
'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__					:						List[str]											=     logging.get_logger(__name__)

UpperCamelCase__					:						Dict											=     {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'vilt'






       def __init__(							self				,							A__=3_05_22				,							A__=2				,							A__=2				,							A__=40				,							A__=7_68				,							A__=12				,							A__=12				,							A__=30_72				,							A__="gelu"				,							A__=0.0				,							A__=0.0				,							A__=0.02				,							A__=1E-12				,							A__=3_84				,							A__=32				,							A__=3				,							A__=True				,							A__=-1				,							A__=False				,							A__=-1				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(tie_word_embeddings=A__				,							**A__					)

          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = type_vocab_size
          _SCREAMING_SNAKE_CASE          = modality_type_vocab_size
          _SCREAMING_SNAKE_CASE          = max_position_embeddings

          _SCREAMING_SNAKE_CASE          = hidden_size
          _SCREAMING_SNAKE_CASE          = num_hidden_layers
          _SCREAMING_SNAKE_CASE          = num_attention_heads
          _SCREAMING_SNAKE_CASE          = intermediate_size
          _SCREAMING_SNAKE_CASE          = hidden_act
          _SCREAMING_SNAKE_CASE          = hidden_dropout_prob
          _SCREAMING_SNAKE_CASE          = attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE          = initializer_range
          _SCREAMING_SNAKE_CASE          = layer_norm_eps

          _SCREAMING_SNAKE_CASE          = image_size
          _SCREAMING_SNAKE_CASE          = patch_size
          _SCREAMING_SNAKE_CASE          = num_channels
          _SCREAMING_SNAKE_CASE          = qkv_bias
          _SCREAMING_SNAKE_CASE          = max_image_length
          _SCREAMING_SNAKE_CASE          = num_images








'''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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = tmp_path / """text.txt"""
   _SCREAMING_SNAKE_CASE          = 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(SCREAMING_SNAKE_CASE_			,					"""w"""			,					encoding="""utf-8"""  ) as f:
      f.write(SCREAMING_SNAKE_CASE_  )
   return str(SCREAMING_SNAKE_CASE_  )
@pytest.fixture
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = tmp_path / """text_with_image.txt"""
   with open(SCREAMING_SNAKE_CASE_			,					"""w"""			,					encoding="""utf-8"""  ) as f:
      f.write(SCREAMING_SNAKE_CASE_  )
   return str(SCREAMING_SNAKE_CASE_  )
@pytest.mark.parametrize("""keep_linebreaks"""			,					[True, False]  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   with open(SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""  ) as f:
      _SCREAMING_SNAKE_CASE          = f.read().splitlines(keepends=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = Text(keep_linebreaks=SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""  )
   _SCREAMING_SNAKE_CASE          = text._generate_tables([[text_file]]  )
   _SCREAMING_SNAKE_CASE          = pa.concat_tables([table for _, table in generator]  ).to_pydict()["""text"""]
   assert generated_content == expected_content
@require_pil
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[str]:




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







@pytest.mark.parametrize("""sample_by"""			,					["""line""", """paragraph""", """document"""]  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   List[Any]:




   """simple docstring"""
   with open(SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""  ) as f:
      _SCREAMING_SNAKE_CASE          = f.read()
   if sample_by == "line":
      _SCREAMING_SNAKE_CASE          = expected_content.splitlines()
   elif sample_by == "paragraph":
      _SCREAMING_SNAKE_CASE          = expected_content.split("""\n\n"""  )
   elif sample_by == "document":
      _SCREAMING_SNAKE_CASE          = [expected_content]
   _SCREAMING_SNAKE_CASE          = Text(sample_by=SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""			,					chunksize=1_00  )
   _SCREAMING_SNAKE_CASE          = text._generate_tables([[text_file]]  )
   _SCREAMING_SNAKE_CASE          = pa.concat_tables([table for _, table in generator]  ).to_pydict()["""text"""]
   assert generated_content == expected_content






