





'''simple docstring'''


from typing import Mapping

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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "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_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       "umt5"
 __lowerCamelCase    :				List[Any]								=       ["past_key_values"]




 def __init__(						self      ,  _lowerCAmelCase=250112      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=64      ,  _lowerCAmelCase=1024      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=6      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=128      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=1E-6      ,  _lowerCAmelCase=1.0      ,  _lowerCAmelCase="gated-gelu"      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase="T5Tokenizer"      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(
         is_encoder_decoder=_lowerCAmelCase      ,  tokenizer_class=_lowerCAmelCase      ,  tie_word_embeddings=_lowerCAmelCase      ,  pad_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  decoder_start_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   d_model
     _lowerCAmelCase						      =   d_kv
     _lowerCAmelCase						      =   d_ff
     _lowerCAmelCase						      =   num_layers
     _lowerCAmelCase						      =   (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   relative_attention_num_buckets
     _lowerCAmelCase						      =   relative_attention_max_distance
     _lowerCAmelCase						      =   dropout_rate
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_factor
     _lowerCAmelCase						      =   feed_forward_proj
     _lowerCAmelCase						      =   use_cache

     _lowerCAmelCase						      =   self.feed_forward_proj.split("-"		)
     _lowerCAmelCase						      =   act_info[-1]
     _lowerCAmelCase						      =   act_info[0] == "gated"

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




 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return self.d_model




 @property
 def 				_snake_case (						self		)     ->				Tuple:
     return self.num_heads





 @property
 def 				_snake_case (						self		)     ->				str:
     return self.num_layers







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase						      =   {
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
         _lowerCAmelCase						      =   "past_encoder_sequence + sequence"
         _lowerCAmelCase						      =   {0: "batch"}
         _lowerCAmelCase						      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
         _lowerCAmelCase						      =   {0: "batch", 1: "decoder_sequence"}
         _lowerCAmelCase						      =   {0: "batch", 1: "decoder_sequence"}

     if self.use_past:
         self.fill_with_past_key_values_(_lowerCAmelCase      ,  direction="inputs"		)

     return common_inputs




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





 @property
 def 				_snake_case (						self		)     ->				float:
     return 5E-4







'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


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




def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


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


if __name__ == "__main__":
 import doctest

 doctest.testmod()




