





"""simple docstring"""



import random
import sys

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

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

_snake_case          =						[0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]						):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]						):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any						):






							"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""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)

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








"""simple docstring"""



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_snake_case          =						logging.getLogger(__name__)
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str
				SCREAMING_SNAKE_CASE_:							List[str]
				SCREAMING_SNAKE_CASE_:							Optional[List[str]]






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[int]
				SCREAMING_SNAKE_CASE_:							List[int]
				SCREAMING_SNAKE_CASE_:							Optional[List[int]]							=					None
				SCREAMING_SNAKE_CASE_:							Optional[List[int]]							=					None






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"train"
				SCREAMING_SNAKE_CASE_:							Tuple							=					"dev"
				SCREAMING_SNAKE_CASE_:							List[str]							=					"test"






class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Union[Split, str]  )		->  List[InputExample]:







											"""simple docstring"""




											raise NotImplementedError


				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str  )		->  List[str]:







											"""simple docstring"""




											raise NotImplementedError







				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			List[InputExample]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Optional[int]="[CLS]"				,     UpperCAmelCase_	:			Tuple=1				,     UpperCAmelCase_	:			str="[SEP]"				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=0				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=-100				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=True				,     )		->  List[InputFeatures]:







											"""simple docstring"""




											_lowerCAmelCase  	=			{label: i for i, label in enumerate(UpperCAmelCase_  )}

											_lowerCAmelCase  	=			[]
											for ex_index, example in enumerate(UpperCAmelCase_  ):
																		if ex_index % 10_000 == 0:
																									logger.info('Writing example %d of %d'				,     UpperCAmelCase_				,     len(UpperCAmelCase_  )  )

																		_lowerCAmelCase  	=			[]
																		_lowerCAmelCase  	=			[]
																		for word, label in zip(example.words				,     example.labels  ):
																									_lowerCAmelCase  	=			tokenizer.tokenize(UpperCAmelCase_  )

																									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																									if len(UpperCAmelCase_  ) > 0:
																																tokens.extend(UpperCAmelCase_  )
																																# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																																label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCAmelCase_  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																		_lowerCAmelCase  	=			tokenizer.num_special_tokens_to_add()
																		if len(UpperCAmelCase_  ) > max_seq_length - special_tokens_count:
																									_lowerCAmelCase  	=			tokens[: (max_seq_length - special_tokens_count)]
																									_lowerCAmelCase  	=			label_ids[: (max_seq_length - special_tokens_count)]

																		# The convention in BERT is:
																		# (a) For sequence pairs:
																		#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
																		#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
																		# (b) For single sequences:
																		#  tokens:   [CLS] the dog is hairy . [SEP]
																		#  type_ids:   0   0   0   0  0     0   0
																		#
																		# Where "type_ids" are used to indicate whether this is the first
																		# sequence or the second sequence. The embedding vectors for `type=0` and
																		# `type=1` were learned during pre-training and are added to the wordpiece
																		# embedding vector (and position vector). This is not *strictly* necessary
																		# since the [SEP] token unambiguously separates the sequences, but it makes
																		# it easier for the model to learn the concept of sequences.
																		#
																		# For classification tasks, the first vector (corresponding to [CLS]) is
																		# used as the "sentence vector". Note that this only makes sense because
																		# the entire model is fine-tuned.
																		tokens += [sep_token]
																		label_ids += [pad_token_label_id]
																		if sep_token_extra:
																									# roberta uses an extra separator b/w pairs of sentences
																									tokens += [sep_token]
																									label_ids += [pad_token_label_id]
																		_lowerCAmelCase  	=			[sequence_a_segment_id] * len(UpperCAmelCase_  )

																		if cls_token_at_end:
																									tokens += [cls_token]
																									label_ids += [pad_token_label_id]
																									segment_ids += [cls_token_segment_id]
																		else:
																									_lowerCAmelCase  	=			[cls_token] + tokens
																									_lowerCAmelCase  	=			[pad_token_label_id] + label_ids
																									_lowerCAmelCase  	=			[cls_token_segment_id] + segment_ids

																		_lowerCAmelCase  	=			tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )

																		# The mask has 1 for real tokens and 0 for padding tokens. Only real
																		# tokens are attended to.
																		_lowerCAmelCase  	=			[1 if mask_padding_with_zero else 0] * len(UpperCAmelCase_  )

																		# Zero-pad up to the sequence length.
																		_lowerCAmelCase  	=			max_seq_length - len(UpperCAmelCase_  )
																		if pad_on_left:
																									_lowerCAmelCase  	=			([pad_token] * padding_length) + input_ids
																									_lowerCAmelCase  	=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																									_lowerCAmelCase  	=			([pad_token_segment_id] * padding_length) + segment_ids
																									_lowerCAmelCase  	=			([pad_token_label_id] * padding_length) + label_ids
																		else:
																									input_ids += [pad_token] * padding_length
																									input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																									segment_ids += [pad_token_segment_id] * padding_length
																									label_ids += [pad_token_label_id] * padding_length

