





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       :   Tuple				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   int				       =							{
    "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     __snake_case       (							__lowerCamelCase   ,			__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """swin"""

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




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

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







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     version.parse("""1.11"""			)




       @property
       def 							UpperCAmelCase__ (  self :    Tuple		):
         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]		)






       @property
       def 							UpperCAmelCase__ (  self :    List[Any]		):
         return 1E-4













import math







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5					,					int(math.sqrt(SCREAMING_SNAKE_CASE__) + 1)					,					6):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 			A__							(    SCREAMING_SNAKE_CASE__ = 1_0001)   ->    int:

  try:
    __snake_case:						List[str]   =						int(SCREAMING_SNAKE_CASE__)
  except (TypeError, ValueError):
    raise TypeError("""Parameter nth must be int or castable to int.""") from None
  if nth <= 0:
    raise ValueError("""Parameter nth must be greater than or equal to one.""")
  __snake_case:						list[int]   =						[]
  __snake_case:						List[str]   =						2
  while len(SCREAMING_SNAKE_CASE__) < nth:
    if is_prime(SCREAMING_SNAKE_CASE__):
      primes.append(SCREAMING_SNAKE_CASE__)
      num += 1
    else:
      num += 1
  return primes[len(SCREAMING_SNAKE_CASE__) - 1]


if __name__ == "__main__":
      print(f'{solution() = }')






