



import random
import sys

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

_lowercase						  =		"Usage of script: script_name <size_of_canvas:int>"

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int	)->  list[list[bool]]:
    A__		 =					[[False for i in range(UpperCamelCase_	)] for j in range(UpperCamelCase_	)]
    return canvas



def 		lowerCAmelCase__    (					UpperCamelCase_				:       list[list[bool]]	)->  None:
    for i, row in enumerate(UpperCamelCase_	):
        for j, _ in enumerate(UpperCamelCase_	):
            A__		 =					bool(random.getrandbits(1	)	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       list[list[bool]]	)->  list[list[bool]]:
    A__		 =					np.array(UpperCamelCase_	)
    A__		 =					np.array(create_canvas(current_canvas.shape[0]	)	)
    for r, row in enumerate(UpperCamelCase_	):
        for c, pt in enumerate(UpperCamelCase_	):
            A__		 =					__judge_point(
                UpperCamelCase_ ,       current_canvas[r - 1 : r + 2, c - 1 : c + 2]	)

    A__		 =					next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    A__		 =					current_canvas.tolist()
    return return_canvas



def 		lowerCAmelCase__    (					UpperCamelCase_				:       bool ,       UpperCamelCase_				:       list[list[bool]]	)->  bool:
    A__		 =					0
    A__		 =					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__		 =					pt
    if pt:
        if alive < 2:
            A__		 =					False
        elif alive == 2 or alive == 3:
            A__		 =					True
        elif alive > 3:
            A__		 =					False
    else:
        if alive == 3:
            A__		 =					True

    return state


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

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









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		{
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''bert'''




     def __init__(				self ,						a__=3_0_5_2_2 ,						a__=7_6_8 ,						a__=1_2 ,						a__=1_2 ,						a__=3_0_7_2 ,						a__="gelu" ,						a__=0.1 ,						a__=0.1 ,						a__=5_1_2 ,						a__=2 ,						a__=0.0_2 ,						a__=1e-12 ,						a__=0 ,						a__="absolute" ,						a__=True ,						a__=None ,						**a__ ,						):
         super().__init__(pad_token_id=a__ ,						**a__)

         A__		 =					vocab_size
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_attention_heads
         A__		 =					hidden_act
         A__		 =					intermediate_size
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					max_position_embeddings
         A__		 =					type_vocab_size
         A__		 =					initializer_range
         A__		 =					layer_norm_eps
         A__		 =					position_embedding_type
         A__		 =					use_cache
         A__		 =					classifier_dropout







class     _UpperCAmelCase			(       A__     ):




     @property
     def 		snake_case_		(				self):
         if self.task == "multiple-choice":
             A__		 =					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__		 =					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ])
