
import math






def   lowerCamelCase__							(					a      )      ->   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(a      ) + 1      )		,		6      ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True






def   lowerCamelCase__							(					a = 1_00_01      )      ->   int:

   try:
      _A:  List[str]					=       int(a      )
   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.'''      )
   _A:  list[int]					=       []
   _A:  Tuple					=       2
   while len(a      ) < nth:
      if is_prime(a      ):
         primes.append(a      )
         num += 1
      else:
         num += 1
   return primes[len(a      ) - 1]


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





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


UpperCAmelCase__		:     Dict     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''swin2sr'''

   __UpperCamelCase		:    str 				=    {
       '''hidden_size''': '''embed_dim''',
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							int=6_4			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							Dict=3			,			lowerCAmelCase_     :							Dict=1_8_0			,			lowerCAmelCase_     :							Union[str, Any]=[6, 6, 6, 6, 6, 6]			,			lowerCAmelCase_     :							Tuple=[6, 6, 6, 6, 6, 6]			,			lowerCAmelCase_     :							int=8			,			lowerCAmelCase_     :							Any=2.0			,			lowerCAmelCase_     :							Union[str, Any]=True			,			lowerCAmelCase_     :							Dict=0.0			,			lowerCAmelCase_     :							Optional[int]=0.0			,			lowerCAmelCase_     :							int=0.1			,			lowerCAmelCase_     :							Any="gelu"			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							str=0.02			,			lowerCAmelCase_     :							Optional[Any]=1e-5			,			lowerCAmelCase_     :							int=2			,			lowerCAmelCase_     :							int=1.0			,			lowerCAmelCase_     :							Any="1conv"			,			lowerCAmelCase_     :							List[str]="pixelshuffle"			,			**lowerCAmelCase_     :							str			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[str]					=       image_size
      _A:  Any					=       patch_size
      _A:  Any					=       num_channels
      _A:  Union[str, Any]					=       embed_dim
      _A:  int					=       depths
      _A:  List[Any]					=       len(lowerCAmelCase_     )
      _A:  int					=       num_heads
      _A:  Any					=       window_size
      _A:  Optional[int]					=       mlp_ratio
      _A:  int					=       qkv_bias
      _A:  List[Any]					=       hidden_dropout_prob
      _A:  List[str]					=       attention_probs_dropout_prob
      _A:  List[Any]					=       drop_path_rate
      _A:  Any					=       hidden_act
      _A:  List[str]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  str					=       initializer_range
      _A:  int					=       upscale
      _A:  int					=       img_range
      _A:  Optional[Any]					=       resi_connection
      _A:  int					=       upsampler



