





from typing import Mapping

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


_UpperCAmelCase       =				logging.get_logger(__name__)

_UpperCAmelCase       =				{
    '''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_							(						lowerCamelCase__		):
  A_     				=       'umt5'
  A_     				=       ['past_key_values']






  def __init__(				self					:	int  ,    _snake_case					:	Optional[Any]=250112  ,    _snake_case					:	Optional[int]=512  ,    _snake_case					:	Any=64  ,    _snake_case					:	Union[str, Any]=1024  ,    _snake_case					:	Tuple=8  ,    _snake_case					:	Optional[int]=None  ,    _snake_case					:	Union[str, Any]=6  ,    _snake_case					:	List[Any]=32  ,    _snake_case					:	Dict=128  ,    _snake_case					:	List[str]=0.1  ,    _snake_case					:	List[Any]=1E-6  ,    _snake_case					:	Dict=1.0  ,    _snake_case					:	Union[str, Any]="gated-gelu"  ,    _snake_case					:	Union[str, Any]=True  ,    _snake_case					:	Any=True  ,    _snake_case					:	List[str]="T5Tokenizer"  ,    _snake_case					:	Union[str, Any]=True  ,    _snake_case					:	Union[str, Any]=0  ,    _snake_case					:	List[Any]=1  ,    _snake_case					:	List[Any]=0  ,    **_snake_case					:	Any  ,    )->Optional[int]:






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

   __lowerCAmelCase	:			int         =  self.feed_forward_proj.split("""-"""     )
   __lowerCAmelCase	:			Any         =  act_info[-1]
   __lowerCAmelCase	:			Tuple         =  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'"""     )

   if feed_forward_proj == "gated-gelu":
    __lowerCAmelCase	:			Optional[Any]         =  "gelu_new"






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






   '''simple docstring'''

   return self.d_model






  @property
  def  UpperCAmelCase__		(				self					:	str     )->Any:






   '''simple docstring'''

   return self.num_heads






  @property
  def  UpperCAmelCase__		(				self					:	Tuple     )->str:






   '''simple docstring'''

   return self.num_layers






class   snake_case_							(						lowerCamelCase__		):






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






   '''simple docstring'''

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






   '''simple docstring'''

   return 13






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






   '''simple docstring'''

   return 5E-4






def   __snake_case    (       _lowerCAmelCase			:			int = 1					,					_lowerCAmelCase			:			int = 1000						)			->    int:
   A_       :					Optional[int]        =      1
   A_       :					int        =      0

   for divide_by_number in range(_lowerCAmelCase					,					digit + 1						):
      A_       :					list[int]        =      []
      A_       :					Union[str, Any]        =      numerator
      for _ in range(1					,					digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(_lowerCAmelCase						):
               A_       :					Optional[Any]        =      len(_lowerCAmelCase						)
               A_       :					Union[str, Any]        =      divide_by_number
         else:
            has_been_divided.append(_lowerCAmelCase						)
            A_       :					Dict        =      now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()
