
from typing import Mapping

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


_A							=       logging.get_logger(__name__)

_A							=       {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Tuple    =     """longt5"""
 A__    :					List[str]    =     ["""past_key_values"""]
 A__    :					Optional[int]    =     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

 def __init__(   self  ,						__UpperCamelCase=3_2_1_2_8  ,						__UpperCamelCase=5_1_2  ,						__UpperCamelCase=6_4  ,						__UpperCamelCase=2_0_4_8  ,						__UpperCamelCase=6  ,						__UpperCamelCase=None  ,						__UpperCamelCase=8  ,						__UpperCamelCase=1_2_7  ,						__UpperCamelCase=1_6  ,						__UpperCamelCase=3_2  ,						__UpperCamelCase=1_2_8  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=1e-6  ,						__UpperCamelCase=1.0  ,						__UpperCamelCase="relu"  ,						__UpperCamelCase=True  ,						__UpperCamelCase="local"  ,						__UpperCamelCase=True  ,						__UpperCamelCase=0  ,						__UpperCamelCase=1  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							vocab_size
        UpperCamelCase_											=							d_model
        UpperCamelCase_											=							d_kv
        UpperCamelCase_											=							d_ff
        UpperCamelCase_											=							num_layers
        # default = symmetry
        UpperCamelCase_											=							num_decoder_layers if num_decoder_layers is not None else self.num_layers
        UpperCamelCase_											=							num_heads
        UpperCamelCase_											=							local_radius
        UpperCamelCase_											=							global_block_size
        UpperCamelCase_											=							relative_attention_num_buckets
        UpperCamelCase_											=							relative_attention_max_distance
        UpperCamelCase_											=							dropout_rate
        UpperCamelCase_											=							layer_norm_epsilon
        UpperCamelCase_											=							initializer_factor
        UpperCamelCase_											=							feed_forward_proj
        UpperCamelCase_											=							encoder_attention_type
        UpperCamelCase_											=							use_cache

        UpperCamelCase_											=							self.feed_forward_proj.split("""-"""  )
        UpperCamelCase_											=							act_info[-1]
        UpperCamelCase_											=							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'"""  )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
               UpperCamelCase_											=							"""gelu_new"""

        super().__init__(
            pad_token_id=__UpperCamelCase  ,						eos_token_id=__UpperCamelCase  ,						is_encoder_decoder=__UpperCamelCase  ,						**__UpperCamelCase  ,						)



class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

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

        return common_inputs

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return 1_3





_A							=       [chr(i) for i in range(32, 126)]
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(reversed(range(len(alphabets))))
_A							=       []
_A							=       _A							=       _A							=       0




def 					lowerCamelCase__					(    )		->						List[str]:
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       UpperCamelCase_											=							gear_one[0]
       gear_one.append(a__  )
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(a__  )  ) == 0:
              UpperCamelCase_											=							gear_two[0]
              gear_two.append(a__  )
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(a__  )  ) == 0:
                     UpperCamelCase_											=							gear_three[0]
                     gear_three.append(a__  )
                     del gear_three[0]
                     gear_three_pos += 1




def 					lowerCamelCase__					(    a__	:	Optional[int]  )		->						Any:
       UpperCamelCase_											=							alphabets.index(a__  )
       UpperCamelCase_											=							gear_one[target]
       UpperCamelCase_											=							gear_two[target]
       UpperCamelCase_											=							gear_three[target]
       UpperCamelCase_											=							reflector[target]
       UpperCamelCase_											=							gear_three.index(a__  )
       UpperCamelCase_											=							gear_two.index(a__  )
       UpperCamelCase_											=							gear_one.index(a__  )
       code.append(alphabets[target]  )
       rotator()


if __name__ == "__main__":
      _A							=       list(input('''Type your message:\n'''))
      while True:
            try:
                  _A							=       int(input('''Please set token:(must be only digits)\n'''))
                  break
            except Exception as error:
                  print(error)
      for _ in range(token):
            rotator()
      for j in decode:
            engine(j)
      print('''\n''' + ''''''.join(code))
      print(
          F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
          '''this message again you should input same digits as token!'''
      )



