

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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A											=     logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')


@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default="""cifar10"""							,						metadata={"""help""": """Name of a dataset from the datasets package"""}							)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}							)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """The column name of the images in the files."""}							)
			__A						  =  field(default=__snake_case							,						metadata={"""help""": """A folder containing the training data."""}							)
			__A						  =  field(default=__snake_case							,						metadata={"""help""": """A folder containing the validation data."""}							)
			__A						  =  field(
			    default=0.15							,						metadata={"""help""": """Percent to split off of train for validation."""}							)
			__A						  =  field(
			    default=__snake_case							,						metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    }							,						)
			__A						  =  field(
			    default=__snake_case							,						metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    }							,						)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= {}
				if self.train_dir is not None:
					snake_case_      		= self.train_dir
				if self.validation_dir is not None:
					snake_case_      		= self.validation_dir
				snake_case_      		= data_files if data_files else None







@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default=__snake_case							,						metadata={
			        """help""": (
			            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
			        )
			    }							,						)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}							)
			__A						  =  field(
			    default=__snake_case							,						metadata={
			        """help""": (
			            """Override some existing default config settings when a model is trained from scratch. Example: """
			            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
			        )
			    }							,						)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}							)
			__A						  =  field(
			    default="""main"""							,						metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,						)
			__A						  =  field(default=__snake_case							,						metadata={"""help""": """Name or path of preprocessor config."""}							)
			__A						  =  field(
			    default=__snake_case							,						metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }							,						)
			__A						  =  field(
			    default=0.75							,						metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}							)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}							)







@dataclass
class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  field(
			    default=1E-3							,						metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}							)







def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= torch.stack([example['pixel_values'] for example in examples]	)
	return {"pixel_values": pixel_values}





def a():





	'''simple docstring'''
	# 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.

	snake_case_      		= HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)	)
	if len(sys.argv	) == 2 and sys.argv[1].endswith('.json'	):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		snake_case_		,       snake_case_		,       snake_case_      		= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
	else:
		snake_case_		,       snake_case_		,       snake_case_      		= parser.parse_args_into_dataclasses()

	# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
	# information sent is the one passed as arguments along with your Python/PyTorch versions.
	send_example_telemetry('run_mae'			, lowercase__			, lowercase__	)

	# Setup logging
	logging.basicConfig(
	    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			, datefmt='%m/%d/%Y %H:%M:%S'			, handlers=[logging.StreamHandler(sys.stdout	)]			, )

	if training_args.should_log:
		# The default of training_args.log_level is passive, so we set log level at info here to have that default.
		transformers.utils.logging.set_verbosity_info()

	snake_case_      		= training_args.get_process_log_level()
	logger.setLevel(lowercase__	)
	transformers.utils.logging.set_verbosity(lowercase__	)
	transformers.utils.logging.enable_default_handler()
	transformers.utils.logging.enable_explicit_format()

	# Log on each process the small summary:
	logger.warning(
	    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
	    + f"""distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}"""	)
	logger.info(f"""Training/evaluation parameters {training_args}"""	)

	# Detecting last checkpoint.
	snake_case_      		= None
	if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
		snake_case_      		= get_last_checkpoint(training_args.output_dir	)
		if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
			raise ValueError(
			    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
			    'Use --overwrite_output_dir to overcome.'	)
		elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
			logger.info(
			    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
			    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'	)

    # Initialize our dataset.
	snake_case_      		= load_dataset(
	    data_args.dataset_name			, data_args.dataset_config_name			, data_files=data_args.data_files			, cache_dir=model_args.cache_dir			, use_auth_token=True if model_args.use_auth_token else None			, )

	# If we don't have a validation split, split off a percentage of train as validation.
	snake_case_      		= None if 'validation' in ds.keys() else data_args.train_val_split
	if isinstance(data_args.train_val_split			, lowercase__	) and data_args.train_val_split > 0.0:
		snake_case_      		= ds['train'].train_test_split(data_args.train_val_split	)
		snake_case_      		= split['train']
		snake_case_      		= split['test']

