
"""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 :Dict						) ->      str:
 __UpperCamelCase							    =			tmp_path / """text.txt"""
 __UpperCamelCase							    =			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 :List[Any]							,   snake_case :Tuple						) ->      Optional[Any]:
 __UpperCamelCase							    =			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 :int							,   snake_case :Tuple						) ->      Optional[Any]:



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






@require_pil
def 				A		(							snake_case :Optional[int]						) ->      List[str]:



 with open(snake_case							,   encoding='utf-8'						) as f:
  __UpperCamelCase							    =			f.read().splitlines()[0]
 __UpperCamelCase							    =			Text(encoding='utf-8'							,   features=Features({'image': Image()}						)						)
 __UpperCamelCase							    =			text._generate_tables([[text_file_with_image]]						)
 __UpperCamelCase							    =			pa.concat_tables([table for _, table in generator]						)
 assert pa_table.schema.field('image'						).type == Image()()
 __UpperCamelCase							    =			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 :Tuple							,   snake_case :Dict						) ->      Any:



 with open(snake_case							,   encoding='utf-8'						) as f:
  __UpperCamelCase							    =			f.read()
 if sample_by == "line":
  __UpperCamelCase							    =			expected_content.splitlines()
 elif sample_by == "paragraph":
  __UpperCamelCase							    =			expected_content.split('\n\n'						)
 elif sample_by == "document":
  __UpperCamelCase							    =			[expected_content]
 __UpperCamelCase							    =			Text(sample_by=snake_case							,   encoding='utf-8'							,   chunksize=1_0_0						)
 __UpperCamelCase							    =			text._generate_tables([[text_file]]						)
 __UpperCamelCase							    =			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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_					:		int           = {
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Tuple           = [
	    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''UniSpeechSatForAudioFrameClassification''',
	    '''UniSpeechSatForCTC''',
	    '''UniSpeechSatForPreTraining''',
	    '''UniSpeechSatForSequenceClassification''',
	    '''UniSpeechSatForXVector''',
	    '''UniSpeechSatModel''',
	    '''UniSpeechSatPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_unispeech_sat import (
		    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    UniSpeechSatForAudioFrameClassification,
		    UniSpeechSatForCTC,
		    UniSpeechSatForPreTraining,
		    UniSpeechSatForSequenceClassification,
		    UniSpeechSatForXVector,
		    UniSpeechSatModel,
		    UniSpeechSatPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase_					:		str           = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
