

import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil






@pytest.fixture
def  UpperCamelCase_(							_snake_case     :						List[Any]			):




			"""simple docstring"""






			__a						   =tmp_path / 'text.txt'
			__a						   =textwrap.dedent(
			    '\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        '			)
			with open(_snake_case ,  'w' ,  encoding='utf-8'			) as f:
						f.write(_snake_case			)
			return str(_snake_case			)






@pytest.fixture
def  UpperCamelCase_(							_snake_case     :						Optional[int] ,  _snake_case     :						List[str]			):




			"""simple docstring"""






			__a						   =tmp_path / 'text_with_image.txt'
			with open(_snake_case ,  'w' ,  encoding='utf-8'			) as f:
						f.write(_snake_case			)
			return str(_snake_case			)






@pytest.mark.parametrize('keep_linebreaks' ,  [True, False]			)
def  UpperCamelCase_(							_snake_case     :						Any ,  _snake_case     :						Dict			):




			"""simple docstring"""






			with open(_snake_case ,  encoding='utf-8'			) as f:
						__a						   =f.read().splitlines(keepends=_snake_case			)
			__a						   =Text(keep_linebreaks=_snake_case ,  encoding='utf-8'			)
			__a						   =text._generate_tables([[text_file]]			)
			__a						   =pa.concat_tables([table for _, table in generator]			).to_pydict()['text']
			assert generated_content == expected_content






@require_pil
def  UpperCamelCase_(							_snake_case     :						int			):




			"""simple docstring"""






			with open(_snake_case ,  encoding='utf-8'			) as f:
						__a						   =f.read().splitlines()[0]
			__a						   =Text(encoding='utf-8' ,  features=Features({'image': Image()}			)			)
			__a						   =text._generate_tables([[text_file_with_image]]			)
			__a						   =pa.concat_tables([table for _, table in generator]			)
			assert pa_table.schema.field('image'			).type == Image()()
			__a						   =pa_table.to_pydict()['image']
			assert generated_content == [{"path": image_file, "bytes": None}]






@pytest.mark.parametrize('sample_by' ,  ['line', 'paragraph', 'document']			)
def  UpperCamelCase_(							_snake_case     :						Optional[Any] ,  _snake_case     :						Tuple			):




			"""simple docstring"""






			with open(_snake_case ,  encoding='utf-8'			) as f:
						__a						   =f.read()
			if sample_by == "line":
						__a						   =expected_content.splitlines()
			elif sample_by == "paragraph":
						__a						   =expected_content.split('\n\n'			)
			elif sample_by == "document":
						__a						   =[expected_content]
			__a						   =Text(sample_by=_snake_case ,  encoding='utf-8' ,  chunksize=100			)
			__a						   =text._generate_tables([[text_file]]			)
			__a						   =pa.concat_tables([table for _, table in generator]			).to_pydict()['text']
			assert generated_content == expected_content









from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCAmelCase      :    Union[str, Any]						       =    {
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase      :    str						       =    ["ErnieMTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase      :    Optional[Any]						       =    [
						    "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ErnieMForMultipleChoice",
						    "ErnieMForQuestionAnswering",
						    "ErnieMForSequenceClassification",
						    "ErnieMForTokenClassification",
						    "ErnieMModel",
						    "ErnieMPreTrainedModel",
						    "ErnieMForInformationExtraction",
						]


if TYPE_CHECKING:
						from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ernie_m import ErnieMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ernie_m import (
												    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ErnieMForInformationExtraction,
												    ErnieMForMultipleChoice,
												    ErnieMForQuestionAnswering,
												    ErnieMForSequenceClassification,
												    ErnieMForTokenClassification,
												    ErnieMModel,
												    ErnieMPreTrainedModel,
												)


else:
						import sys

						_lowerCAmelCase      :    Tuple						       =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






