


'''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    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Dict						=  tmp_path / 'file.csv'
   _a					:  Union[str, 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    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Optional[int]						=  tmp_path / 'malformed_file.csv'
   _a					:  List[str]						=  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    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   _a					:  Any						=  tmp_path / 'csv_with_image.csv'
   _a					:  List[Any]						=  textwrap.dedent(
       f'''\
        image
        {image_file}
        '''      )
   with open(_lowerCamelCase		,	'w'      ) as f:
      f.write(_lowerCamelCase      )
   return str(_lowerCamelCase      )


@pytest.fixture
def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Dict						=  tmp_path / 'csv_with_label.csv'
   _a					:  Optional[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    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Optional[int]						=  tmp_path / 'csv_with_int_list.csv'
   _a					:  Tuple						=  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    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  Dict						=  Csv()
   _a					:  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    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





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


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





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







def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





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








'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_							:	int	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	Dict	     =		{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase):
       __lowercase	:   List[Any]										=      '''swin'''

       __lowercase	:   str										=      {
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=224     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=96     ,	__SCREAMING_SNAKE_CASE=[2, 2, 6, 2]     ,	__SCREAMING_SNAKE_CASE=[3, 6, 12, 24]     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=4.0     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=1E-5     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	int:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	image_size
            __snake_case            =	patch_size
            __snake_case            =	num_channels
            __snake_case            =	embed_dim
            __snake_case            =	depths
            __snake_case            =	len(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	num_heads
            __snake_case            =	window_size
            __snake_case            =	mlp_ratio
            __snake_case            =	qkv_bias
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	drop_path_rate
            __snake_case            =	hidden_act
            __snake_case            =	use_absolute_embeddings
            __snake_case            =	layer_norm_eps
            __snake_case            =	initializer_range
            __snake_case            =	encoder_stride
            # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __snake_case            =	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE	) - 1)	)
            __snake_case            =	['''stem'''] + [F'''stage{idx}''' for idx in range(1     ,	len(__SCREAMING_SNAKE_CASE	) + 1	)]
            __snake_case       ,     __snake_case            =	get_aligned_output_features_output_indices(
                out_features=__SCREAMING_SNAKE_CASE     ,	out_indices=__SCREAMING_SNAKE_CASE     ,	stage_names=self.stage_names	)





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Optional[int]										=      version.parse('''1.11''')


       @property
       def    lowerCAmelCase   ( self	)   ->	Mapping[str, Mapping[int, str]]:



            '''simple docstring'''




            return OrderedDict(
                [
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ]	)




       @property
       def    lowerCAmelCase   ( self	)   ->	float:



            '''simple docstring'''




            return 1E-4






