

import warnings

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


__lowercase     : List[Any]       =						logging.get_logger(__name__)

__lowercase     : Dict       =						{
    '''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     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      int					=   '''xlnet'''
  __lowerCamelCase			:      Tuple					=   ['''mems''']
  __lowerCamelCase			:      List[Any]					=   {
      '''n_token''': '''vocab_size''',  # Backward compatibility
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''n_head''',
      '''num_hidden_layers''': '''n_layer''',
  }





  def __init__(  self   ,SCREAMING_SNAKE_CASE_=32000   ,SCREAMING_SNAKE_CASE_=1024   ,SCREAMING_SNAKE_CASE_=24   ,SCREAMING_SNAKE_CASE_=16   ,SCREAMING_SNAKE_CASE_=4096   ,SCREAMING_SNAKE_CASE_="gelu"   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_="bi"   ,SCREAMING_SNAKE_CASE_=0.02   ,SCREAMING_SNAKE_CASE_=1E-12   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=512   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_=-1   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_="last"   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_="tanh"   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=5   ,SCREAMING_SNAKE_CASE_=5   ,SCREAMING_SNAKE_CASE_=5   ,SCREAMING_SNAKE_CASE_=1   ,SCREAMING_SNAKE_CASE_=2   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		vocab_size
      snake_case							:		List[str]        =		d_model
      snake_case							:		Optional[int]        =		n_layer
      snake_case							:		List[str]        =		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})"""    )
      snake_case							:		Dict        =		d_model // n_head
      snake_case							:		Any        =		ff_activation
      snake_case							:		Optional[int]        =		d_inner
      snake_case							:		Optional[int]        =		untie_r
      snake_case							:		Optional[int]        =		attn_type

      snake_case							:		Any        =		initializer_range
      snake_case							:		Union[str, Any]        =		layer_norm_eps

      snake_case							:		Union[str, Any]        =		dropout
      snake_case							:		int        =		mem_len
      snake_case							:		List[str]        =		reuse_len
      snake_case							:		List[Any]        =		bi_data
      snake_case							:		Dict        =		clamp_len
      snake_case							:		int        =		same_length

      snake_case							:		Any        =		summary_type
      snake_case							:		Union[str, Any]        =		summary_use_proj
      snake_case							:		int        =		summary_activation
      snake_case							:		List[Any]        =		summary_last_dropout
      snake_case							:		List[str]        =		start_n_top
      snake_case							:		Optional[int]        =		end_n_top

      snake_case							:		Union[str, Any]        =		bos_token_id
      snake_case							:		Any        =		pad_token_id
      snake_case							:		str        =		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."""   ,SCREAMING_SNAKE_CASE_   ,)
          snake_case							:		int        =		kwargs["""use_cache"""]

      snake_case							:		str        =		use_mems_eval
      snake_case							:		Optional[int]        =		use_mems_train
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,bos_token_id=SCREAMING_SNAKE_CASE_   ,eos_token_id=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  @property
  def    snake_case_       (  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    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      # 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 PIL import Image







def 						lowerCamelCase__      ( __A    :Image		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case       	=							0
       __snake_case       	=							image.load()
       for i in range(__A		):
              for j in range(__A		):
                     __snake_case       	=							pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A		):
              for i in range(__A		):
                     __snake_case       	=							2_5_5 if pixels[i, j] > mean else 0
       return image


if __name__ == "__main__":
    UpperCamelCase__		 =     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')

