






"""simple docstring"""





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 				A(  snake_case_		):
       """simple docstring"""





       lowercase__:				Optional[Any]			  =			tmp_path / "text.txt"
       lowercase__:				List[str]			  =			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 				A(  snake_case_						,   snake_case_		):
       """simple docstring"""





       lowercase__:				str			  =			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 				A(  snake_case_						,   snake_case_		):
       """simple docstring"""





       with open(snake_case_						,   encoding="utf-8"		) as f:
              lowercase__:				int			  =			f.read().splitlines(keepends=snake_case_		)
       lowercase__:				int			  =			Text(keep_linebreaks=snake_case_						,   encoding="utf-8"		)
       lowercase__:				Optional[Any]			  =			text._generate_tables([[text_file]]		)
       lowercase__:				Any			  =			pa.concat_tables([table for _, table in generator]		).to_pydict()["text"]
       assert generated_content == expected_content






@require_pil
def 				A(  snake_case_		):
       """simple docstring"""





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







@pytest.mark.parametrize("sample_by"						,   ["line", "paragraph", "document"]		)
def 				A(  snake_case_						,   snake_case_		):
       """simple docstring"""





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









from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_						=					{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'HubertForCTC',
					    'HubertForSequenceClassification',
					    'HubertModel',
					    'HubertPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFHubertForCTC',
					    'TFHubertModel',
					    'TFHubertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


