
"""simple docstring"""

import warnings

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


UpperCamelCase			:       List[str]									=				logging.get_logger(__name__)

UpperCamelCase			:       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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"xlnet"
     lowercase		     =		["mems"]
     lowercase		     =		{
         "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_2000			,  __UpperCAmelCase=1024			,  __UpperCAmelCase=24			,  __UpperCAmelCase=16			,  __UpperCAmelCase=4096			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=True			,  __UpperCAmelCase="bi"			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=1E-12			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=512			,  __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			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= d_model
      __UpperCamelCase       						= n_layer
      __UpperCamelCase       						= 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})'   )
      __UpperCamelCase       						= d_model // n_head
      __UpperCamelCase       						= ff_activation
      __UpperCamelCase       						= d_inner
      __UpperCamelCase       						= untie_r
      __UpperCamelCase       						= attn_type

      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= layer_norm_eps

      __UpperCamelCase       						= dropout
      __UpperCamelCase       						= mem_len
      __UpperCamelCase       						= reuse_len
      __UpperCamelCase       						= bi_data
      __UpperCamelCase       						= clamp_len
      __UpperCamelCase       						= same_length

      __UpperCamelCase       						= summary_type
      __UpperCamelCase       						= summary_use_proj
      __UpperCamelCase       						= summary_activation
      __UpperCamelCase       						= summary_last_dropout
      __UpperCamelCase       						= start_n_top
      __UpperCamelCase       						= end_n_top

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= pad_token_id
      __UpperCamelCase       						= 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			,  )
       __UpperCamelCase       						= kwargs['use_cache']

      __UpperCamelCase       						= use_mems_eval
      __UpperCamelCase       						= use_mems_train
      super().__init__(pad_token_id=__UpperCAmelCase			,  bos_token_id=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase			,  **__UpperCAmelCase   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      raise NotImplementedError(
          F'The model {self.model_type} is one of the few models that has no sequence length limit.'   )





"""simple docstring"""

from __future__ import annotations

import os
from typing import Any

import requests

UpperCamelCase			:       Union[str, Any]									=				"https://api.github.com"

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase			:       Union[str, Any]									=				BASE_URL + "/user"

# https://github.com/settings/tokens
UpperCamelCase			:       Optional[int]									=				os.environ.get("USER_TOKEN", "")






def 							A		(       snake_case   :str	)   ->			dict[Any, Any]:
 __UpperCamelCase       						= {
     'Authorization': f'token {auth_token}',
     'Accept': 'application/vnd.github.v3+json',
 }
 return requests.get(snake_case       ,      headers=snake_case	).json()


if __name__ == "__main__":  # pragma: no cover
      if USER_TOKEN:
            for key, value in fetch_github_info(USER_TOKEN).items():
                  print(f'''{key}: {value}''')
      else:
            raise ValueError("'USER_TOKEN' field cannot be empty.")



