




"""simple docstring"""


from __future__ import annotations

from typing import TypedDict






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42







def  UpperCAmelCase__	(snake_case__							: str   ):
  """simple docstring"""

  if not isinstance(snake_case__		,       snake_case__   ):
    raise TypeError("""The parameter s type must be str."""   )

  return [s[i:] + s[:i] for i in range(len(snake_case__   )   )]


def  UpperCAmelCase__	(snake_case__							: str   ):
  """simple docstring"""

  if not isinstance(snake_case__		,       snake_case__   ):
    raise TypeError("""The parameter s type must be str."""   )
  if not s:
    raise ValueError("""The parameter s must not be empty."""   )

  _snake_case							:		Dict								=  all_rotations(snake_case__   )
  rotations.sort()  # sort the list of rotations in alphabetically order
  # make a string composed of the last char of each rotation
  _snake_case							:		BWTTransformDict								=  {
      "bwt_string": "".join([word[-1] for word in rotations]   ),
      "idx_original_string": rotations.index(snake_case__   ),
  }
  return response


def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: int   ):
  """simple docstring"""

  if not isinstance(snake_case__		,       snake_case__   ):
    raise TypeError("""The parameter bwt_string type must be str."""   )
  if not bwt_string:
    raise ValueError("""The parameter bwt_string must not be empty."""   )
  try:
    _snake_case							:		Optional[int]								=  int(snake_case__   )
  except ValueError:
    raise TypeError(
        """The parameter idx_original_string type must be int or passive"""
        """ of cast to int."""   )
  if idx_original_string < 0:
    raise ValueError("""The parameter idx_original_string must not be lower than 0."""   )
  if idx_original_string >= len(snake_case__   ):
    raise ValueError(
        """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""   )

  _snake_case							:		Union[str, Any]								=  [""""""] * len(snake_case__   )
  for _ in range(len(snake_case__   )   ):
    for i in range(len(snake_case__   )   ):
      _snake_case							:		str								=  bwt_string[i] + ordered_rotations[i]
    ordered_rotations.sort()
  return ordered_rotations[idx_original_string]


if __name__ == "__main__":
     A_      =    '''Provide a string that I will generate its BWT transform: '''
     A_      =    input(entry_msg).strip()
     A_      =    bwt_transform(s)
     print(
         F'''Burrows Wheeler transform for string \'{s}\' results '''
         F'''in \'{result['bwt_string']}\''''
     )
     A_      =    reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
     print(
         F'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
         F'''we get original string \'{original_string}\''''
     )








"""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


A_      =    logging.get_logger(__name__)

A_      =    {
    '''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  lowercase(					__a	,      __a			):







 '''simple docstring'''





 lowercase__			 = "swin"

 lowercase__			 = {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }


 def __init__(    self:		Any,				a_:		List[str]=224,				a_:		List[Any]=4,				a_:		List[Any]=3,				a_:		Dict=96,				a_:		List[str]=[2, 2, 6, 2],				a_:		int=[3, 6, 12, 24],				a_:		int=7,				a_:		str=4.0,				a_:		Optional[Any]=True,				a_:		Dict=0.0,				a_:		List[Any]=0.0,				a_:		List[str]=0.1,				a_:		Union[str, Any]="gelu",				a_:		Dict=False,				a_:		Union[str, Any]=0.02,				a_:		Optional[int]=1E-5,				a_:		Optional[int]=32,				a_:		Tuple=None,				a_:		Union[str, Any]=None,				**a_:		Any,				):




   '''simple docstring'''



   super().__init__(**a_		)

   _snake_case							:		Any								=  image_size
   _snake_case							:		List[Any]								=  patch_size
   _snake_case							:		Tuple								=  num_channels
   _snake_case							:		str								=  embed_dim
   _snake_case							:		Union[str, Any]								=  depths
   _snake_case							:		int								=  len(a_		)
   _snake_case							:		Union[str, Any]								=  num_heads
   _snake_case							:		List[str]								=  window_size
   _snake_case							:		str								=  mlp_ratio
   _snake_case							:		Union[str, Any]								=  qkv_bias
   _snake_case							:		Dict								=  hidden_dropout_prob
   _snake_case							:		str								=  attention_probs_dropout_prob
   _snake_case							:		Union[str, Any]								=  drop_path_rate
   _snake_case							:		Optional[int]								=  hidden_act
   _snake_case							:		str								=  use_absolute_embeddings
   _snake_case							:		Tuple								=  layer_norm_eps
   _snake_case							:		List[Any]								=  initializer_range
   _snake_case							:		Optional[Any]								=  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							:		Any								=  int(embed_dim * 2 ** (len(a_		) - 1)		)
   _snake_case							:		Any								=  ["""stem"""] + [f"stage{idx}" for idx in range(1,				len(a_		) + 1		)]
   _snake_case		, _snake_case							:		List[str]								=  get_aligned_output_features_output_indices(
       out_features=a_,				out_indices=a_,				stage_names=self.stage_names		)







class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = version.parse("1.11"			)


 @property
 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



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


 @property
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   return 1E-4


