


'''simple docstring'''







from typing import Mapping

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


A				:					List[str]    						=			logging.get_logger(__name__)

A				:					List[str]    						=			{
    '''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 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "umt5"
    a													=   ["past_key_values"]


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]=250112				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=512				,							SCREAMING_SNAKE_CASE      :							Dict=64				,							SCREAMING_SNAKE_CASE      :							Dict=1024				,							SCREAMING_SNAKE_CASE      :							int=8				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE      :							Tuple=6				,							SCREAMING_SNAKE_CASE      :							Optional[int]=32				,							SCREAMING_SNAKE_CASE      :							Tuple=128				,							SCREAMING_SNAKE_CASE      :							List[Any]=0.1				,							SCREAMING_SNAKE_CASE      :							List[str]=1e-6				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=1.0				,							SCREAMING_SNAKE_CASE      :							Optional[Any]="gated-gelu"				,							SCREAMING_SNAKE_CASE      :							Dict=True				,							SCREAMING_SNAKE_CASE      :							Optional[int]=True				,							SCREAMING_SNAKE_CASE      :							str="T5Tokenizer"				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=True				,							SCREAMING_SNAKE_CASE      :							str=0				,							SCREAMING_SNAKE_CASE      :							int=1				,							SCREAMING_SNAKE_CASE      :							int=0				,							**SCREAMING_SNAKE_CASE      :							str				,							):
           super().__init__(
               is_encoder_decoder=SCREAMING_SNAKE_CASE				,							tokenizer_class=SCREAMING_SNAKE_CASE				,							tie_word_embeddings=SCREAMING_SNAKE_CASE				,							pad_token_id=SCREAMING_SNAKE_CASE				,							eos_token_id=SCREAMING_SNAKE_CASE				,							decoder_start_token_id=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE				,							)
           _A					:     List[Any]     = vocab_size
           _A					:     Any     = d_model
           _A					:     Optional[int]     = d_kv
           _A					:     str     = d_ff
           _A					:     List[str]     = num_layers
           _A					:     int     = (
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           _A					:     List[str]     = num_heads
           _A					:     Optional[Any]     = relative_attention_num_buckets
           _A					:     Optional[Any]     = relative_attention_max_distance
           _A					:     str     = dropout_rate
           _A					:     Any     = layer_norm_epsilon
           _A					:     int     = initializer_factor
           _A					:     Optional[Any]     = feed_forward_proj
           _A					:     str     = use_cache

           _A					:     Dict     = self.feed_forward_proj.split('-')
           _A					:     str     = act_info[-1]
           _A					:     Tuple     = act_info[0] == 'gated'

           if len(SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(SCREAMING_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\'')

           if feed_forward_proj == "gated-gelu":
                  _A					:     Tuple     = 'gelu_new'


    @property
    def        A   (  self      :							str):
           return self.d_model


    @property
    def        A   (  self      :							Tuple):
           return self.num_heads


    @property
    def        A   (  self      :							int):
           return self.num_layers




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def        A   (  self      :							Tuple):
           _A					:     Optional[int]     = {
               'input_ids': {0: 'batch', 1: 'encoder_sequence'},
               'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
           }
           if self.use_past:
                  _A					:     Union[str, Any]     = 'past_encoder_sequence + sequence'
                  _A					:     List[str]     = {0: 'batch'}
                  _A					:     Any     = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
                  _A					:     Dict     = {0: 'batch', 1: 'decoder_sequence'}
                  _A					:     List[str]     = {0: 'batch', 1: 'decoder_sequence'}

           if self.use_past:
                  self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE				,							direction='inputs')

           return common_inputs


    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def        A   (  self      :							int):
           return 13


    @property
    def        A   (  self      :							Any):
           return 5e-4










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int = 10       ):
       if not isinstance(lowerCamelCase  ,lowerCamelCase       ) or n < 0:
              raise ValueError('Invalid input'       )
       _A					:     Optional[Any]     = 10**n
       _A					:     List[str]     = 28433 * (pow(2  ,7830457  ,lowerCamelCase       )) + 1
       return str(number % modulus       )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print(f"""{solution(10) = }""")






