


def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						assert x is not None
						assert y is not None

						__snake_case				=		len(_lowerCAmelCase )
						__snake_case				=		len(_lowerCAmelCase )

						# declaring the array for storing the dp values
						__snake_case				=		[[0] * (n + 1) for _ in range(m + 1 )]  # noqa: E741

						for i in range(1      ,	m + 1 ):
												for j in range(1      ,	n + 1 ):
																		__snake_case				=		1 if x[i - 1] == y[j - 1] else 0

																		__snake_case				=		max(l[i - 1][j]      ,	l[i][j - 1]      ,	l[i - 1][j - 1] + match )

						__snake_case				=		""
						__snake_case		,					__snake_case				=		m, n
						while i > 0 and j > 0:
												__snake_case				=		1 if x[i - 1] == y[j - 1] else 0

												if l[i][j] == l[i - 1][j - 1] + match:
																		if match == 1:
																								__snake_case				=		x[i - 1] + seq
																		i -= 1
																		j -= 1
												elif l[i][j] == l[i - 1][j]:
																		i -= 1
												else:
																		j -= 1

						return l[m][n], seq


if __name__ == "__main__":
	A							:					Optional[Any]     						=     'AGGTAB'
	A							:					Optional[Any]     						=     'GXTXAYB'
	A							:					str     						=     4
	A							:					Optional[int]     						=     'GTAB'

	A						,  A							:					Optional[int]     						=     longest_common_subsequence(a, b)
	print('len =', ln, ', sub-sequence =', subseq)
	import doctest

	doctest.testmod()









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


A							:					str     						=     logging.get_logger(__name__)

A							:					str     						=     {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

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

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		type_vocab_size
											__snake_case				=		layer_norm_eps
											__snake_case				=		use_tpu_fourier_optimizations
											__snake_case				=		tpu_short_seq_length





