






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__a      :       int				  =   logging.get_logger(__name__)

__a      :       int				  =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__a      :       Optional[Any]				  =   {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__a      :       Tuple				  =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__a      :       Tuple				  =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE   						=				SqueezeBertTokenizer




     def __init__(				self       :			Any ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			str=None ,	UpperCamelCase_       :			Tuple=True ,	UpperCamelCase_       :			int="[UNK]" ,	UpperCamelCase_       :			Tuple="[SEP]" ,	UpperCamelCase_       :			Any="[PAD]" ,	UpperCamelCase_       :			Optional[int]="[CLS]" ,	UpperCamelCase_       :			Optional[int]="[MASK]" ,	UpperCamelCase_       :			Union[str, Any]=True ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			Dict ,	):


         """simple docstring"""



         super().__init__(
             UpperCamelCase_ ,	tokenizer_file=UpperCamelCase_ ,	do_lower_case=UpperCamelCase_ ,	unk_token=UpperCamelCase_ ,	sep_token=UpperCamelCase_ ,	pad_token=UpperCamelCase_ ,	cls_token=UpperCamelCase_ ,	mask_token=UpperCamelCase_ ,	tokenize_chinese_chars=UpperCamelCase_ ,	strip_accents=UpperCamelCase_ ,	**UpperCamelCase_ ,	)

         __A         =	json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
         if (
             normalizer_state.get("""lowercase""" ,	UpperCamelCase_				) != do_lower_case
             or normalizer_state.get("""strip_accents""" ,	UpperCamelCase_				) != strip_accents
             or normalizer_state.get("""handle_chinese_chars""" ,	UpperCamelCase_				) != tokenize_chinese_chars
         ):
             __A         =	getattr(UpperCamelCase_ ,	normalizer_state.pop("""type"""				)				)
             __A         =	do_lower_case
             __A         =	strip_accents
             __A         =	tokenize_chinese_chars
             __A         =	normalizer_class(**UpperCamelCase_				)

         __A         =	do_lower_case




     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Tuple=None				):


         """simple docstring"""



         __A         =	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output




     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			List[int] ,	UpperCamelCase_       :			Optional[List[int]] = None				):


         """simple docstring"""



         __A         =	[self.sep_token_id]
         __A         =	[self.cls_token_id]
         if token_ids_a is None:
             return len(cls + token_ids_a + sep				) * [0]
         return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]





     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[str] = None				):


         """simple docstring"""



         __A         =	self._tokenizer.model.save(UpperCamelCase_ ,	name=UpperCamelCase_				)
         return tuple(UpperCamelCase_				)












import random
import sys

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

__a      :       List[Any]				  =   "Usage of script: script_name <size_of_canvas:int>"

__a      :       Dict				  =   [0] * 100 + [1] * 10
random.shuffle(choice)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      list[list[bool]]:





    """simple docstring"""

    __A         =	[[False for i in range(__lowercase    )] for j in range(__lowercase    )]
    return canvas



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[list[bool]]    )						->      None:





    """simple docstring"""

    for i, row in enumerate(__lowercase    ):
        for j, _ in enumerate(__lowercase    ):
            __A         =	bool(random.getrandbits(1    )    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[list[bool]]    )						->      list[list[bool]]:





    """simple docstring"""

    __A         =	np.array(__lowercase    )
    __A         =	np.array(create_canvas(current_canvas.shape[0]    )    )
    for r, row in enumerate(__lowercase    ):
        for c, pt in enumerate(__lowercase    ):
            __A         =	__judge_point(
                __lowercase			,				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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			bool			,				__lowercase   :			list[list[bool]]    )						->      bool:





    """simple docstring"""

    __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)

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




