
"""simple docstring"""




from typing import Mapping

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


A__   :					Optional[int]							=   logging.get_logger(__name__)

A__   :					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 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''umt5'''
       UpperCamelCase_   =		['''past_key_values''']
       def __init__(     self ,				A_=25_0112 ,				A_=512 ,				A_=64 ,				A_=1024 ,				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_ ,				)     ->	Union[str, 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_ ,				)
         _lowercase:			int       =     vocab_size
         _lowercase:			int       =     d_model
         _lowercase:			Optional[int]       =     d_kv
         _lowercase:			Any       =     d_ff
         _lowercase:			int       =     num_layers
         _lowercase:			List[str]       =     (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         _lowercase:			List[str]       =     num_heads
         _lowercase:			Optional[Any]       =     relative_attention_num_buckets
         _lowercase:			Union[str, Any]       =     relative_attention_max_distance
         _lowercase:			Optional[Any]       =     dropout_rate
         _lowercase:			int       =     layer_norm_epsilon
         _lowercase:			List[Any]       =     initializer_factor
         _lowercase:			List[Any]       =     feed_forward_proj
         _lowercase:			Dict       =     use_cache

         _lowercase:			str       =     self.feed_forward_proj.split('''-'''       )
         _lowercase:			Union[str, Any]       =     act_info[-1]
         _lowercase:			str       =     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":
           _lowercase:			Optional[int]       =     '''gelu_new'''
       @property
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




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





         """simple docstring"""




         return self.num_heads



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





         """simple docstring"""




         return self.num_layers


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





         """simple docstring"""




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





         """simple docstring"""




         return 13



       @property
       def 							lowercase_							(     self       )     ->	float:





         """simple docstring"""




         return 5E-4


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  if len(_UpperCamelCase						) == 1:
    return True
  _lowercase:			Any       =     series[1] - series[0]
  for index in range(len(_UpperCamelCase						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  _lowercase:			Tuple       =     0
  for val in series:
    answer += val
  return answer / len(_UpperCamelCase						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
