


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      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     str  )  ->      Dict:
       _a   :			Optional[Any]					 =tmp_path / """text.txt"""
       _a   :			int					 =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(_UpperCAmelCase   ,"""w"""   ,encoding="""utf-8"""  ) as f:
              f.write(_UpperCAmelCase  )
       return str(_UpperCAmelCase  )


@pytest.fixture
def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Any   ,_UpperCAmelCase     :     Tuple  )  ->      Dict:
       _a   :			Dict					 =tmp_path / """text_with_image.txt"""
       with open(_UpperCAmelCase   ,"""w"""   ,encoding="""utf-8"""  ) as f:
              f.write(_UpperCAmelCase  )
       return str(_UpperCAmelCase  )


@pytest.mark.parametrize("""keep_linebreaks"""   ,[True, False]  )
def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Tuple   ,_UpperCAmelCase     :     List[Any]  )  ->      Dict:
       with open(_UpperCAmelCase   ,encoding="""utf-8"""  ) as f:
              _a   :			List[Any]					 =f.read().splitlines(keepends=_UpperCAmelCase  )
       _a   :			Optional[Any]					 =Text(keep_linebreaks=_UpperCAmelCase   ,encoding="""utf-8"""  )
       _a   :			Union[str, Any]					 =text._generate_tables([[text_file]]  )
       _a   :			str					 =pa.concat_tables([table for _, table in generator]  ).to_pydict()["""text"""]
       assert generated_content == expected_content


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


@pytest.mark.parametrize("""sample_by"""   ,["""line""", """paragraph""", """document"""]  )
def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Tuple   ,_UpperCAmelCase     :     Dict  )  ->      Tuple:
       with open(_UpperCAmelCase   ,encoding="""utf-8"""  ) as f:
              _a   :			Any					 =f.read()
       if sample_by == "line":
              _a   :			int					 =expected_content.splitlines()
       elif sample_by == "paragraph":
              _a   :			str					 =expected_content.split("""\n\n"""  )
       elif sample_by == "document":
              _a   :			Union[str, Any]					 =[expected_content]
       _a   :			List[Any]					 =Text(sample_by=_UpperCAmelCase   ,encoding="""utf-8"""   ,chunksize=100  )
       _a   :			Optional[Any]					 =text._generate_tables([[text_file]]  )
       _a   :			Tuple					 =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 A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Optional[Any]     	=							["image_processor", "tokenizer"]
      __UpperCamelCase	:  Optional[Any]     	=							"EfficientNetImageProcessor"
      __UpperCamelCase	:  Union[str, Any]     	=							("BertTokenizer", "BertTokenizerFast")






      def __init__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             super().__init__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __call__(			self      :str					,						SCREAMING_SNAKE_CASE      :List[Any]=None					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Union[str, Any]="max_length"					,						SCREAMING_SNAKE_CASE      :Any=6_4					,						SCREAMING_SNAKE_CASE      :List[str]=None					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             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:
                    _a   :			Dict					 =self.tokenizer(
                        SCREAMING_SNAKE_CASE					,						padding=SCREAMING_SNAKE_CASE					,						max_length=SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

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

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






      def __UpperCAmelCase	(			self      :Any					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :str     )	-> Dict:




             '''simple docstring'''
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int					,						*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      @property
      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             _a   :			int					 =self.tokenizer.model_input_names
             _a   :			Union[str, Any]					 =self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


