



def A						(       __UpperCamelCase					)						->      list:

			# bit count represents no. of bits in the gray code
			if bit_count < 0:
						raise ValueError('The given input must be positive'					)

			# get the generated string sequence
			A__       =    gray_code_sequence_string(__UpperCamelCase					)
			#
			# convert them to integers
			for i in range(len(__UpperCamelCase					)					):
						A__       =    int(sequence[i] ,	2					)

			return sequence


def A						(       __UpperCamelCase					)						->      list:

			# The approach is a recursive one
			# Base case achieved when either n = 0 or n=1
			if bit_count == 0:
						return ["0"]

			if bit_count == 1:
						return ["0", "1"]

			A__       =    1 << bit_count  # defines the length of the sequence
			# 1<< n is equivalent to 2^n

			# recursive answer will generate answer for n-1 bits
			A__       =    gray_code_sequence_string(bit_count - 1					)

			A__       =    []

			# append 0 to first half of the smaller sequence generated
			for i in range(seq_len // 2					):
						A__       =    '0' + smaller_sequence[i]
						sequence.append(__UpperCamelCase					)

			# append 1 to second half ... start from the end of the list
			for i in reversed(range(seq_len // 2					)					):
						A__       =    '1' + smaller_sequence[i]
						sequence.append(__UpperCamelCase					)

			return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()









import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE__       =      logging.getLogger(__name__)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			return (preds == labels).mean()


@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		str			       =    field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,     )







@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		str			       =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()      )}      )
					A__				:		str			       =    field(metadata={"help": "Should contain the data files for the task."}      )
					A__				:		int			       =    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."
					        )
					    }							,     )
					A__				:		bool			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Overwrite the cached training and evaluation sets"}      )







def A						(       )						->      Any:
			# 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.

			A__       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
			A__     , A__     , A__       =    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' ,	__UpperCamelCase					)

			# Set seed
			set_seed(training_args.seed					)

			try:
						A__       =    processors[data_args.task_name]()
						A__       =    processor.get_labels()
						A__       =    len(__UpperCamelCase					)
			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.

			A__       =    AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	num_labels=__UpperCamelCase ,	finetuning_task=data_args.task_name ,	cache_dir=model_args.cache_dir ,	)
			A__       =    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 ,	)
			A__       =    AutoModelForMultipleChoice.from_pretrained(
			    model_args.model_name_or_path ,	from_tf=bool('.ckpt' in model_args.model_name_or_path					) ,	config=__UpperCamelCase ,	cache_dir=model_args.cache_dir ,	)

			# Get datasets
			A__       =    (
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir ,	tokenizer=__UpperCamelCase ,	task=data_args.task_name ,	max_seq_length=data_args.max_seq_length ,	overwrite_cache=data_args.overwrite_cache ,	mode=Split.train ,	)
			    if training_args.do_train
			    else None
			)
			A__       =    (
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir ,	tokenizer=__UpperCamelCase ,	task=data_args.task_name ,	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 compute_metrics(__UpperCamelCase					) -> Dict:
						A__       =    np.argmax(p.predictions ,	axis=1					)
						return {"acc": simple_accuracy(__UpperCamelCase ,	p.label_ids					)}

			# Data collator
			A__       =    DataCollatorWithPadding(__UpperCamelCase ,	pad_to_multiple_of=8					) if training_args.fpaa else None

			# Initialize our Trainer
			A__       =    Trainer(
			    model=__UpperCamelCase ,	args=__UpperCamelCase ,	train_dataset=__UpperCamelCase ,	eval_dataset=__UpperCamelCase ,	compute_metrics=__UpperCamelCase ,	data_collator=__UpperCamelCase ,	)

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

						A__       =    trainer.evaluate()

						A__       =    os.path.join(training_args.output_dir ,	'eval_results.txt'					)
						if trainer.is_world_master():
									with open(__UpperCamelCase ,	'w'					) as writer:
												logger.info('***** Eval results *****'					)
												for key, value in result.items():
															logger.info('  %s = %s' ,	__UpperCamelCase ,	__UpperCamelCase					)
															writer.write('%s = %s\n' % (key, value)					)

												results.update(__UpperCamelCase					)

			return results


def A						(       __UpperCamelCase					)						->      List[Any]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()




