





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			int     =    "switch_transformers"
	a      :			Dict     =    ["past_key_values"]
	a      :			Optional[Any]     =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


	def __init__(self       ,_lowerCamelCase=32128       ,_lowerCamelCase=768       ,_lowerCamelCase=64       ,_lowerCamelCase=2048       ,_lowerCamelCase=64       ,_lowerCamelCase=12       ,_lowerCamelCase=3       ,_lowerCamelCase=12       ,_lowerCamelCase=3       ,_lowerCamelCase=12       ,_lowerCamelCase=8       ,_lowerCamelCase=False       ,_lowerCamelCase=0.0_1       ,_lowerCamelCase="float32"       ,_lowerCamelCase=False       ,_lowerCamelCase=32       ,_lowerCamelCase=128       ,_lowerCamelCase=0.1       ,_lowerCamelCase=1E-6       ,_lowerCamelCase=0.0_0_1       ,_lowerCamelCase=0.0_0_1       ,_lowerCamelCase=1.0       ,_lowerCamelCase="relu"       ,_lowerCamelCase=True       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=0       ,_lowerCamelCase=1       ,**_lowerCamelCase       ,)							->							str:







				'''simple docstring'''





				__lowercase			  =							vocab_size
				__lowercase			  =							d_model
				__lowercase			  =							d_kv
				__lowercase			  =							d_ff

				__lowercase			  =							num_sparse_encoder_layers

				__lowercase			  =							num_layers
				__lowercase			  =							(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				__lowercase			  =							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:
							__lowercase			  =							self.num_layers // self.num_sparse_encoder_layers
				else:
							__lowercase			  =							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:
							__lowercase			  =							self.num_decoder_layers // self.num_sparse_decoder_layers
				else:
							__lowercase			  =							self.num_decoder_layers  # HACK: this will create 0 sparse layers

				__lowercase			  =							num_heads
				__lowercase			  =							num_experts
				__lowercase			  =							expert_capacity
				__lowercase			  =							router_bias
				__lowercase			  =							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}"	)
				__lowercase			  =							router_dtype

				__lowercase			  =							router_ignore_padding_tokens
				__lowercase			  =							relative_attention_num_buckets
				__lowercase			  =							relative_attention_max_distance

				__lowercase			  =							dropout_rate
				__lowercase			  =							layer_norm_epsilon
				__lowercase			  =							initializer_factor
				__lowercase			  =							feed_forward_proj
				__lowercase			  =							use_cache
				__lowercase			  =							add_router_probs

				__lowercase			  =							router_z_loss_coef
				__lowercase			  =							router_aux_loss_coef

				__lowercase			  =							self.feed_forward_proj.split('''-'''	)
				__lowercase			  =							act_info[-1]
				__lowercase			  =							act_info[0] == '''gated'''

				if len(_lowerCamelCase	) > 1 and act_info[0] != "gated" or len(_lowerCamelCase	) > 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":
							__lowercase			  =							'''gelu_new'''

				super().__init__(
				    pad_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,is_encoder_decoder=_lowerCamelCase       ,**_lowerCamelCase       ,)












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			__lowercase			  =							len(lowerCamelCase_			) + 1
			__lowercase			  =							len(lowerCamelCase_			) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			__lowercase			  =							[[0 for i in range(lowerCamelCase_			)] for j in range(lowerCamelCase_			)]

			# since string of zero length match pattern of zero length
			__lowercase			  =							1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1						,    lowerCamelCase_			):
						__lowercase			  =							0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1						,    lowerCamelCase_			):
						__lowercase			  =							dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1						,    lowerCamelCase_			):
						for j in range(1						,    lowerCamelCase_			):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												__lowercase			  =							dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															__lowercase			  =							1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															__lowercase			  =							dp[i - 1][j]
												else:
															__lowercase			  =							0
									else:
												__lowercase			  =							0

			return bool(dp[-1][-1]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# inputing the strings
	# input_string = input("input a string :")
	# pattern = input("input a pattern :")

	_SCREAMING_SNAKE_CASE   =      '''aab'''
	_SCREAMING_SNAKE_CASE   =      '''c*a*b'''

	# using function to check whether given string matches the given pattern
	if match_pattern(input_string, pattern):
		print(f'''{input_string} matches the given pattern {pattern}''')
	else:
		print(f'''{input_string} does not match with the given pattern {pattern}''')





