



import itertools
import string
from collections.abc import Generator, Iterable

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]	,   _snake_case	:List[Any]				)  ->     Union[str, Any]:
   _A      =       iter(snake_case__				)
   while True:
      _A      =       tuple(itertools.islice(snake_case__	,   snake_case__				)				)
      if not chunk:
         return
      yield chunk

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     Optional[int]:
   _A      =       ''''''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
   _A      =       ''''''

   if len(snake_case__				) < 2:
      return dirty

   for i in range(len(snake_case__				) - 1				):
      clean += dirty[i]

      if dirty[i] == dirty[i + 1]:
         clean += "X"

   clean += dirty[-1]

   if len(snake_case__				) & 1:
      clean += "X"

   return clean

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict				)  ->     Any:
   _A      =       '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
   # we're using a list instead of a '2d' array because it makes the math
   # for setting up the table and doing the actual encoding/decoding simpler
   _A      =       []

   # copy key chars into the table if they are in `alphabet` ignoring duplicates
   for char in key.upper():
      if char not in table and char in alphabet:
         table.append(snake_case__				)

    # fill the rest of the table in with the remaining alphabet chars
   for char in alphabet:
      if char not in table:
         table.append(snake_case__				)

   return table

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int	,   _snake_case	:Any				)  ->     str:
   _A      =       generate_table(snake_case__				)
   _A      =       prepare_input(snake_case__				)
   _A      =       ''''''

   # https://en.wikipedia.org/wiki/Playfair_cipher#Description
   for chara, chara in chunker(snake_case__	,   2				):
      _A      =       divmod(table.index(snake_case__				)	,   5				)
      _A      =       divmod(table.index(snake_case__				)	,   5				)

      if rowa == rowa:
         ciphertext += table[rowa * 5 + (cola + 1) % 5]
         ciphertext += table[rowa * 5 + (cola + 1) % 5]
      elif cola == cola:
         ciphertext += table[((rowa + 1) % 5) * 5 + cola]
         ciphertext += table[((rowa + 1) % 5) * 5 + cola]
      else:  # rectangle
         ciphertext += table[rowa * 5 + cola]
         ciphertext += table[rowa * 5 + cola]

   return ciphertext


def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:Any				)  ->     Optional[Any]:
   _A      =       generate_table(snake_case__				)
   _A      =       ''''''

   # https://en.wikipedia.org/wiki/Playfair_cipher#Description
   for chara, chara in chunker(snake_case__	,   2				):
      _A      =       divmod(table.index(snake_case__				)	,   5				)
      _A      =       divmod(table.index(snake_case__				)	,   5				)

      if rowa == rowa:
         plaintext += table[rowa * 5 + (cola - 1) % 5]
         plaintext += table[rowa * 5 + (cola - 1) % 5]
      elif cola == cola:
         plaintext += table[((rowa - 1) % 5) * 5 + cola]
         plaintext += table[((rowa - 1) % 5) * 5 + cola]
      else:  # rectangle
         plaintext += table[rowa * 5 + cola]
         plaintext += table[rowa * 5 + cola]

   return plaintext





'''simple docstring'''




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


lowercase     :							List[str]       						=     logging.get_logger(__name__)

lowercase     :							Optional[int]       						=     {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''switch_transformers'''
     __magic_name__          =						['''past_key_values''']
     __magic_name__          =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(				self		,     SCREAMING_SNAKE_CASE=32128		,     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=0.001		,     SCREAMING_SNAKE_CASE=0.001		,     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		,     )					->							List[Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		vocab_size
         A			:  Any   =		d_model
         A			:  Dict   =		d_kv
         A			:  Optional[Any]   =		d_ff

         A			:  List[Any]   =		num_sparse_encoder_layers

         A			:  List[Any]   =		num_layers
         A			:  Optional[int]   =		(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A			:  List[str]   =		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:
             A			:  int   =		self.num_layers // self.num_sparse_encoder_layers
         else:
             A			:  int   =		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:
             A			:  str   =		self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
             A			:  Dict   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

         A			:  List[Any]   =		num_heads
         A			:  str   =		num_experts
         A			:  Dict   =		expert_capacity
         A			:  Optional[int]   =		router_bias
         A			:  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}'      )
         A			:  Dict   =		router_dtype

         A			:  int   =		router_ignore_padding_tokens
         A			:  List[Any]   =		relative_attention_num_buckets
         A			:  Dict   =		relative_attention_max_distance

         A			:  Optional[int]   =		dropout_rate
         A			:  Dict   =		layer_norm_epsilon
         A			:  Optional[int]   =		initializer_factor
         A			:  Union[str, Any]   =		feed_forward_proj
         A			:  Any   =		use_cache
         A			:  Tuple   =		add_router_probs

         A			:  List[str]   =		router_z_loss_coef
         A			:  str   =		router_aux_loss_coef

         A			:  Union[str, Any]   =		self.feed_forward_proj.split('''-'''      )
         A			:  Optional[Any]   =		act_info[-1]
         A			:  Union[str, 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":
             A			:  str   =		'''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		,     )




