

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					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	tmp_path / '''file.csv'''
						snake_case_   =	textwrap.dedent(
						    '''\
        header1,header2
        1,2
        10,20
        '''       )
						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''       ) as f:
												f.write(SCREAMING_SNAKE_CASE__       )
						return str(SCREAMING_SNAKE_CASE__       )


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


@pytest.fixture
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	tmp_path / '''csv_with_image.csv'''
						snake_case_   =	textwrap.dedent(
						    F'''\
        image
        {image_file}
        '''       )
						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''       ) as f:
												f.write(SCREAMING_SNAKE_CASE__       )
						return str(SCREAMING_SNAKE_CASE__       )


@pytest.fixture
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	tmp_path / '''csv_with_label.csv'''
						snake_case_   =	textwrap.dedent(
						    '''\
        label
        good
        bad
        good
        '''       )
						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''       ) as f:
												f.write(SCREAMING_SNAKE_CASE__       )
						return str(SCREAMING_SNAKE_CASE__       )


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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	Csv()
						snake_case_   =	csv._generate_tables([[csv_file, malformed_csv_file]]       )
						with pytest.raises(SCREAMING_SNAKE_CASE__  ,		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(SCREAMING_SNAKE_CASE__       ) in record.message
						    for record in caplog.records       )


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


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


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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XGLMForCausalLM''',
					    '''XGLMModel''',
					    '''XGLMPreTrainedModel''',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxXGLMForCausalLM''',
					    '''FlaxXGLMModel''',
					    '''FlaxXGLMPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXGLMForCausalLM''',
					    '''TFXGLMModel''',
					    '''TFXGLMPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm import XGLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm_fast import XGLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xglm import (
										    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXGLMForCausalLM,
										    TFXGLMModel,
										    TFXGLMPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)