

"""simple docstring"""





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   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	tmp_path / '''file.csv'''
	lowerCAmelCase__	:						List[str]  				=	textwrap.dedent(
	    '''\
        header1,header2
        1,2
        10,20
        ''' )
	with open(A_						,     '''w''' ) as f:
		f.write(A_ )
	return str(A_ )

@pytest.fixture
def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Optional[Any]  				=	tmp_path / '''malformed_file.csv'''
	lowerCAmelCase__	:						Tuple  				=	textwrap.dedent(
	    '''\
        header1,header2
        1,2
        10,20,
        ''' )
	with open(A_						,     '''w''' ) as f:
		f.write(A_ )
	return str(A_ )

@pytest.fixture
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	tmp_path / '''csv_with_image.csv'''
	lowerCAmelCase__	:						Dict  				=	textwrap.dedent(
	    f'\\n        image\n        {image_file}\n        ' )
	with open(A_						,     '''w''' ) as f:
		f.write(A_ )
	return str(A_ )

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

@pytest.fixture
def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Tuple  				=	tmp_path / '''csv_with_int_list.csv'''
	lowerCAmelCase__	:						Dict  				=	textwrap.dedent(
	    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        ''' )
	with open(A_						,     '''w''' ) as f:
		f.write(A_ )
	return str(A_ )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	Csv()
	lowerCAmelCase__	:						Union[str, Any]  				=	csv._generate_tables([[csv_file, malformed_csv_file]] )
	with pytest.raises(A_						,     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(A_ ) in record.message
	    for record in caplog.records )

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

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





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







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




