
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase :  List[Any]      =      logging.getLogger(__name__)


@dataclass
class        SCREAMING_SNAKE_CASE__	:
				lowerCAmelCase_			       =				field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
				lowerCAmelCase_			       =				field(
				    default=snake_case_				,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
				lowerCAmelCase_			       =				field(
				    default=snake_case_				,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
				lowerCAmelCase_			       =				field(
				    default=snake_case_				,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,    )


@dataclass
class        SCREAMING_SNAKE_CASE__	:
				lowerCAmelCase_			       =				field(
				    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys())})
				lowerCAmelCase_			       =				field(
				    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""})
				lowerCAmelCase_			       =				field(
				    default=1_28				,    metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }				,    )
				lowerCAmelCase_			       =				field(
				    default=snake_case_				,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""})







def 						A_(			A		:						List[InputFeatures]):
							UpperCamelCase					       =			default_data_collator(A)
							UpperCamelCase					       =			batch.pop('pairID'						,			A)
							return batch







def 						A_(			):
							# 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.

							UpperCamelCase					       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
							UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			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.')

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'						,			datefmt='%m/%d/%Y %H:%M:%S'						,			level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,			)
							logger.warning(
							    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'						,			training_args.local_rank						,			training_args.device						,			training_args.n_gpu						,			bool(training_args.local_rank != -1)						,			training_args.fpaa						,			)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info('Training/evaluation parameters %s'						,			A)

							# Set seed
							set_seed(training_args.seed)

							try:
														UpperCamelCase					       =			hans_tasks_num_labels[data_args.task_name]
							except KeyError:
														raise ValueError('Task not found: %s' % (data_args.task_name))

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

							UpperCamelCase					       =			AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,			num_labels=A						,			finetuning_task=data_args.task_name						,			cache_dir=model_args.cache_dir						,			)
							UpperCamelCase					       =			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						,			)
							UpperCamelCase					       =			AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path						,			from_tf=bool('.ckpt' in model_args.model_name_or_path)						,			config=A						,			cache_dir=model_args.cache_dir						,			)

							# Get datasets
							UpperCamelCase					       =			(
							    HansDataset(
							        data_dir=data_args.data_dir						,			tokenizer=A						,			task=data_args.task_name						,			max_seq_length=data_args.max_seq_length						,			overwrite_cache=data_args.overwrite_cache						,			)
							    if training_args.do_train
							    else None
							)
							UpperCamelCase					       =			(
							    HansDataset(
							        data_dir=data_args.data_dir						,			tokenizer=A						,			task=data_args.task_name						,			max_seq_length=data_args.max_seq_length						,			overwrite_cache=data_args.overwrite_cache						,			evaluate=A						,			)
							    if training_args.do_eval
							    else None
							)

							# Initialize our Trainer
							UpperCamelCase					       =			Trainer(
							    model=A						,			args=A						,			train_dataset=A						,			eval_dataset=A						,			data_collator=A						,			)

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
							if training_args.do_eval:
														logger.info('*** Evaluate ***')

														UpperCamelCase					       =			trainer.predict(A)
														UpperCamelCase					       =			output.predictions
														UpperCamelCase					       =			np.argmax(A						,			axis=1)

														UpperCamelCase					       =			[ex.pairID for ex in eval_dataset]
														UpperCamelCase					       =			os.path.join(training_args.output_dir						,			'hans_predictions.txt')
														UpperCamelCase					       =			eval_dataset.get_labels()
														if trainer.is_world_master():
																					with open(A						,			'w') as writer:
																												writer.write('pairID,gold_label\n')
																												for pid, pred in zip(A						,			A):
																																			writer.write('ex' + str(A) + ',' + label_list[int(A)] + '\n')

														trainer._log(output.metrics)







def 						A_(			A		:						Any):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
						main()






'''simple docstring'''
from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
lowerCAmelCase :  Union[str, Any]      =      [[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):
							return [int(A) for c in f'''{ruleset:08}'''[:8]]







def 						A_(			A		:						list[list[int]]						,			A		:						list[int]						,			A		:						int):
							UpperCamelCase					       =			len(cells[0])  # 31
							UpperCamelCase					       =			[]
							for i in range(A):
														# Get the neighbors of each cell
														# Handle neighbours outside bounds by using 0 as their value
														UpperCamelCase					       =			0 if i == 0 else cells[time][i - 1]
														UpperCamelCase					       =			0 if i == population - 1 else cells[time][i + 1]
														# Define a new cell and add it to the new generation
														UpperCamelCase					       =			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]]):
							UpperCamelCase					       =			Image.new('RGB'						,			(len(cells[0]), len(A)))
							UpperCamelCase					       =			img.load()
							# Generates image
							for w in range(img.width):
														for h in range(img.height):
																					UpperCamelCase					       =			255 - int(255 * cells[h][w])
																					UpperCamelCase					       =			(color, color, color)
							return img


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




