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






def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[[False for i in range(__lowerCAmelCase       )] for j in range(__lowerCAmelCase       )]
			return canvas






def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[bool]]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[bool]]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			np.array(__lowerCAmelCase       )
			lowerCAmelCase_      			=			np.array(create_canvas(current_canvas.shape[0]       )       )
			for r, row in enumerate(__lowerCAmelCase       ):
						for c, pt in enumerate(__lowerCAmelCase       ):
									lowerCAmelCase_      			=			__judge_point(
									    __lowerCAmelCase					,							current_canvas[r - 1 : r + 2, c - 1 : c + 2]       )

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






def      lowerCamelCase__       (       __lowerCAmelCase						:						bool					,							__lowerCAmelCase						:						list[list[bool]]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			0
			lowerCAmelCase_      			=			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.
			lowerCAmelCase_      			=			pt
			if pt:
						if alive < 2:
									lowerCAmelCase_      			=			False
						elif alive == 2 or alive == 3:
									lowerCAmelCase_      			=			True
						elif alive > 3:
									lowerCAmelCase_      			=			False
			else:
						if alive == 3:
									lowerCAmelCase_      			=			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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A     =  {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Wav2Vec2ForAudioFrameClassification",
			    "Wav2Vec2ForCTC",
			    "Wav2Vec2ForMaskedLM",
			    "Wav2Vec2ForPreTraining",
			    "Wav2Vec2ForSequenceClassification",
			    "Wav2Vec2ForXVector",
			    "Wav2Vec2Model",
			    "Wav2Vec2PreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFWav2Vec2ForCTC",
			    "TFWav2Vec2Model",
			    "TFWav2Vec2PreTrainedModel",
			    "TFWav2Vec2ForSequenceClassification",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "FlaxWav2Vec2ForCTC",
			    "FlaxWav2Vec2ForPreTraining",
			    "FlaxWav2Vec2Model",
			    "FlaxWav2Vec2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



