


'''simple docstring'''

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


_UpperCAmelCase				: List[Any]							    =				logging.get_logger(__name__)

_UpperCAmelCase				: Dict							    =				{
    '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
    '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
    '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
    '''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
    '''funnel-transformer/intermediate''': (
        '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
    ),
    '''funnel-transformer/intermediate-base''': (
        '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
    ),
    '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
    '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
    '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
    '''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}


class       a__				( a__      ):







				"""simple docstring"""



				__UpperCamelCase    :		Dict	=	"""funnel"""
				__UpperCamelCase    :		Any	=	{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """n_head""",
				}




				def __init__(self       ,			__lowercase=3_05_22       ,			__lowercase=[4, 4, 4]       ,			__lowercase=None       ,			__lowercase=2       ,			__lowercase=7_68       ,			__lowercase=12       ,			__lowercase=64       ,			__lowercase=30_72       ,			__lowercase="gelu_new"       ,			__lowercase=0.1       ,			__lowercase=0.1       ,			__lowercase=0.0       ,			__lowercase=0.1       ,			__lowercase=None       ,			__lowercase=1e-9       ,			__lowercase="mean"       ,			__lowercase="relative_shift"       ,			__lowercase=True       ,			__lowercase=True       ,			__lowercase=True       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				vocab_size
										__lowerCAmelCase							       =				block_sizes
										__lowerCAmelCase							       =				[1] * len(lowerCAmelCase__		) if block_repeats is None else block_repeats
										assert len(lowerCAmelCase__		) == len(
										    self.block_repeats		), "`block_sizes` and `block_repeats` should have the same length."
										__lowerCAmelCase							       =				num_decoder_layers
										__lowerCAmelCase							       =				d_model
										__lowerCAmelCase							       =				n_head
										__lowerCAmelCase							       =				d_head
										__lowerCAmelCase							       =				d_inner
										__lowerCAmelCase							       =				hidden_act
										__lowerCAmelCase							       =				hidden_dropout
										__lowerCAmelCase							       =				attention_dropout
										__lowerCAmelCase							       =				activation_dropout
										__lowerCAmelCase							       =				initializer_range
										__lowerCAmelCase							       =				initializer_std
										__lowerCAmelCase							       =				layer_norm_eps
										assert pooling_type in [
										    "mean",
										    "max",
										], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
										__lowerCAmelCase							       =				pooling_type
										assert attention_type in [
										    "relative_shift",
										    "factorized",
										], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
										__lowerCAmelCase							       =				attention_type
										__lowerCAmelCase							       =				separate_cls
										__lowerCAmelCase							       =				truncate_seq
										__lowerCAmelCase							       =				pool_q_only

										super().__init__(**lowerCAmelCase__		)




				@property
				def 					_snake_case  (self		):
										return sum(self.block_sizes		)




				@num_hidden_layers.setter
				def 					_snake_case  (self       ,			__lowercase		):
										raise NotImplementedError(
										    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''		)




				@property
				def 					_snake_case  (self		):
										return len(self.block_sizes		)




				@num_blocks.setter
				def 					_snake_case  (self       ,			__lowercase		):
										raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''		)





'''simple docstring'''

from string import ascii_uppercase

_UpperCAmelCase				: Optional[int]							    =				{char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase				: Optional[int]							    =				dict(enumerate(ascii_uppercase))

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				len(lowerCamelCase)
						__lowerCAmelCase							       =				0
						while True:
												if x == i:
																		__lowerCAmelCase							       =				0
												if len(lowerCamelCase) == len(lowerCamelCase):
																		break
												key += key[i]
												i += 1
						return key

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] - dicta[key_new[i]]) % 2_6
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] + dicta[key_new[i]] + 2_6) % 2_6
																		i += 1
																		or_txt += dicta[x]
						return or_txt

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''THE GERMAN ATTACK'''
						__lowerCAmelCase							       =				'''SECRET'''
						__lowerCAmelCase							       =				generate_key(lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				cipher_text(lowerCamelCase,				lowerCamelCase)
						print(F"""Encrypted Text = {s}""")
						print(F"""Original Text = {original_text(lowerCamelCase,				lowerCamelCase)}""")


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

