"""simple docstring"""




from __future__ import annotations
class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase   )    -> None:
													A__		   =							order

													# a_{0} ... a_{k}
													A__		   =							[1.0] + [0.0] * order
													# b_{0} ... b_{k}
													A__		   =							[1.0] + [0.0] * order

													# x[n-1] ... x[n-k]
													A__		   =							[0.0] * self.order
													# y[n-1] ... y[n-k]
													A__		   =							[0.0] * self.order





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													if len(__UpperCAmelCase   ) < self.order:
																				A__		   =							[1.0, *a_coeffs]

													if len(__UpperCAmelCase   ) != self.order + 1:
																				A__		   =							(
																				    f'''Expected a_coeffs to have {self.order + 1} elements '''
																				    f'''for {self.order}-order filter, got {len(__UpperCAmelCase   )}'''
																				)
																				raise ValueError(__UpperCAmelCase   )

													if len(__UpperCAmelCase   ) != self.order + 1:
																				A__		   =							(
																				    f'''Expected b_coeffs to have {self.order + 1} elements '''
																				    f'''for {self.order}-order filter, got {len(__UpperCAmelCase   )}'''
																				)
																				raise ValueError(__UpperCAmelCase   )

													A__		   =							a_coeffs
													A__		   =							b_coeffs





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> float:
													A__		   =							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]
																				)

													A__		   =							(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

													A__		   =							self.input_history[:-1]
													A__		   =							self.output_history[:-1]

													A__		   =							sample
													A__		   =							result

													return result






"""simple docstring"""




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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Union[str, Any]  							=						'transfo-xl'
						lowerCAmelCase__     :  Any  							=						['mems']
						lowerCAmelCase__     :  Tuple  							=						{
						    'n_token': 'vocab_size',
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}





						def __init__(				self						,__UpperCAmelCase=26_77_35						,__UpperCAmelCase=[2_00_00, 4_00_00, 20_00_00]						,__UpperCAmelCase=10_24						,__UpperCAmelCase=10_24						,__UpperCAmelCase=16						,__UpperCAmelCase=64						,__UpperCAmelCase=40_96						,__UpperCAmelCase=4						,__UpperCAmelCase=False						,__UpperCAmelCase=18						,__UpperCAmelCase=16_00						,__UpperCAmelCase=10_00						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=0						,__UpperCAmelCase=-1						,__UpperCAmelCase=True						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=True						,__UpperCAmelCase="normal"						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=1e-5						,__UpperCAmelCase=0						,**__UpperCAmelCase						,)    -> Tuple:
													A__		   =							vocab_size
													A__		   =							[]
													self.cutoffs.extend(__UpperCAmelCase   )
													if proj_share_all_but_first:
																				A__		   =							[False] + [True] * len(self.cutoffs   )
													else:
																				A__		   =							[False] + [False] * len(self.cutoffs   )
													A__		   =							d_model
													A__		   =							d_embed
													A__		   =							d_head
													A__		   =							d_inner
													A__		   =							div_val
													A__		   =							pre_lnorm
													A__		   =							n_layer
													A__		   =							n_head
													A__		   =							mem_len
													A__		   =							same_length
													A__		   =							attn_type
													A__		   =							clamp_len
													A__		   =							sample_softmax
													A__		   =							adaptive
													A__		   =							dropout
													A__		   =							dropatt
													A__		   =							untie_r
													A__		   =							init
													A__		   =							init_range
													A__		   =							proj_init_std
													A__		   =							init_std
													A__		   =							layer_norm_epsilon
													super().__init__(eos_token_id=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													# Message copied from Transformer-XL documentation
													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						,__UpperCAmelCase   )    -> int:
													# 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.'''   )





