



from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
UpperCAmelCase							=       [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]

def       A_   (						__a  :					int		):

				"""simple docstring"""
				return [int(__a		) for c in F'''{ruleset:08}'''[:8]]

def       A_   (						__a  :					list[list[int]]			,		__a  :					list[int]			,		__a  :					int		):

				"""simple docstring"""
				a__					   =		len(cells[0]		)  # 31
				a__					   =		[]
				for i in range(__a		):
								# Get the neighbors of each cell
								# Handle neighbours outside bounds by using 0 as their value
								a__					   =		0 if i == 0 else cells[time][i - 1]
								a__					   =		0 if i == population - 1 else cells[time][i + 1]
								# Define a new cell and add it to the new generation
								a__					   =		7 - int(F'''{left_neighbor}{cells[time][i]}{right_neighbor}'''			,		2		)
								next_generation.append(rule[situation]		)
				return next_generation

def       A_   (						__a  :					list[list[int]]		):

				"""simple docstring"""
				a__					   =		Image.new("""RGB"""			,		(len(cells[0]		), len(__a		))		)
				a__					   =		img.load()
				# Generates image
				for w in range(img.width		):
								for h in range(img.height		):
												a__					   =		255 - int(255 * cells[h][w]		)
												a__					   =		(color, color, color)
				return img


if __name__ == "__main__":
	UpperCAmelCase							=       bin(int(input("""Rule:\n""").strip()))[2:]
	UpperCAmelCase							=       format_ruleset(int(rule_num))
	for time in range(16):
		CELLS.append(new_generation(CELLS, rule, time))
	UpperCAmelCase							=       generate_image(CELLS)
	# Uncomment to save the image
	# img.save(f"rule_{rule_num}.png")
	img.show()



import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
@dataclass
class       A__  :
							lowerCAmelCase__						:		str            =				field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default="NER"       ,		metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
							lowerCAmelCase__						:		bool            =				field(default=lowerCAmelCase__       ,		metadata={"help": "Set this flag to use fast tokenization."}							)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,		)






@dataclass
class       A__  :
							lowerCAmelCase__						:		str            =				field(
							    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}							)
							lowerCAmelCase__						:		int            =				field(
							    default=128       ,		metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }       ,		)
							lowerCAmelCase__						:		bool            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Overwrite the cached training and evaluation sets"}							)






def 				__SCREAMING_SNAKE_CASE  (						)	->		int:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.
				__lowercase								=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
				__lowercase						,   __lowercase						,   __lowercase								=   parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir     )
				    and os.listdir(training_args.output_dir     )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ' --overwrite_output_dir to overcome.'     )

				__lowercase								=   import_module('tasks'     )

				try:
								__lowercase								=   getattr(SCREAMING_SNAKE_CASE       ,				model_args.task_type     )
								__lowercase								=   token_classification_task_clazz()
				except AttributeError:
								raise ValueError(
								    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
								    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""     )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,				datefmt='%m/%d/%Y %H:%M:%S'       ,				level=logging.INFO       ,				)
				logger.info(
				    'n_replicas: %s, distributed training: %s, 16-bits training: %s'       ,				training_args.n_replicas       ,				bool(training_args.n_replicas > 1     )       ,				training_args.fpaa       ,				)
				logger.info('Training/evaluation parameters %s'       ,				SCREAMING_SNAKE_CASE     )

				# Prepare Token Classification task
				__lowercase								=   token_classification_task.get_labels(data_args.labels     )
				__lowercase								=   dict(enumerate(SCREAMING_SNAKE_CASE     )     )
				__lowercase								=   len(SCREAMING_SNAKE_CASE     )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__lowercase								=   AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,				num_labels=SCREAMING_SNAKE_CASE       ,				idalabel=SCREAMING_SNAKE_CASE       ,				labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE     )}       ,				cache_dir=model_args.cache_dir       ,				)
				__lowercase								=   AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,				cache_dir=model_args.cache_dir       ,				use_fast=model_args.use_fast       ,				)

				with training_args.strategy.scope():
								__lowercase								=   TFAutoModelForTokenClassification.from_pretrained(
								    model_args.model_name_or_path       ,				from_pt=bool('.bin' in model_args.model_name_or_path     )       ,				config=SCREAMING_SNAKE_CASE       ,				cache_dir=model_args.cache_dir       ,				)

