



'''simple docstring'''





from typing import Mapping

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


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Tuple		=			'umt5'
 __lowerCamelCase       :			Optional[int]		=			['past_key_values']



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


      """simple docstring"""



      super().__init__(
          is_encoder_decoder=A					,  tokenizer_class=A					,  tie_word_embeddings=A					,  pad_token_id=A					,  eos_token_id=A					,  decoder_start_token_id=A					,  **A					,  )
      _a           =    vocab_size
      _a           =    d_model
      _a           =    d_kv
      _a           =    d_ff
      _a           =    num_layers
      _a           =    (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      _a           =    num_heads
      _a           =    relative_attention_num_buckets
      _a           =    relative_attention_max_distance
      _a           =    dropout_rate
      _a           =    layer_norm_epsilon
      _a           =    initializer_factor
      _a           =    feed_forward_proj
      _a           =    use_cache

      _a           =    self.feed_forward_proj.split('''-'''			)
      _a           =    act_info[-1]
      _a           =    act_info[0] == '''gated'''

      if len(A			) > 1 and act_info[0] != "gated" or len(A			) > 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           =    '''gelu_new'''



 @property
 def    a__    (self			)       ->	Any:


      """simple docstring"""



      return self.d_model



 @property
 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      return self.num_heads



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


      """simple docstring"""



      return self.num_layers






class __A     (    A		):



 '''simple docstring'''







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


      """simple docstring"""



      _a           =    {
          '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
          '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
      }
      if self.use_past:
           _a           =    '''past_encoder_sequence + sequence'''
           _a           =    {0: '''batch'''}
           _a           =    {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
      else:
           _a           =    {0: '''batch''', 1: '''decoder_sequence'''}
           _a           =    {0: '''batch''', 1: '''decoder_sequence'''}

      if self.use_past:
           self.fill_with_past_key_values_(A					,  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 __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    2
     _a           =    []
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__A)
     if n > 1:
          factors.append(__A)
     return factors


if __name__ == "__main__":
  import doctest

  doctest.testmod()



