )
						with open(_lowerCAmelCase       ,			'''w''' ) as f:
												f.write(_lowerCAmelCase )
						return str(_lowerCAmelCase )


@pytest.fixture
def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	Tuple						     =						tmp_path / '''csv_with_label.csv'''
						__SCREAMING_SNAKE_CASE		:	List[Any]						     =						textwrap.dedent(
						    '''\
        label
        good
        bad
        good
        ''' )
						with open(_lowerCAmelCase       ,			'''w''' ) as f:
												f.write(_lowerCAmelCase )
						return str(_lowerCAmelCase )


@pytest.fixture
def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	str						     =						tmp_path / '''csv_with_int_list.csv'''
						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						textwrap.dedent(
						    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        ''' )
						with open(_lowerCAmelCase       ,			'''w''' ) as f:
												f.write(_lowerCAmelCase )
						return str(_lowerCAmelCase )


def 			_UpperCamelCase				( lowercase__       ,			lowercase__       ,			lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	int						     =						Csv()
						__SCREAMING_SNAKE_CASE		:	List[Any]						     =						csv._generate_tables([[csv_file, malformed_csv_file]] )
						with pytest.raises(_lowerCAmelCase       ,			match='''Error tokenizing data''' ):
												for _ in generator:
																		pass
						assert any(
						    record.levelname == '''ERROR'''
						    and '''Failed to read file''' in record.message
						    and os.path.basename(_lowerCAmelCase ) in record.message
						    for record in caplog.records )


@require_pil
def 			_UpperCamelCase				( lowercase__ ):
						with open(_lowerCAmelCase       ,			encoding='''utf-8''' ) as f:
												__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						f.read().splitlines()[1]
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						Csv(encoding='''utf-8'''       ,			features=Features({'''image''': Image()} ) )
						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						csv._generate_tables([[csv_file_with_image]] )
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field('''image''' ).type == Image()()
						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						pa_table.to_pydict()['''image''']
						assert generated_content == [{"path": image_file, "bytes": None}]


def 			_UpperCamelCase				( lowercase__ ):
						with open(_lowerCAmelCase       ,			encoding='''utf-8''' ) as f:
												__SCREAMING_SNAKE_CASE		:	Tuple						     =						f.read().splitlines()[1:]
						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						Csv(encoding='''utf-8'''       ,			features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						csv._generate_tables([[csv_file_with_label]] )
						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						pa_table.to_pydict()['''label''']
						assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(_lowerCAmelCase ) for label in labels]


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	Dict						     =						Csv(encoding='''utf-8'''       ,			sep=''','''       ,			converters={'''int_list''': lambda lowercase__ : [int(_lowerCAmelCase ) for i in x.split()]} )
						__SCREAMING_SNAKE_CASE		:	Any						     =						csv._generate_tables([[csv_file_with_int_list]] )
						__SCREAMING_SNAKE_CASE		:	int						     =						pa.concat_tables([table for _, table in generator] )
						assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
						__SCREAMING_SNAKE_CASE		:	int						     =						pa_table.to_pydict()['''int_list''']
						assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]


"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_=None    ,				**snake_case_				)       ->  Optional[Any]:
													warnings.warn(
													    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
													    """instead."""    ,				snake_case_    ,				)
													super().__init__(args=snake_case_    ,				**snake_case_				)
