import warnings

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


lowerCamelCase_    :		str							=	logging.get_logger(__name__)

lowerCamelCase_    :		str							=	{
    """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 						a__							(       _UpperCamelCase		):
   A__    :       Optional[Any]        =      'xlnet'
   A__    :       int        =      ['mems']
   A__    :       Optional[int]        =      {
       'n_token': 'vocab_size',  # Backward compatibility
       'hidden_size': 'd_model',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }



   def __init__(						self				,						UpperCAmelCase=3_2_0_0_0				,						UpperCAmelCase=1_0_2_4				,						UpperCAmelCase=2_4				,						UpperCAmelCase=1_6				,						UpperCAmelCase=4_0_9_6				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=True				,						UpperCAmelCase="bi"				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=None				,						UpperCAmelCase=True				,						UpperCAmelCase=False				,						UpperCAmelCase=False				,						UpperCAmelCase=-1				,						UpperCAmelCase=False				,						UpperCAmelCase="last"				,						UpperCAmelCase=True				,						UpperCAmelCase="tanh"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5				,						UpperCAmelCase=5				,						UpperCAmelCase=5				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   str:
        __a		 =       vocab_size
        __a		 =       d_model
        __a		 =       n_layer
        __a		 =       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})'''   )
        __a		 =       d_model // n_head
        __a		 =       ff_activation
        __a		 =       d_inner
        __a		 =       untie_r
        __a		 =       attn_type

        __a		 =       initializer_range
        __a		 =       layer_norm_eps

        __a		 =       dropout
        __a		 =       mem_len
        __a		 =       reuse_len
        __a		 =       bi_data
        __a		 =       clamp_len
        __a		 =       same_length

        __a		 =       summary_type
        __a		 =       summary_use_proj
        __a		 =       summary_activation
        __a		 =       summary_last_dropout
        __a		 =       start_n_top
        __a		 =       end_n_top

        __a		 =       bos_token_id
        __a		 =       pad_token_id
        __a		 =       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.'				,						_UpperCAmelCase				,						)
             __a		 =       kwargs['use_cache']

        __a		 =       use_mems_eval
        __a		 =       use_mems_train
        super().__init__(pad_token_id=_UpperCAmelCase				,						bos_token_id=_UpperCAmelCase				,						eos_token_id=_UpperCAmelCase				,						**_UpperCAmelCase   )



   @property
   def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
        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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Tuple:
        # 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

import numpy as np
def 							lowerCAmelCase(     __lowerCamelCase						):
					return np.maximum(0				,    __lowerCamelCase						)


if __name__ == "__main__":
	print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]




