


'''simple docstring'''

from typing import Mapping

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


lowerCamelCase				:  Dict    		=      logging.get_logger(__name__)

lowerCamelCase				:  Union[str, Any]    		=      {
    '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		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	List[str]       =				"""umt5"""
      lowerCAmelCase__   :	List[str]       =				["""past_key_values"""]



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

             '''simple docstring'''







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

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

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



      @property
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             return self.d_model



      @property
      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             return self.num_heads






      @property
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             return self.num_layers






class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






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

             '''simple docstring'''







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

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

             return common_inputs



      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             return 13






      @property
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             return 5E-4








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE (A			)			->			float:






       """simple docstring"""







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


if __name__ == "__main__":
 import doctest

 doctest.testmod()




