




'''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						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			tmp_path / '''text.txt'''
   SCREAMING_SNAKE_CASE__      :					str							    =			textwrap.dedent(
       '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

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


@pytest.fixture
def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					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 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

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


@require_pil
def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

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




   '''simple docstring'''

   with open(snake_case						,  encoding='''utf-8''' ) as f:
      SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			f.read()
   if sample_by == "line":
      SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			expected_content.splitlines()
   elif sample_by == "paragraph":
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			expected_content.split('''\n\n''' )
   elif sample_by == "document":
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			[expected_content]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			Text(sample_by=snake_case						,  encoding='''utf-8'''						,  chunksize=1_00 )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			text._generate_tables([[text_file]] )
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			pa.concat_tables([table for _, table in generator] ).to_pydict()['''text''']
   assert generated_content == expected_content







'''simple docstring'''





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''EfficientNetImageProcessor'''
      __lowercase					=	('''BertTokenizer''', '''BertTokenizerFast''')






      def __init__(self			,	a__			,	a__       )							-> Tuple:
         super().__init__(a__			,	a__       )






      def __call__(self			,	a__=None			,	a__=None			,	a__="max_length"			,	a__=6_4			,	a__=None			,	**a__       )							-> Any:
         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

         if text is not None:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.tokenizer(
                a__			,	padding=a__			,	max_length=a__			,	return_tensors=a__			,	**a__       )

         if images is not None:
            SCREAMING_SNAKE_CASE__      :					Any							    =			self.image_processor(a__			,	return_tensors=a__			,	**a__       )

         if text is not None and images is not None:
            SCREAMING_SNAKE_CASE__      :					str							    =			image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__       )			,	tensor_type=a__       )






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.tokenizer.model_input_names
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

