code: 
  '''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       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					List[str]     =	tmp_path / "text.txt"
  		snake_case     :					Tuple     =	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(lowercase__   ,					'''w'''   ,					encoding='''utf-8''' ) as f:
  				f.write(lowercase__ )
  		return str(lowercase__ )
  
  
  
  
  @pytest.fixture
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       str:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Any     =	tmp_path / "text_with_image.txt"
  		with open(lowercase__   ,					'''w'''   ,					encoding='''utf-8''' ) as f:
  				f.write(lowercase__ )
  		return str(lowercase__ )
  
  
  
  
  @pytest.mark.parametrize('''keep_linebreaks'''   ,					[True, False] )
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		with open(lowercase__   ,					encoding='''utf-8''' ) as f:
  				snake_case     :					List[Any]     =	f.read().splitlines(keepends=lowercase__ )
  		snake_case     :					List[Any]     =	Text(keep_linebreaks=lowercase__   ,					encoding='''utf-8''' )
  		snake_case     :					Optional[int]     =	text._generate_tables([[text_file]] )
  		snake_case     :					int     =	pa.concat_tables([table for _, table in generator] ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  
  
  
  @require_pil
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[int]:
  
  
  		'''simple docstring'''
  
  
  		with open(lowercase__   ,					encoding='''utf-8''' ) as f:
  				snake_case     :					Union[str, Any]     =	f.read().splitlines()[0]
  		snake_case     :					List[Any]     =	Text(encoding='''utf-8'''   ,					features=Features({'''image''': Image()} ) )
  		snake_case     :					Tuple     =	text._generate_tables([[text_file_with_image]] )
  		snake_case     :					List[str]     =	pa.concat_tables([table for _, table in generator] )
  		assert pa_table.schema.field('''image''' ).type == Image()()
  		snake_case     :					Optional[Any]     =	pa_table.to_pydict()["image"]
  		assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  
  @pytest.mark.parametrize('''sample_by'''   ,					['''line''', '''paragraph''', '''document'''] )
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
  		'''simple docstring'''
  
  
  		with open(lowercase__   ,					encoding='''utf-8''' ) as f:
  				snake_case     :					Tuple     =	f.read()
  		if sample_by == "line":
  				snake_case     :					Optional[int]     =	expected_content.splitlines()
  		elif sample_by == "paragraph":
  				snake_case     :					str     =	expected_content.split('''\n\n''' )
  		elif sample_by == "document":
  				snake_case     :					List[Any]     =	[expected_content]
  		snake_case     :					str     =	Text(sample_by=lowercase__   ,					encoding='''utf-8'''   ,					chunksize=100 )
  		snake_case     :					Optional[int]     =	text._generate_tables([[text_file]] )
  		snake_case     :					Optional[Any]     =	pa.concat_tables([table for _, table in generator] ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  
  
style_context: 
  '''simple docstring'''
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: List[str]			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	AutoTokenizer.from_pretrained(UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: str			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Any     =	None
  						for l in x:
  								snake_case     :					str     =	self.tokenizer(
  								    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  								snake_case     :					Tuple     =	torch.tensor([len(UpperCamelCase__			)]			)
  								if d is not None:
  										for k in d.keys():
  												snake_case     :					int     =	torch.cat((d[k], t[k])     ,  0			)
  								else:
  										snake_case     :					List[str]     =	t
  
  						snake_case     :					Union[str, Any]     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''			)			)
  						snake_case     :					int     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''			)			)
  
  				elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Optional[int]     =	self.tokenizer(
  						    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  
  				else:
  						raise Exception(
  						    '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
  						    ''' strings` for supports are supported.'''			)
  
  				return d
  
  
  
  		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Any=0.70			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	[]
  				for idx in range(len(W_query['''input_ids''']			)			):
  						snake_case     :					Optional[int]     =	range(p_start.shape[1]			)
  
  						snake_case     :					str     =	[]
  						for start_id in start_indexes:
  								for end_id in end_indexes:
  										if start_id < end_id:
  												output.append(
  												    (
  												        start_id,
  												        end_id,
  												        p_start[idx][start_id].item(),
  												        p_end[idx][end_id].item(),
  												    )			)
  
  						output.sort(key=lambda UpperCamelCase__			: (tup[2] * tup[3])     ,  reverse=UpperCamelCase__			)
  						snake_case     :					int     =	[]
  						for k in range(len(UpperCamelCase__			)			):
  								if output[k][2] * output[k][3] >= thresh:
  										snake_case ,snake_case     :					Tuple     =	output[k][0], output[k][1]
  										snake_case     :					str     =	self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]			)
  										temp.append((decoded, output[k][2] * output[k][3])			)
  
  						final_outputs.append(UpperCamelCase__			)
  
  				return final_outputs
  
  
  