
'''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 		A_							(						_lowerCAmelCase :     Optional[Any]				):



      """simple docstring"""




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







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     List[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Any		    = tmp_path / "malformed_file.csv"
      _lowerCamelCase					: Any		    = textwrap.dedent(
          "\\n        header1,header2\n        1,2\n        10,20,\n        "				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: int		    = tmp_path / "csv_with_image.csv"
      _lowerCamelCase					: int		    = textwrap.dedent(
          F'\\n        image\n        {image_file}\n        '				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = tmp_path / "csv_with_label.csv"
      _lowerCamelCase					: int		    = textwrap.dedent(
          "\\n        label\n        good\n        bad\n        good\n        "				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = tmp_path / "csv_with_int_list.csv"
      _lowerCamelCase					: Any		    = textwrap.dedent(
          "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







def 		A_							(						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     Tuple				):



      """simple docstring"""




      _lowerCamelCase					: List[Any]		    = Csv()
      _lowerCamelCase					: Any		    = csv._generate_tables([[csv_file, malformed_csv_file]]				)
      with pytest.raises(_lowerCAmelCase					,						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(_lowerCAmelCase				) in record.message
          for record in caplog.records				)







@require_pil
def 		A_							(						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      with open(_lowerCAmelCase					,						encoding="utf-8"				) as f:
            _lowerCamelCase					: Any		    = f.read().splitlines()[1]
      _lowerCamelCase					: Optional[Any]		    = Csv(encoding="utf-8"					,						features=Features({"image": Image()}				)				)
      _lowerCamelCase					: Union[str, Any]		    = csv._generate_tables([[csv_file_with_image]]				)
      _lowerCamelCase					: List[str]		    = pa.concat_tables([table for _, table in generator]				)
      assert pa_table.schema.field("image"				).type == Image()()
      _lowerCamelCase					: int		    = pa_table.to_pydict()["image"]
      assert generated_content == [{"path": image_file, "bytes": None}]







def 		A_							(						_lowerCAmelCase :     List[Any]				):



      """simple docstring"""




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







def 		A_							(						_lowerCAmelCase :     str				):



      """simple docstring"""




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

"""simple docstring"""
from typing import List

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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientformer'''




							def __init__( self    ,				snake_case_ = [3, 2, 6, 4]    ,				snake_case_ = [48, 96, 224, 448]    ,				snake_case_ = [True, True, True, True]    ,				snake_case_ = 448    ,				snake_case_ = 32    ,				snake_case_ = 4    ,				snake_case_ = 7    ,				snake_case_ = 5    ,				snake_case_ = 8    ,				snake_case_ = 4    ,				snake_case_ = 0.0    ,				snake_case_ = 16    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 2    ,				snake_case_ = 1    ,				snake_case_ = 0.0    ,				snake_case_ = 1    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = 1e-5    ,				snake_case_ = "gelu"    ,				snake_case_ = 0.02    ,				snake_case_ = 1e-1_2    ,				snake_case_ = 224    ,				snake_case_ = 1e-0_5    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  hidden_sizes
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  depths
													__lowerCAmelCase     =  mlp_expansion_ratio
													__lowerCAmelCase     =  downsamples
													__lowerCAmelCase     =  dim
													__lowerCAmelCase     =  key_dim
													__lowerCAmelCase     =  attention_ratio
													__lowerCAmelCase     =  resolution
													__lowerCAmelCase     =  pool_size
													__lowerCAmelCase     =  downsample_patch_size
													__lowerCAmelCase     =  downsample_stride
													__lowerCAmelCase     =  downsample_pad
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  num_metaad_blocks
													__lowerCAmelCase     =  distillation
													__lowerCAmelCase     =  use_layer_scale
													__lowerCAmelCase     =  layer_scale_init_value
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  batch_norm_eps
