


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''switch_transformers'''
   __UpperCAmelCase							       =					['''past_key_values''']
   __UpperCAmelCase							       =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=3_2128      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=64      ,   SCREAMING_SNAKE_CASE=2048      ,   SCREAMING_SNAKE_CASE=64      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=8      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=0.01      ,   SCREAMING_SNAKE_CASE="float32"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=128      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=1e-6      ,   SCREAMING_SNAKE_CASE=0.0_01      ,   SCREAMING_SNAKE_CASE=0.0_01      ,   SCREAMING_SNAKE_CASE=1.0      ,   SCREAMING_SNAKE_CASE="relu"      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=1      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Any:
      _lowerCamelCase	:					Optional[int]   =		vocab_size
      _lowerCamelCase	:					str   =		d_model
      _lowerCamelCase	:					Tuple   =		d_kv
      _lowerCamelCase	:					Any   =		d_ff

      _lowerCamelCase	:					str   =		num_sparse_encoder_layers

      _lowerCamelCase	:					Tuple   =		num_layers
      _lowerCamelCase	:					List[Any]   =		(
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      _lowerCamelCase	:					Optional[int]   =		num_sparse_decoder_layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_encoder_layers > 0:
         _lowerCamelCase	:					Union[str, Any]   =		self.num_layers // self.num_sparse_encoder_layers
      else:
         _lowerCamelCase	:					Union[str, Any]   =		self.num_layers  # HACK: this will create 0 sparse layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_decoder_layers > 0:
         _lowerCamelCase	:					str   =		self.num_decoder_layers // self.num_sparse_decoder_layers
      else:
         _lowerCamelCase	:					Tuple   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

      _lowerCamelCase	:					Dict   =		num_heads
      _lowerCamelCase	:					List[Any]   =		num_experts
      _lowerCamelCase	:					Dict   =		expert_capacity
      _lowerCamelCase	:					Tuple   =		router_bias
      _lowerCamelCase	:					Any   =		router_jitter_noise
      if router_dtype not in ["float32", "float16", "bfloat16"]:
         raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
      _lowerCamelCase	:					List[Any]   =		router_dtype

      _lowerCamelCase	:					List[Any]   =		router_ignore_padding_tokens
      _lowerCamelCase	:					Optional[int]   =		relative_attention_num_buckets
      _lowerCamelCase	:					List[str]   =		relative_attention_max_distance

      _lowerCamelCase	:					Tuple   =		dropout_rate
      _lowerCamelCase	:					List[Any]   =		layer_norm_epsilon
      _lowerCamelCase	:					Dict   =		initializer_factor
      _lowerCamelCase	:					List[Any]   =		feed_forward_proj
      _lowerCamelCase	:					Optional[Any]   =		use_cache
      _lowerCamelCase	:					str   =		add_router_probs

      _lowerCamelCase	:					Optional[Any]   =		router_z_loss_coef
      _lowerCamelCase	:					int   =		router_aux_loss_coef

      _lowerCamelCase	:					Union[str, Any]   =		self.feed_forward_proj.split("""-""")
      _lowerCamelCase	:					Any   =		act_info[-1]
      _lowerCamelCase	:					Any   =		act_info[0] == """gated"""

      if len(SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE) > 2:
         raise ValueError(
             F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
             """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
             """'gated-gelu' or 'relu'""")

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
         _lowerCamelCase	:					Dict   =		"""gelu_new"""

      super().__init__(
          pad_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE      ,   is_encoder_decoder=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   )





"""simple docstring"""
from collections import defaultdict

def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		first_str.lower().strip()
   _lowerCamelCase	:					int   =		second_str.lower().strip()

   # Remove whitespace
   _lowerCamelCase	:					Any   =		first_str.replace(""" """						, """"""   )
   _lowerCamelCase	:					List[str]   =		second_str.replace(""" """						, """"""   )

   # Strings of different lengths are not anagrams
   if len(__snake_case   ) != len(__snake_case   ):
      return False

   # Default values for count should be 0
   _lowerCamelCase	:					defaultdict[str, int]   =		defaultdict(__snake_case   )

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(__snake_case   )   ):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()   )


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase				=						input("""Enter the first string """).strip()
 UpperCAmelCase				=						input("""Enter the second string """).strip()

 UpperCAmelCase				=						check_anagrams(input_a, input_b)
 print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')

