






'''simple docstring'''






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       str  )    -> str:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= tmp_path / 'file.csv'
   __snake_case				:      Optional[int]     							= textwrap.dedent(
       '\\n        header1,header2\n        1,2\n        10,20\n        '  )
   with open(A  ,		'w'  ) as f:
      f.write(A  )
   return str(A  )




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Union[str, Any]     							= tmp_path / 'malformed_file.csv'
   __snake_case				:      Dict     							= textwrap.dedent(
       '\\n        header1,header2\n        1,2\n        10,20,\n        '  )
   with open(A  ,		'w'  ) as f:
      f.write(A  )
   return str(A  )




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  ,		A  :       Any  )    -> Union[str, Any]:







   """simple docstring"""
   __snake_case				:      Union[str, Any]     							= tmp_path / 'csv_with_image.csv'
   __snake_case				:      Optional[int]     							= textwrap.dedent(
       F"""\
        image
        {image_file}
        """  )
   with open(A  ,		'w'  ) as f:
      f.write(A  )
   return str(A  )




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> str:







   """simple docstring"""
   __snake_case				:      int     							= tmp_path / 'csv_with_label.csv'
   __snake_case				:      Optional[int]     							= textwrap.dedent(
       '\\n        label\n        good\n        bad\n        good\n        '  )
   with open(A  ,		'w'  ) as f:
      f.write(A  )
   return str(A  )




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  )    -> Dict:







   """simple docstring"""
   __snake_case				:      str     							= tmp_path / 'csv_with_int_list.csv'
   __snake_case				:      Optional[int]     							= textwrap.dedent(
       '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '  )
   with open(A  ,		'w'  ) as f:
      f.write(A  )
   return str(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  ,		A  :       int  ,		A  :       Tuple  )    -> Tuple:







   """simple docstring"""
   __snake_case				:      Union[str, Any]     							= Csv()
   __snake_case				:      Dict     							= csv._generate_tables([[csv_file, malformed_csv_file]]  )
   with pytest.raises(A  ,		match='Error tokenizing data'  ):
      for _ in generator:
         pass
   assert any(
       record.levelname == 'ERROR'
       and 'Failed to read file' in record.message
       and os.path.basename(A  ) in record.message
       for record in caplog.records  )




@require_pil
def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  )    -> Optional[int]:







   """simple docstring"""
   with open(A  ,		encoding='utf-8'  ) as f:
      __snake_case				:      int     							= f.read().splitlines()[1]
   __snake_case				:      List[Any]     							= Csv(encoding='utf-8'  ,		features=Features({'image': Image()}  )  )
   __snake_case				:      List[Any]     							= csv._generate_tables([[csv_file_with_image]]  )
   __snake_case				:      int     							= 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}]




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  )    -> List[Any]:







   """simple docstring"""
   with open(A  ,		encoding='utf-8'  ) as f:
      __snake_case				:      List[str]     							= f.read().splitlines()[1:]
   __snake_case				:      Union[str, Any]     							= Csv(encoding='utf-8'  ,		features=Features({'label': ClassLabel(names=['good', 'bad']  )}  )  )
   __snake_case				:      Union[str, Any]     							= csv._generate_tables([[csv_file_with_label]]  )
   __snake_case				:      Dict     							= pa.concat_tables([table for _, table in generator]  )
   assert pa_table.schema.field('label'  ).type == ClassLabel(names=['good', 'bad']  )()
   __snake_case				:      List[Any]     							= pa_table.to_pydict()['label']
   assert generated_content == [ClassLabel(names=['good', 'bad']  ).straint(A  ) for label in labels]





def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> int:







   """simple docstring"""
   __snake_case				:      str     							= Csv(encoding='utf-8'  ,		sep=','  ,		converters={'int_list': lambda A  : [int(A  ) for i in x.split()]}  )
   __snake_case				:      Union[str, Any]     							= csv._generate_tables([[csv_file_with_int_list]]  )
   __snake_case				:      Optional[int]     							= pa.concat_tables([table for _, table in generator]  )
   assert pa.types.is_list(pa_table.schema.field('int_list'  ).type  )
   __snake_case				:      Any     							= pa_table.to_pydict()['int_list']
   assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]







'''simple docstring'''






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


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""vit_msn"""







   def __init__(self					,   __a=7_6_8					,   __a=1_2					,   __a=1_2					,   __a=3_0_7_2					,   __a="gelu"					,   __a=0.0					,   __a=0.0					,   __a=0.02					,   __a=1E-06					,   __a=2_2_4					,   __a=1_6					,   __a=3					,   __a=True					,   **__a					,   )			->      Any:
      """simple docstring"""






      super().__init__(**__a)

      __snake_case				:      List[str]     							= hidden_size
      __snake_case				:      Optional[int]     							= num_hidden_layers
      __snake_case				:      Optional[Any]     							= num_attention_heads
      __snake_case				:      str     							= intermediate_size
      __snake_case				:      List[str]     							= hidden_act
      __snake_case				:      List[Any]     							= hidden_dropout_prob
      __snake_case				:      Tuple     							= attention_probs_dropout_prob
      __snake_case				:      List[str]     							= initializer_range
      __snake_case				:      Optional[int]     							= layer_norm_eps
      __snake_case				:      Dict     							= image_size
      __snake_case				:      int     							= patch_size
      __snake_case				:      Dict     							= num_channels
      __snake_case				:      Tuple     							= qkv_bias