

import warnings

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


__A			:					Union[str, Any]						=			logging.get_logger(__name__)

__A			:					int						=			{
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "xlnet"
   lowerCamelCase__				=      ["mems"]
   lowerCamelCase__				=      {
       "n_token": "vocab_size",  # Backward compatibility
       "hidden_size": "d_model",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }





   def __init__(       self					:			Any     , __lowerCamelCase					:			int=32000     , __lowerCamelCase					:			List[str]=1024     , __lowerCamelCase					:			int=24     , __lowerCamelCase					:			Optional[int]=16     , __lowerCamelCase					:			Any=4096     , __lowerCamelCase					:			Any="gelu"     , __lowerCamelCase					:			Union[str, Any]=True     , __lowerCamelCase					:			Optional[int]="bi"     , __lowerCamelCase					:			Any=0.02     , __lowerCamelCase					:			Union[str, Any]=1e-12     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[str]=512     , __lowerCamelCase					:			List[str]=None     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			Any=False     , __lowerCamelCase					:			List[Any]=False     , __lowerCamelCase					:			Tuple=-1     , __lowerCamelCase					:			int=False     , __lowerCamelCase					:			str="last"     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			int="tanh"     , __lowerCamelCase					:			Any=0.1     , __lowerCamelCase					:			Tuple=5     , __lowerCamelCase					:			Dict=5     , __lowerCamelCase					:			Tuple=5     , __lowerCamelCase					:			Dict=1     , __lowerCamelCase					:			List[str]=2     , **__lowerCamelCase					:			str     , ):
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   d_model
       SCREAMING_SNAKE_CASE										=   n_layer
       SCREAMING_SNAKE_CASE										=   n_head
       if d_model % n_head != 0:
           raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0"			)
       if "d_head" in kwargs:
           if kwargs["d_head"] != d_model // n_head:
               raise ValueError(
                   f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"			)
       SCREAMING_SNAKE_CASE										=   d_model // n_head
       SCREAMING_SNAKE_CASE										=   ff_activation
       SCREAMING_SNAKE_CASE										=   d_inner
       SCREAMING_SNAKE_CASE										=   untie_r
       SCREAMING_SNAKE_CASE										=   attn_type

       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps

       SCREAMING_SNAKE_CASE										=   dropout
       SCREAMING_SNAKE_CASE										=   mem_len
       SCREAMING_SNAKE_CASE										=   reuse_len
       SCREAMING_SNAKE_CASE										=   bi_data
       SCREAMING_SNAKE_CASE										=   clamp_len
       SCREAMING_SNAKE_CASE										=   same_length

       SCREAMING_SNAKE_CASE										=   summary_type
       SCREAMING_SNAKE_CASE										=   summary_use_proj
       SCREAMING_SNAKE_CASE										=   summary_activation
       SCREAMING_SNAKE_CASE										=   summary_last_dropout
       SCREAMING_SNAKE_CASE										=   start_n_top
       SCREAMING_SNAKE_CASE										=   end_n_top

       SCREAMING_SNAKE_CASE										=   bos_token_id
       SCREAMING_SNAKE_CASE										=   pad_token_id
       SCREAMING_SNAKE_CASE										=   eos_token_id

       if "use_cache" in kwargs:
           warnings.warn(
               "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
               " instead."     , __lowerCamelCase     , )
           SCREAMING_SNAKE_CASE										=   kwargs["use_cache"]

       SCREAMING_SNAKE_CASE										=   use_mems_eval
       SCREAMING_SNAKE_CASE										=   use_mems_train
       super().__init__(pad_token_id=__lowerCamelCase     , bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)





   @property
   def      _snake_case		(       self					:			Dict			):
       logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."			)
       return -1





   @max_position_embeddings.setter
   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Union[str, Any]			):
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f"The model {self.model_type} is one of the few models that has no sequence length limit."			)







from __future__ import annotations




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :str 							=    order

            # a_{0} ... a_{k}
            __snake_case      :int 							=    [1.0] + [0.0] * order
            # b_{0} ... b_{k}
            __snake_case      :Any 							=    [1.0] + [0.0] * order

            # x[n-1] ... x[n-k]
            __snake_case      :List[Any] 							=    [0.0] * self.order
            # y[n-1] ... y[n-k]
            __snake_case      :int 							=    [0.0] * self.order



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            if len(a__     ) < self.order:
                 __snake_case      :Optional[int] 							=    [1.0, *a_coeffs]

            if len(a__     ) != self.order + 1:
                 __snake_case      :Dict 							=    (
                     F'''Expected a_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            if len(a__     ) != self.order + 1:
                 __snake_case      :Optional[Any] 							=    (
                     F'''Expected b_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            __snake_case      :str 							=    a_coeffs
            __snake_case      :str 							=    b_coeffs



       def        __lowercase    (				self      ,    a__     )			->   float:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    0.0

            # Start at index 1 and do index 0 at the end.
            for i in range(1      ,    self.order + 1     ):
                 result += (
                     self.b_coeffs[i] * self.input_history[i - 1]
                     - self.a_coeffs[i] * self.output_history[i - 1]
                 )

            __snake_case      :Dict 							=    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

            __snake_case      :Optional[int] 							=    self.input_history[:-1]
            __snake_case      :Optional[Any] 							=    self.output_history[:-1]

            __snake_case      :Union[str, Any] 							=    sample
            __snake_case      :Optional[Any] 							=    result

            return result


