
"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_         =       logging.get_logger(__name__)

lowercase_         =       {
    "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
    "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
    "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
    "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
    "funnel-transformer/intermediate": (
        "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"
    ),
    "funnel-transformer/intermediate-base": (
        "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"
    ),
    "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
    "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
    "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
    "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}



class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  Union[str, Any]					 =     """funnel"""
 UpperCamelCase_    :  Optional[Any]					 =     {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """n_head""",
 }




 def __init__(			self							:			int  , a_							:			Any=3_05_22  , a_							:			Any=[4, 4, 4]  , a_							:			int=None  , a_							:			Any=2  , a_							:			List[str]=7_68  , a_							:			List[Any]=12  , a_							:			int=64  , a_							:			Optional[Any]=30_72  , a_							:			Tuple="gelu_new"  , a_							:			List[Any]=0.1  , a_							:			int=0.1  , a_							:			Any=0.0  , a_							:			Optional[int]=0.1  , a_							:			Dict=None  , a_							:			Optional[int]=1E-9  , a_							:			str="mean"  , a_							:			Tuple="relative_shift"  , a_							:			str=True  , a_							:			Any=True  , a_							:			Optional[int]=True  , **a_							:			Dict  , )->       Optional[int]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	vocab_size
      UpperCAmelCase_    :     int            =	block_sizes
      UpperCAmelCase_    :     Optional[Any]            =	[1] * len(a_			) if block_repeats is None else block_repeats
      assert len(a_			) == len(
          self.block_repeats			), "`block_sizes` and `block_repeats` should have the same length."
      UpperCAmelCase_    :     int            =	num_decoder_layers
      UpperCAmelCase_    :     Optional[int]            =	d_model
      UpperCAmelCase_    :     Dict            =	n_head
      UpperCAmelCase_    :     List[Any]            =	d_head
      UpperCAmelCase_    :     Union[str, Any]            =	d_inner
      UpperCAmelCase_    :     Dict            =	hidden_act
      UpperCAmelCase_    :     List[Any]            =	hidden_dropout
      UpperCAmelCase_    :     Optional[int]            =	attention_dropout
      UpperCAmelCase_    :     Optional[int]            =	activation_dropout
      UpperCAmelCase_    :     Dict            =	initializer_range
      UpperCAmelCase_    :     Dict            =	initializer_std
      UpperCAmelCase_    :     Optional[int]            =	layer_norm_eps
      assert pooling_type in [
          "mean",
          "max",
      ], f'''Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'''
      UpperCAmelCase_    :     Any            =	pooling_type
      assert attention_type in [
          "relative_shift",
          "factorized",
      ], f'''Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'''
      UpperCAmelCase_    :     Any            =	attention_type
      UpperCAmelCase_    :     Tuple            =	separate_cls
      UpperCAmelCase_    :     Any            =	truncate_seq
      UpperCAmelCase_    :     Any            =	pool_q_only

      super().__init__(**a_			)




 @property
 def 	a   (			self							:			int			)->       str:



      """simple docstring"""

      return sum(self.block_sizes			)




 @num_hidden_layers.setter
 def 	a   (			self							:			Dict  , a_							:			Dict			)->       Any:



      """simple docstring"""

      raise NotImplementedError(
          """This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."""			)




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



      """simple docstring"""

      return len(self.block_sizes			)






 @num_blocks.setter
 def 	a   (			self							:			Optional[Any]  , a_							:			Optional[int]			)->       str:



      """simple docstring"""

      raise NotImplementedError("""This model does not support the setting of `num_blocks`. Please set `block_sizes`."""			)



"""simple docstring"""







def       A_      (       lowercase   )  ->				str:


     """simple docstring"""







     UpperCAmelCase_    :     Any            =	0

     # if input_string is "aba" than new_input_string become "a|b|a"
     UpperCAmelCase_    :     Union[str, Any]            =	""""""
     UpperCAmelCase_    :     List[Any]            =	""""""

     # append each character + "|" in new_string for range(0, length-1)
     for i in input_string[: len(lowercase   ) - 1]:
          new_input_string += i + "|"
     # append last character
     new_input_string += input_string[-1]

     # we will store the starting and ending of previous furthest ending palindromic
     # substring
     UpperCAmelCase_       ,UpperCAmelCase_    :     Dict            =	0, 0

     # length[i] shows the length of palindromic substring with center i
     UpperCAmelCase_    :     Dict            =	[1 for i in range(len(lowercase   )   )]

     # for each character in new_string find corresponding palindromic string
     UpperCAmelCase_    :     Optional[Any]            =	0
     for j in range(len(lowercase   )   ):
          UpperCAmelCase_    :     str            =	1 if j > r else min(length[l + r - j] // 2    ,			r - j + 1   )
          while (
              j - k >= 0
              and j + k < len(lowercase   )
              and new_input_string[k + j] == new_input_string[j - k]
          ):
               k += 1

          UpperCAmelCase_    :     Union[str, Any]            =	2 * k - 1

          # does this string is ending after the previously explored end (that is r) ?
          # if yes the update the new r to the last index of this
          if j + k - 1 > r:
               UpperCAmelCase_    :     List[Any]            =	j - k + 1  # noqa: E741
               UpperCAmelCase_    :     Union[str, Any]            =	j + k - 1

          # update max_length and start position
          if max_length < length[j]:
               UpperCAmelCase_    :     Optional[Any]            =	length[j]
               UpperCAmelCase_    :     int            =	j

    # create that string
     UpperCAmelCase_    :     List[str]            =	new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
     for i in s:
          if i != "|":
               output_string += i

     return output_string


if __name__ == "__main__":
       import doctest

       doctest.testmod()

