

"""simple docstring"""


from typing import Mapping

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


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  """umt5"""
				SCREAMING_SNAKE_CASE_       :				str       				=  ["""past_key_values"""]

				def __init__(     self							:    Dict					,  UpperCamelCase__							:    List[str]=2_5_0_1_1_2					,  UpperCamelCase__							:    Optional[int]=5_1_2					,  UpperCamelCase__							:    int=6_4					,  UpperCamelCase__							:    Dict=1_0_2_4					,  UpperCamelCase__							:    Optional[Any]=8					,  UpperCamelCase__							:    int=None					,  UpperCamelCase__							:    Union[str, Any]=6					,  UpperCamelCase__							:    Tuple=3_2					,  UpperCamelCase__							:    Optional[int]=1_2_8					,  UpperCamelCase__							:    Any=0.1					,  UpperCamelCase__							:    List[str]=1e-6					,  UpperCamelCase__							:    Optional[int]=1.0					,  UpperCamelCase__							:    int="gated-gelu"					,  UpperCamelCase__							:    List[Any]=True					,  UpperCamelCase__							:    int=True					,  UpperCamelCase__							:    List[Any]="T5Tokenizer"					,  UpperCamelCase__							:    Tuple=True					,  UpperCamelCase__							:    Optional[int]=0					,  UpperCamelCase__							:    Union[str, Any]=1					,  UpperCamelCase__							:    Any=0					,  **UpperCamelCase__							:    Union[str, Any]					,  )->       Dict:



						'''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__					,  )
						__lowerCAmelCase:	Optional[int]   				=      vocab_size
						__lowerCAmelCase:	Union[str, Any]   				=      d_model
						__lowerCAmelCase:	int   				=      d_kv
						__lowerCAmelCase:	List[Any]   				=      d_ff
						__lowerCAmelCase:	Any   				=      num_layers
						__lowerCAmelCase:	Optional[int]   				=      (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__lowerCAmelCase:	Dict   				=      num_heads
						__lowerCAmelCase:	Dict   				=      relative_attention_num_buckets
						__lowerCAmelCase:	Dict   				=      relative_attention_max_distance
						__lowerCAmelCase:	Dict   				=      dropout_rate
						__lowerCAmelCase:	str   				=      layer_norm_epsilon
						__lowerCAmelCase:	Dict   				=      initializer_factor
						__lowerCAmelCase:	int   				=      feed_forward_proj
						__lowerCAmelCase:	List[str]   				=      use_cache

						__lowerCAmelCase:	Union[str, Any]   				=      self.feed_forward_proj.split("-")
						__lowerCAmelCase:	int   				=      act_info[-1]
						__lowerCAmelCase:	List[Any]   				=      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'")

						if feed_forward_proj == "gated-gelu":
								__lowerCAmelCase:	Union[str, Any]   				=      "gelu_new"

				@property
				def 							lowercase_					(     self							:    List[Any])->       List[str]:



						'''simple docstring'''


						return self.d_model

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



						'''simple docstring'''


						return self.num_heads




				@property
				def 							lowercase_					(     self							:    List[str])->       List[str]:



						'''simple docstring'''


						return self.num_layers





class    snake_case     (    __snake_case  ):

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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      {
						    "input_ids": {0: "batch", 1: "encoder_sequence"},
						    "attention_mask": {0: "batch", 1: "encoder_sequence"},
						}
						if self.use_past:
								__lowerCAmelCase:	Tuple   				=      "past_encoder_sequence + sequence"
								__lowerCAmelCase:	Union[str, Any]   				=      {0: "batch"}
								__lowerCAmelCase:	str   				=      {0: "batch", 1: "past_decoder_sequence + sequence"}
						else:
								__lowerCAmelCase:	str   				=      {0: "batch", 1: "decoder_sequence"}
								__lowerCAmelCase:	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 							lowercase_					(     self							:    Dict)->       int:



						'''simple docstring'''


						return 1_3




				@property
				def 							lowercase_					(     self							:    Any)->       float:



						'''simple docstring'''


						return 5e-4





"""simple docstring"""


import math
import sys






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		if number != int(__SCREAMING_SNAKE_CASE			):
				raise ValueError("the value of input must be a natural number"			)
		if number < 0:
				raise ValueError("the value of input must not be a negative number"			)
		if number == 0:
				return 1
		__lowerCAmelCase:	int   				=      [-1] * (number + 1)
		__lowerCAmelCase:	Optional[Any]   				=      0
		for i in range(1 ,			number + 1			):
				__lowerCAmelCase:	List[str]   				=      sys.maxsize
				__lowerCAmelCase:	int   				=      int(math.sqrt(__SCREAMING_SNAKE_CASE			)			)
				for j in range(1 ,			root + 1			):
						__lowerCAmelCase:	Optional[Any]   				=      1 + answers[i - (j**2)]
						__lowerCAmelCase:	List[Any]   				=      min(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Optional[Any]   				=      answer
		return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


