


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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  "swinv2"

    UpperCAmelCase__						:		Dict      =  {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }






    def __init__(			self					,   A_=224					,   A_=4					,   A_=3					,   A_=96					,   A_=[2, 2, 6, 2]					,   A_=[3, 6, 12, 24]					,   A_=7					,   A_=4.0					,   A_=True					,   A_=0.0					,   A_=0.0					,   A_=0.1					,   A_="gelu"					,   A_=False					,   A_=0.02					,   A_=1E-5					,   A_=32					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=embed_dim
         __UpperCamelCase    							=depths
         __UpperCamelCase    							=len(A_    )
         __UpperCamelCase    							=num_heads
         __UpperCamelCase    							=window_size
         __UpperCamelCase    							=mlp_ratio
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=drop_path_rate
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=use_absolute_embeddings
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=encoder_stride
         # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         __UpperCamelCase    							=int(embed_dim * 2 ** (len(A_    ) - 1)    )
         __UpperCamelCase    							=(0, 0, 0, 0)










from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = "arrow",			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[int]:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			keep_in_memory=SCREAMING_SNAKE_CASE_,			streaming=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  List[str]							=	load_from_cache_file
          UpperCamelCase    :  List[str]							=	file_format
          UpperCamelCase    :  Optional[int]							=	Spark(
              df=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			working_dir=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)






       def 					snake_case_       (			self       )     ->		int:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split       )
          UpperCamelCase    :  Union[str, Any]							=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,			file_format=self._file_format,			)
          return self.builder.as_dataset(split=self.split       )



