
import warnings

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


__lowerCAmelCase       :      Optional[int]         =		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     __lowerCAmelCase						(						__lowercase   ):



      """simple docstring"""







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




      def __init__(   self		:							Optional[Any]					,							_snake_case		:							Union[str, Any]=3_2000					,							_snake_case		:							Optional[Any]=1024					,							_snake_case		:							int=24					,							_snake_case		:							List[Any]=16					,							_snake_case		:							Union[str, Any]=4096					,							_snake_case		:							Optional[int]="gelu"					,							_snake_case		:							int=True					,							_snake_case		:							Union[str, Any]="bi"					,							_snake_case		:							Any=0.02					,							_snake_case		:							Dict=1E-1_2					,							_snake_case		:							Optional[Any]=0.1					,							_snake_case		:							Union[str, Any]=512					,							_snake_case		:							List[str]=None					,							_snake_case		:							int=True					,							_snake_case		:							List[str]=False					,							_snake_case		:							List[Any]=False					,							_snake_case		:							List[Any]=-1					,							_snake_case		:							Tuple=False					,							_snake_case		:							Any="last"					,							_snake_case		:							List[str]=True					,							_snake_case		:							Union[str, Any]="tanh"					,							_snake_case		:							Dict=0.1					,							_snake_case		:							Dict=5					,							_snake_case		:							Optional[int]=5					,							_snake_case		:							List[Any]=5					,							_snake_case		:							List[Any]=1					,							_snake_case		:							Union[str, Any]=2					,							**_snake_case		:							str					,							):
          __lowercase		:      Any						     =							vocab_size
          __lowercase		:      Any						     =							d_model
          __lowercase		:      Any						     =							n_layer
          __lowercase		:      Optional[Any]						     =							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})'					)
          __lowercase		:      Tuple						     =							d_model // n_head
          __lowercase		:      int						     =							ff_activation
          __lowercase		:      str						     =							d_inner
          __lowercase		:      int						     =							untie_r
          __lowercase		:      List[str]						     =							attn_type

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

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

          __lowercase		:      List[Any]						     =							summary_type
          __lowercase		:      Tuple						     =							summary_use_proj
          __lowercase		:      Dict						     =							summary_activation
          __lowercase		:      Dict						     =							summary_last_dropout
          __lowercase		:      int						     =							start_n_top
          __lowercase		:      int						     =							end_n_top

          __lowercase		:      Any						     =							bos_token_id
          __lowercase		:      Optional[Any]						     =							pad_token_id
          __lowercase		:      int						     =							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.'''					,							_a					,							)
              __lowercase		:      Optional[Any]						     =							kwargs['''use_cache''']

          __lowercase		:      List[str]						     =							use_mems_eval
          __lowercase		:      List[Any]						     =							use_mems_train
          super().__init__(pad_token_id=_a					,							bos_token_id=_a					,							eos_token_id=_a					,							**_a					)




      @property
      def 		snake_case_      (   self		:							int					):
          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		:							Tuple					,							_snake_case		:							List[str]					):
          # 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.'					)





import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

a__												=						'''Usage of script: script_name <size_of_canvas:int>'''

a__												=						[0] * 100 + [1] * 10
random.shuffle(choice)



def   __UpperCAmelCase					(      __a :     int      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       int   =						[[False for i in range(__a      )] for j in range(__a      )]
					return canvas



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	None:

					"""simple docstring"""

					for i, row in enumerate(__a      ):
										for j, _ in enumerate(__a      ):
															_a      :       Optional[int]   =						bool(random.getrandbits(1      )      )



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       Any   =						np.array(__a      )
					_a      :       Optional[int]   =						np.array(create_canvas(current_canvas.shape[0]      )      )
					for r, row in enumerate(__a      ):
										for c, pt in enumerate(__a      ):
															_a      :       Tuple   =						__judge_point(
															    __a						,current_canvas[r - 1 : r + 2, c - 1 : c + 2]      )

					_a      :       List[str]   =						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					_a      :       list[list[bool]]   =						current_canvas.tolist()
					return return_canvas



def   __UpperCAmelCase					(      __a :     bool						,__a :     list[list[bool]]      )     ->	bool:

					"""simple docstring"""

					_a      :       Optional[Any]   =						0
					_a      :       str   =						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					_a      :       Optional[int]   =						pt
					if pt:
										if alive < 2:
															_a      :       Dict   =						False
										elif alive == 2 or alive == 3:
															_a      :       Optional[Any]   =						True
										elif alive > 3:
															_a      :       str   =						False
					else:
										if alive == 3:
															_a      :       int   =						True

					return state


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise Exception(usage_doc)

			a__												=						int(sys.argv[1])
			# main working structure of this module.
			a__												=						create_canvas(canvas_size)
			seed(c)
			a__						,     a__												=						plt.subplots()
			fig.show()
			a__												=						ListedColormap(['''w''', '''k'''])
			try:
						while True:
									a__												=						run(c)
									ax.matshow(c, cmap=cmap)
									fig.canvas.draw()
									ax.cla()
			except KeyboardInterrupt:
						# do nothing.
						pass