				# Get datasets
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				model_type=config.model_type       ,				max_seq_length=data_args.max_seq_length       ,				overwrite_cache=data_args.overwrite_cache       ,				mode=Split.train       ,				)
				    if training_args.do_train
				    else None
				)
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				model_type=config.model_type       ,				max_seq_length=data_args.max_seq_length       ,				overwrite_cache=data_args.overwrite_cache       ,				mode=Split.dev       ,				)
				    if training_args.do_eval
				    else None
				)

				def align_predictions(SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray     ) -> Tuple[List[int], List[int]]:
								__lowercase								=   np.argmax(SCREAMING_SNAKE_CASE       ,				axis=2     )
								__lowercase						,   __lowercase								=   preds.shape
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]

								for i in range(SCREAMING_SNAKE_CASE     ):
												for j in range(SCREAMING_SNAKE_CASE     ):
																if label_ids[i, j] != -100:
																				out_label_list[i].append(label_map[label_ids[i][j]]     )
																				preds_list[i].append(label_map[preds[i][j]]     )

								return preds_list, out_label_list

				def compute_metrics(SCREAMING_SNAKE_CASE			: EvalPrediction     ) -> Dict:
								__lowercase						,   __lowercase								=   align_predictions(p.predictions       ,				p.label_ids     )

								return {
								    "precision": precision_score(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     ),
								    "recall": recall_score(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     ),
								    "f1": fa_score(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     ),
								}

				# Initialize our Trainer
				__lowercase								=   TFTrainer(
				    model=SCREAMING_SNAKE_CASE       ,				args=SCREAMING_SNAKE_CASE       ,				train_dataset=train_dataset.get_dataset() if train_dataset else None       ,				eval_dataset=eval_dataset.get_dataset() if eval_dataset else None       ,				compute_metrics=SCREAMING_SNAKE_CASE       ,				)

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir     )

				# Evaluation
				__lowercase								=   {}
				if training_args.do_eval:
								logger.info('*** Evaluate ***'     )

								__lowercase								=   trainer.evaluate()
								__lowercase								=   os.path.join(training_args.output_dir       ,				'eval_results.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												logger.info('***** Eval results *****'     )

												for key, value in result.items():
																logger.info('  %s = %s'       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
																writer.write('%s = %s\n' % (key, value)     )

												results.update(SCREAMING_SNAKE_CASE     )

    # Predict
				if training_args.do_predict:
								__lowercase								=   TFTokenClassificationDataset(
								    token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				model_type=config.model_type       ,				max_seq_length=data_args.max_seq_length       ,				overwrite_cache=data_args.overwrite_cache       ,				mode=Split.test       ,				)

								__lowercase						,   __lowercase						,   __lowercase								=   trainer.predict(test_dataset.get_dataset()     )
								__lowercase						,   __lowercase								=   align_predictions(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
								__lowercase								=   classification_report(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )

								logger.info('\n%s'       ,				SCREAMING_SNAKE_CASE     )

								__lowercase								=   os.path.join(training_args.output_dir       ,				'test_results.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												writer.write('%s\n' % report     )

								# Save predictions
								__lowercase								=   os.path.join(training_args.output_dir       ,				'test_predictions.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												with open(os.path.join(data_args.data_dir       ,				'test.txt'     )       ,				'r'     ) as f:
																__lowercase								=   0

																for line in f:
																				if line.startswith('-DOCSTART-'     ) or line == "" or line == "\n":
																								writer.write(SCREAMING_SNAKE_CASE     )

																								if not preds_list[example_id]:
																												example_id += 1
																				elif preds_list[example_id]:
																								__lowercase								=   line.split()[0] + ' ' + preds_list[example_id].pop(0     ) + '\n'

																								writer.write(SCREAMING_SNAKE_CASE     )
																				else:
																								logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'       ,				line.split()[0]     )

				return results


if __name__ == "__main__":
							main()





