"""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        __snake_case	(					UpperCamelCase__    ):
      a__				=		'''umt5'''
      a__				=		['''past_key_values''']



      def __init__(						self   ,      lowercase=25_01_12   ,      lowercase=5_12   ,      lowercase=64   ,      lowercase=10_24   ,      lowercase=8   ,      lowercase=None   ,      lowercase=6   ,      lowercase=32   ,      lowercase=1_28   ,      lowercase=0.1   ,      lowercase=1e-6   ,      lowercase=1.0   ,      lowercase="gated-gelu"   ,      lowercase=True   ,      lowercase=True   ,      lowercase="T5Tokenizer"   ,      lowercase=True   ,      lowercase=0   ,      lowercase=1   ,      lowercase=0   ,      **lowercase   ,      )     ->      Dict:





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

        a__:					Optional[int]      							=     self.feed_forward_proj.split('-')
        a__:					Optional[Any]      							=     act_info[-1]
        a__:					str      							=     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":
          a__:					Union[str, Any]      							=     'gelu_new'



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





        '''simple docstring'''





        return self.d_model



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





        '''simple docstring'''





        return self.num_heads



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





        '''simple docstring'''





        return self.num_layers







class        __snake_case	(					UpperCamelCase__    ):



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





        '''simple docstring'''





        a__:					str      							=     {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
          a__:					Dict      							=     'past_encoder_sequence + sequence'
          a__:					Any      							=     {0: 'batch'}
          a__:					Tuple      							=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
          a__:					Any      							=     {0: 'batch', 1: 'decoder_sequence'}
          a__:					Dict      							=     {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   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        return 13



      @property
      def   lowerCamelCase_       (						self)     ->      float:





        '''simple docstring'''





        return 5e-4



"""simple docstring"""
from __future__ import annotations




class        __snake_case	:



      def __init__(						self   ,      lowercase=None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     data
        a__:					str      							=     None



      def __repr__(						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        a__:					Union[str, Any]      							=     self
        while temp:
          string_rep.append(f'{temp.data}')
          a__:					Tuple      							=     temp.next
        return "->".join(lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  if not elements_list:
    raise Exception('The Elements List is empty'       )

  a__:					Any      							=     Node(elements_list[0]       )
  for i in range(1		,					len(_SCREAMING_SNAKE_CASE       )       ):
    a__:					Optional[Any]      							=     Node(elements_list[i]       )
    a__:					Tuple      							=     current.next
  return head







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->None:
  if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
    print_reverse(head_node.next       )
    print(head_node.data       )







def 						__a       (     )     ->Optional[Any]:
  from doctest import testmod

  testmod()

  a__:					Tuple      							=     make_linked_list([14, 52, 14, 12, 43]       )
  print('Linked List:'       )
  print(_SCREAMING_SNAKE_CASE       )
  print('Elements in Reverse:'       )
  print_reverse(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()


