



'''simple docstring'''
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_UpperCamelCase											=			logging.get_logger(__name__)



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Union[str, Any]						      =			"mt5"
		_SCREAMING_SNAKE_CASE					:    int						      =			["past_key_values"]





		def __init__(     self ,       __UpperCAmelCase=250_112 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=64 ,       __UpperCAmelCase=1_024 ,       __UpperCAmelCase=8 ,       __UpperCAmelCase=None ,       __UpperCAmelCase=6 ,       __UpperCAmelCase=32 ,       __UpperCAmelCase=128 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=1E-6 ,       __UpperCAmelCase=1.0 ,       __UpperCAmelCase="gated-gelu" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=True ,       __UpperCAmelCase="T5Tokenizer" ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0 ,       __UpperCAmelCase=1 ,       __UpperCAmelCase=0 ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(
					    is_encoder_decoder=__UpperCAmelCase ,       tokenizer_class=__UpperCAmelCase ,       tie_word_embeddings=__UpperCAmelCase ,       pad_token_id=__UpperCAmelCase ,       eos_token_id=__UpperCAmelCase ,       decoder_start_token_id=__UpperCAmelCase ,       **__UpperCAmelCase ,       )
					__UpperCAmelCase   :      Tuple										=     vocab_size
					__UpperCAmelCase   :      str										=     d_model
					__UpperCAmelCase   :      Optional[Any]										=     d_kv
					__UpperCAmelCase   :      Dict										=     d_ff
					__UpperCAmelCase   :      Dict										=     num_layers
					__UpperCAmelCase   :      Union[str, Any]										=     (
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					__UpperCAmelCase   :      Optional[Any]										=     num_heads
					__UpperCAmelCase   :      Any										=     relative_attention_num_buckets
					__UpperCAmelCase   :      Optional[int]										=     relative_attention_max_distance
					__UpperCAmelCase   :      List[Any]										=     dropout_rate
					__UpperCAmelCase   :      Dict										=     layer_norm_epsilon
					__UpperCAmelCase   :      str										=     initializer_factor
					__UpperCAmelCase   :      int										=     feed_forward_proj
					__UpperCAmelCase   :      int										=     use_cache

					__UpperCAmelCase   :      List[str]										=     self.feed_forward_proj.split("""-"""				)
					__UpperCAmelCase   :      List[Any]										=     act_info[-1]
					__UpperCAmelCase   :      List[str]										=     act_info[0] == """gated"""

					if len(__UpperCAmelCase				) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase				) > 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":
								__UpperCAmelCase   :      List[Any]										=     """gelu_new"""





		@property
		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					return self.d_model





		@property
		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					return self.num_heads





		@property
		def 							__A       (     self				)       ->     Tuple:

					'''simple docstring'''
					return self.num_layers



class _A  (    __SCREAMING_SNAKE_CASE    ):





		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def 							__A       (     self				)       ->     Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     {
					    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
					    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
					}
					if self.use_past:
								__UpperCAmelCase   :      int										=     """past_encoder_sequence + sequence"""
								__UpperCAmelCase   :      Optional[Any]										=     {0: """batch"""}
								__UpperCAmelCase   :      int										=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
					else:
								__UpperCAmelCase   :      int										=     {0: """batch""", 1: """decoder_sequence"""}
								__UpperCAmelCase   :      Tuple										=     {0: """batch""", 1: """decoder_sequence"""}

					if self.use_past:
								self.fill_with_past_key_values_(__UpperCAmelCase ,       direction="""inputs"""				)

					return common_inputs





		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					return 13





		@property
		def 							__A       (     self				)       ->     float:

					'''simple docstring'''
					return 5E-4









'''simple docstring'''
from itertools import permutations


def   lowercase_	(   lowerCAmelCase__	:  int = 5 ):

			"""simple docstring"""





			if gon_side < 3 or gon_side > 5:
						raise ValueError("""gon_side must be in the range [3, 5]""" )

			# Since it's 16, we know 10 is on the outer ring
			# Put the big numbers at the end so that they are never the first number
			__UpperCAmelCase   :      int										=     list(range(gon_side + 1			,							0			,							-1 ) )
			__UpperCAmelCase   :      int										=     list(range(gon_side + 2			,							gon_side * 2 + 1 ) )

			for perm in permutations(small_numbers + big_numbers ):
						__UpperCAmelCase   :      Optional[int]										=     generate_gon_ring(lowerCAmelCase__			,							list(lowerCAmelCase__ ) )
						if is_magic_gon(lowerCAmelCase__ ):
									return int("""""".join(str(lowerCAmelCase__ ) for n in numbers ) )

			__UpperCAmelCase   :      Optional[int]										=     f'Magic {gon_side}-gon ring is impossible'
			raise ValueError(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     [0] * (gon_side * 3)
			__UpperCAmelCase   :      Optional[Any]										=     perm[0:3]
			perm.append(perm[1] )

			__UpperCAmelCase   :      Union[str, Any]										=     1 if gon_side < 5 else 2

			for i in range(1			,							len(lowerCAmelCase__ ) // 3 + magic_number ):
						__UpperCAmelCase   :      List[str]										=     perm[2 * i + 1]
						__UpperCAmelCase   :      Optional[int]										=     result[3 * i - 1]
						__UpperCAmelCase   :      Optional[int]										=     perm[2 * i + 2]

			return result


def   lowercase_	(   lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			if len(lowerCAmelCase__ ) % 3 != 0:
						raise ValueError("""a gon ring should have a length that is a multiple of 3""" )

			if min(numbers[::3] ) != numbers[0]:
						return False

			__UpperCAmelCase   :      Optional[int]										=     sum(numbers[:3] )

			return all(sum(numbers[i : i + 3] ) == total for i in range(3			,							len(lowerCAmelCase__ )			,							3 ) )


if __name__ == "__main__":
					print(solution())




