


'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'		,	[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]		,	)
def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   _a					:  List[Any]						=  tmp_path_factory.mktemp('dset_infos_dir'      )
   if "full:README.md" in files:
      with open(dataset_infos_dir / 'README.md'		,	'w'      ) as f:
         f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
   if "empty:README.md" in files:
      with open(dataset_infos_dir / 'README.md'		,	'w'      ) as f:
         f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
   if "dataset_infos.json" in files:
      with open(dataset_infos_dir / 'dataset_infos.json'		,	'w'      ) as f:
         f.write('{"default": {"dataset_size": 42}}'      )
   _a					:  Optional[int]						=  DatasetInfosDict.from_directory(_lowerCamelCase      )
   assert dataset_infos
   assert dataset_infos["default"].dataset_size == 4_2


@pytest.mark.parametrize(
    'dataset_info'		,	[
        DatasetInfo(),
        DatasetInfo(
            description='foo'		,	features=Features({'a': Value('int32'      )}      )		,	builder_name='builder'		,	config_name='config'		,	version='1.0.0'		,	splits=[{'name': 'train'}]		,	download_size=4_2		,	),
    ]		,	)
def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   _a					:  Dict						=  str(_lowerCamelCase      )
   dataset_info.write_to_directory(_lowerCamelCase      )
   _a					:  Dict						=  DatasetInfo.from_directory(_lowerCamelCase      )
   assert dataset_info == reloaded
   assert os.path.exists(os.path.join(_lowerCamelCase		,	'dataset_info.json'      )      )


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





   _a					:  int						=  DatasetInfo(
       description='foo'		,	citation='bar'		,	homepage='https://foo.bar'		,	license='CC0'		,	features=Features({'a': Value('int32'      )}      )		,	post_processed={}		,	supervised_keys=()		,	task_templates=[]		,	builder_name='builder'		,	config_name='config'		,	version='1.0.0'		,	splits=[{'name': 'train', 'num_examples': 4_2}]		,	download_checksums={}		,	download_size=1_3_3_7		,	post_processing_size=4_4_2		,	dataset_size=1_2_3_4		,	size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4		,	)
   _a					:  Tuple						=  dataset_info._to_yaml_dict()
   assert sorted(_lowerCamelCase      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      assert key in dataset_info_yaml_dict
      assert isinstance(dataset_info_yaml_dict[key]		,	(list, dict, int, str)      )
   _a					:  Union[str, Any]						=  yaml.safe_dump(_lowerCamelCase      )
   _a					:  Optional[int]						=  yaml.safe_load(_lowerCamelCase      )
   assert dataset_info_yaml_dict == reloaded


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





   _a					:  str						=  DatasetInfo()
   _a					:  int						=  dataset_info._to_yaml_dict()
   assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    'dataset_infos_dict'		,	[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'		,	features=Features({'a': Value('int32'      )}      )		,	builder_name='builder'		,	config_name='config'		,	version='1.0.0'		,	splits=[{'name': 'train'}]		,	download_size=4_2		,	)
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=4_2      ),
                'v2': DatasetInfo(dataset_size=1_3_3_7      ),
            }      ),
    ]		,	)
def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   _a					:  int						=  str(_lowerCamelCase      )
   dataset_infos_dict.write_to_directory(_lowerCamelCase      )
   _a					:  Union[str, Any]						=  DatasetInfosDict.from_directory(_lowerCamelCase      )

   # the config_name of the dataset_infos_dict take over the attribute
   for config_name, dataset_info in dataset_infos_dict.items():
      _a					:  int						=  config_name
      # the yaml representation doesn't include fields like description or citation
      # so we just test that we can recover what we can from the yaml
      _a					:  Dict						=  DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
   assert dataset_infos_dict == reloaded

   if dataset_infos_dict:
      assert os.path.exists(os.path.join(_lowerCamelCase		,	'README.md'      )      )








'''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






