




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




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=iter(lowerCamelCase   )
				while True:
								__magic_name__	:	Union[str, Any]								=tuple(itertools.islice(lowerCamelCase	,     lowerCamelCase   )   )
								if not chunk:
												return
								yield chunk




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	str								="""""".join([c.upper() for c in dirty if c in string.ascii_letters]   )
				__magic_name__	:	Union[str, Any]								=""""""

				if len(lowerCamelCase   ) < 2:
								return dirty

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

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

				clean += dirty[-1]

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

				return clean




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# I and J are used interchangeably to allow
				# us to use a 5x5 table (25 letters)
				__magic_name__	:	Optional[Any]								="""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
				__magic_name__	:	Optional[Any]								=[]

				# 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(lowerCamelCase   )

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

				return table




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Optional[int]								=generate_table(lowerCamelCase   )
				__magic_name__	:	List[Any]								=prepare_input(lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=""""""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(lowerCamelCase	,     2   ):
								__magic_name__				, __magic_name__	:	Tuple								=divmod(table.index(lowerCamelCase   )	,     5   )
								__magic_name__				, __magic_name__	:	List[str]								=divmod(table.index(lowerCamelCase   )	,     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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	List[Any]								=generate_table(lowerCamelCase   )
				__magic_name__	:	Dict								=""""""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(lowerCamelCase	,     2   ):
								__magic_name__				, __magic_name__	:	Tuple								=divmod(table.index(lowerCamelCase   )	,     5   )
								__magic_name__				, __magic_name__	:	List[str]								=divmod(table.index(lowerCamelCase   )	,     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










from ... import PretrainedConfig


UpperCAmelCase_		:				List[str]									=     {
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
						UpperCamelCase     						=					"""nezha"""

						def __init__(		self   :List[Any]    , __snake_case   :Optional[int]=2_11_28    , __snake_case   :Dict=7_68    , __snake_case   :str=12    , __snake_case   :List[Any]=12    , __snake_case   :Optional[int]=30_72    , __snake_case   :Any="gelu"    , __snake_case   :List[str]=0.1    , __snake_case   :Optional[int]=0.1    , __snake_case   :Dict=5_12    , __snake_case   :Optional[int]=64    , __snake_case   :Any=2    , __snake_case   :List[Any]=0.02    , __snake_case   :List[str]=1E-12    , __snake_case   :Any=0.1    , __snake_case   :str=0    , __snake_case   :int=2    , __snake_case   :str=3    , __snake_case   :Any=True    , **__snake_case   :Any    , ):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case    , bos_token_id=__snake_case    , eos_token_id=__snake_case    , **__snake_case )

										__magic_name__	:	Tuple								=vocab_size
										__magic_name__	:	str								=hidden_size
										__magic_name__	:	Dict								=num_hidden_layers
										__magic_name__	:	str								=num_attention_heads
										__magic_name__	:	int								=hidden_act
										__magic_name__	:	Optional[int]								=intermediate_size
										__magic_name__	:	Union[str, Any]								=hidden_dropout_prob
										__magic_name__	:	Any								=attention_probs_dropout_prob
										__magic_name__	:	Union[str, Any]								=max_position_embeddings
										__magic_name__	:	str								=max_relative_position
										__magic_name__	:	Tuple								=type_vocab_size
										__magic_name__	:	str								=initializer_range
										__magic_name__	:	Tuple								=layer_norm_eps
										__magic_name__	:	Optional[int]								=classifier_dropout
										__magic_name__	:	List[str]								=use_cache




