'''simple docstring'''






import random
import sys

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

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

SCREAMING_SNAKE_CASE_	   =			[0] * 1_00 + [1] * 10
random.shuffle(choice)






def  UpperCamelCase__					(					_lowercase    :				int     )							->	list[list[bool]]:
  __UpperCAmelCase:					List[str]		= [[False for i in range(_lowercase     )] for j in range(_lowercase     )]
  return canvas






def  UpperCamelCase__					(					_lowercase    :				list[list[bool]]     )							->	None:
  for i, row in enumerate(_lowercase     ):
    for j, _ in enumerate(_lowercase     ):
      __UpperCAmelCase:					Optional[int]		= bool(random.getrandbits(1     )     )






def  UpperCamelCase__					(					_lowercase    :				list[list[bool]]     )							->	list[list[bool]]:
  __UpperCAmelCase:					str		= np.array(_lowercase     )
  __UpperCAmelCase:					Dict		= np.array(create_canvas(current_canvas.shape[0]     )     )
  for r, row in enumerate(_lowercase     ):
    for c, pt in enumerate(_lowercase     ):
      __UpperCAmelCase:					List[str]		= __judge_point(
          _lowercase   ,    current_canvas[r - 1 : r + 2, c - 1 : c + 2]     )

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






def  UpperCamelCase__					(					_lowercase    :				bool   ,    _lowercase    :				list[list[bool]]     )							->	bool:
  __UpperCAmelCase:					List[str]		= 0
  __UpperCAmelCase:					List[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.
  __UpperCAmelCase:					Any		= pt
  if pt:
    if alive < 2:
      __UpperCAmelCase:					int		= False
    elif alive == 2 or alive == 3:
      __UpperCAmelCase:					Tuple		= True
    elif alive > 3:
      __UpperCAmelCase:					List[str]		= False
  else:
    if alive == 3:
      __UpperCAmelCase:					Dict		= True

  return state


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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_	   =			{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RemBertForCausalLM',
        'RemBertForMaskedLM',
        'RemBertForMultipleChoice',
        'RemBertForQuestionAnswering',
        'RemBertForSequenceClassification',
        'RemBertForTokenClassification',
        'RemBertLayer',
        'RemBertModel',
        'RemBertPreTrainedModel',
        'load_tf_weights_in_rembert',
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRemBertForCausalLM',
        'TFRemBertForMaskedLM',
        'TFRemBertForMultipleChoice',
        'TFRemBertForQuestionAnswering',
        'TFRemBertForSequenceClassification',
        'TFRemBertForTokenClassification',
        'TFRemBertLayer',
        'TFRemBertModel',
        'TFRemBertPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert import RemBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert_fast import RemBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_rembert import (
            REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            RemBertForCausalLM,
            RemBertForMaskedLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
            RemBertLayer,
            RemBertModel,
            RemBertPreTrainedModel,
            load_tf_weights_in_rembert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_rembert import (
            TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRemBertForCausalLM,
            TFRemBertForMaskedLM,
            TFRemBertForMultipleChoice,
            TFRemBertForQuestionAnswering,
            TFRemBertForSequenceClassification,
            TFRemBertForTokenClassification,
            TFRemBertLayer,
            TFRemBertModel,
            TFRemBertPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)