


def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						if not grid or not grid[0]:
												raise TypeError("The grid does not contain the appropriate information" )

						for cell_n in range(1      ,	len(grid[0] ) ):
												grid[0][cell_n] += grid[0][cell_n - 1]
						__snake_case				=		grid[0]

						for row_n in range(1      ,	len(_lowerCAmelCase ) ):
												__snake_case				=		grid[row_n]
												__snake_case				=		fill_row(_lowerCAmelCase      ,	_lowerCAmelCase )
												__snake_case				=		grid[row_n]

						return grid[-1][-1]





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	list:


						'''simple docstring'''






						current_row[0] += row_above[0]
						for cell_n in range(1      ,	len(_lowerCAmelCase ) ):
												current_row[cell_n] += min(current_row[cell_n - 1]      ,	row_above[cell_n] )

						return current_row


if __name__ == "__main__":
	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