																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length

																		if ex_index < 5:
																									logger.info('*** Example ***'  )
																									logger.info('guid: %s'				,     example.guid  )
																									logger.info('tokens: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in tokens]  )  )
																									logger.info('input_ids: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in input_ids]  )  )
																									logger.info('input_mask: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in input_mask]  )  )
																									logger.info('segment_ids: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in segment_ids]  )  )
																									logger.info('label_ids: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in label_ids]  )  )

																		if "token_type_ids" not in tokenizer.model_input_names:
																									_lowerCAmelCase  	=			None

																		features.append(
																		    InputFeatures(
																		        input_ids=UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_				,     label_ids=UpperCAmelCase_  )  )
											return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset
	class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					nn.CrossEntropyLoss().ignore_index


					def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  List[str]:







												"""simple docstring"""




												_lowerCAmelCase  	=			os.path.join(
												    UpperCAmelCase_				,     'cached_{}_{}_{}'.format(mode.value				,     tokenizer.__class__.__name__				,     str(UpperCAmelCase_  )  )				,     )

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_lowerCAmelCase  	=			cached_features_file + '.lock'
												with FileLock(UpperCAmelCase_  ):
																			if os.path.exists(UpperCAmelCase_  ) and not overwrite_cache:
																										logger.info(F"""Loading features from cached file {cached_features_file}"""  )
																										_lowerCAmelCase  	=			torch.load(UpperCAmelCase_  )
																			else:
																										logger.info(F"""Creating features from dataset file at {data_dir}"""  )
																										_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
																										# TODO clean up all this to leverage built-in features of tokenizers
																										_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
																										    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     cls_token_at_end=bool(model_type in ['xlnet']  )				,     cls_token=tokenizer.cls_token				,     cls_token_segment_id=2 if model_type in ['xlnet'] else 0				,     sep_token=tokenizer.sep_token				,     sep_token_extra=UpperCAmelCase_				,     pad_on_left=bool(tokenizer.padding_side == 'left'  )				,     pad_token=tokenizer.pad_token_id				,     pad_token_segment_id=tokenizer.pad_token_type_id				,     pad_token_label_id=self.pad_token_label_id				,     )
																										logger.info(F"""Saving features into cached file {cached_features_file}"""  )
																										torch.save(self.features				,     UpperCAmelCase_  )


					def __len__(					self	:			Dict  )		->  int:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			List[str]				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]


if is_tf_available():
	import tensorflow as tf
	class        _SCREAMING_SNAKE_CASE  :






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					-1_0_0


					def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  Dict:







												"""simple docstring"""




												_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
												# TODO clean up all this to leverage built-in features of tokenizers
												_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
												    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     cls_token_at_end=bool(model_type in ['xlnet']  )				,     cls_token=tokenizer.cls_token				,     cls_token_segment_id=2 if model_type in ['xlnet'] else 0				,     sep_token=tokenizer.sep_token				,     sep_token_extra=UpperCAmelCase_				,     pad_on_left=bool(tokenizer.padding_side == 'left'  )				,     pad_token=tokenizer.pad_token_id				,     pad_token_segment_id=tokenizer.pad_token_type_id				,     pad_token_label_id=self.pad_token_label_id				,     )

												def gen():
																			for ex in self.features:
																										if ex.token_type_ids is None:
																																	yield (
																																	    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																																	    ex.label_ids,
																																	)
																										else:
																																	yield (
																																	    {
																																	        "input_ids": ex.input_ids,
																																	        "attention_mask": ex.attention_mask,
																																	        "token_type_ids": ex.token_type_ids,
																																	    },
																																	    ex.label_ids,
																																	)

												if "token_type_ids" not in tokenizer.model_input_names:
																			_lowerCAmelCase  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)				,     (
																			        {'input_ids': tf.TensorShape([None]  ), 'attention_mask': tf.TensorShape([None]  )},
																			        tf.TensorShape([None]  ),
																			    )				,     )
												else:
																			_lowerCAmelCase  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)				,     (
																			        {
																			            'input_ids': tf.TensorShape([None]  ),
																			            'attention_mask': tf.TensorShape([None]  ),
																			            'token_type_ids': tf.TensorShape([None]  ),
																			        },
																			        tf.TensorShape([None]  ),
																			    )				,     )


					def 				__lowerCamelCase	(					self	:			str  )		->  Tuple:







												"""simple docstring"""




												_lowerCAmelCase  	=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

												return self.dataset


					def __len__(					self	:			List[Any]  )		->  Optional[Any]:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			int				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]






