

'''simple docstring'''

from typing import Mapping

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


a       :      Optional[Any]  	=			logging.get_logger(__name__)







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Tuple       =							"mt5"
		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["past_key_values"]







		def __init__(			self   ,      snake_case=2_5_0_1_1_2   ,      snake_case=5_1_2   ,      snake_case=6_4   ,      snake_case=1_0_2_4   ,      snake_case=8   ,      snake_case=None   ,      snake_case=6   ,      snake_case=3_2   ,      snake_case=1_2_8   ,      snake_case=0.1   ,      snake_case=1e-6   ,      snake_case=1.0   ,      snake_case="gated-gelu"   ,      snake_case=True   ,      snake_case=True   ,      snake_case="T5Tokenizer"   ,      snake_case=False   ,      snake_case=0   ,      snake_case=1   ,      snake_case=0   ,      **snake_case   ,      ):


							'''simple docstring'''

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

							UpperCAmelCase		:							Union[str, Any]        =       self.feed_forward_proj.split("-"      )
							UpperCAmelCase		:							Optional[Any]        =       act_info[-1]
							UpperCAmelCase		:							str        =       act_info[0] == "gated"

							if len(snake_case      ) > 1 and act_info[0] != "gated" or len(snake_case      ) > 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		:							str        =       "gelu_new"







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return self.d_model







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return self.num_heads







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return self.num_layers







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       {
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
												UpperCAmelCase		:							Union[str, Any]        =       "past_encoder_sequence + sequence"
												UpperCAmelCase		:							Tuple        =       {0: "batch"}
												UpperCAmelCase		:							Tuple        =       {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
												UpperCAmelCase		:							List[str]        =       {0: "batch", 1: "decoder_sequence"}
												UpperCAmelCase		:							int        =       {0: "batch", 1: "decoder_sequence"}

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

							return common_inputs







		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def 				A_	(			self      ):


							'''simple docstring'''

							return 1_3







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return 5e-4



'''simple docstring'''

def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       1.0
					for i in range(1			,   r + 1							):
										ret *= (n + 1 - i) / i
					return round(__magic_name__							)





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					return choose(8 + n			,   __magic_name__							) + choose(9 + n			,   __magic_name__							) - 10





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					return sum(non_bouncy_exact(__magic_name__							) for i in range(1			,   n + 1							)							)





def 				lowercase (					__magic_name__ = 100							):



					'''simple docstring'''







					return non_bouncy_upto(__magic_name__							)


if __name__ == "__main__":
				print(F'{solution() = }')