	# Load pretrained model and image processor
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	snake_case_      		= {
	    'cache_dir': model_args.cache_dir,
	    'revision': model_args.model_revision,
	    'use_auth_token': True if model_args.use_auth_token else None,
	}
	if model_args.config_name:
		snake_case_      		= ViTMAEConfig.from_pretrained(model_args.config_name			, **lowercase__	)
	elif model_args.model_name_or_path:
		snake_case_      		= ViTMAEConfig.from_pretrained(model_args.model_name_or_path			, **lowercase__	)
	else:
		snake_case_      		= ViTMAEConfig()
		logger.warning('You are instantiating a new config instance from scratch.'	)
		if model_args.config_overrides is not None:
			logger.info(f"""Overriding config: {model_args.config_overrides}"""	)
			config.update_from_string(model_args.config_overrides	)
			logger.info(f"""New config: {config}"""	)

    # adapt config
	config.update(
	    {
	        'mask_ratio': model_args.mask_ratio,
	        'norm_pix_loss': model_args.norm_pix_loss,
	    }	)

	# create image processor
	if model_args.image_processor_name:
		snake_case_      		= ViTImageProcessor.from_pretrained(model_args.image_processor_name			, **lowercase__	)
	elif model_args.model_name_or_path:
		snake_case_      		= ViTImageProcessor.from_pretrained(model_args.model_name_or_path			, **lowercase__	)
	else:
		snake_case_      		= ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		snake_case_      		= ViTMAEForPreTraining.from_pretrained(
		    model_args.model_name_or_path			, from_tf=bool('.ckpt' in model_args.model_name_or_path	)			, config=lowercase__			, cache_dir=model_args.cache_dir			, revision=model_args.model_revision			, use_auth_token=True if model_args.use_auth_token else None			, )
	else:
		logger.info('Training new model from scratch'	)
		snake_case_      		= ViTMAEForPreTraining(lowercase__	)

	if training_args.do_train:
		snake_case_      		= ds['train'].column_names
	else:
		snake_case_      		= ds['validation'].column_names

	if data_args.image_column_name is not None:
		snake_case_      		= data_args.image_column_name
	elif "image" in column_names:
		snake_case_      		= 'image'
	elif "img" in column_names:
		snake_case_      		= 'img'
	else:
		snake_case_      		= column_names[0]

	# transformations as done in original MAE paper
	# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
	if "shortest_edge" in image_processor.size:
		snake_case_      		= image_processor.size['shortest_edge']
	else:
		snake_case_      		= (image_processor.size['height'], image_processor.size['width'])
	snake_case_      		= Compose(
	    [
	        Lambda(lambda lowercase__	: img.convert('RGB'	) if img.mode != "RGB" else img	),
	        RandomResizedCrop(lowercase__			, scale=(0.2, 1.0)			, interpolation=InterpolationMode.BICUBIC	),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        Normalize(mean=image_processor.image_mean			, std=image_processor.image_std	),
	    ]	)

	def preprocess_images(lowercase__	):
		snake_case_      		= [transforms(lowercase__	) for image in examples[image_column_name]]
		return examples

	if training_args.do_train:
		if "train" not in ds:
			raise ValueError('--do_train requires a train dataset'	)
		if data_args.max_train_samples is not None:
			snake_case_      		= ds['train'].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
		# Set the training transforms
		ds["train"].set_transform(lowercase__	)

	if training_args.do_eval:
		if "validation" not in ds:
			raise ValueError('--do_eval requires a validation dataset'	)
		if data_args.max_eval_samples is not None:
			snake_case_      		= (
			    ds['validation'].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
			)
		# Set the validation transforms
		ds["validation"].set_transform(lowercase__	)

	# Compute absolute learning rate
	snake_case_      		= (
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		snake_case_      		= training_args.base_learning_rate * total_train_batch_size / 256

	# Initialize our trainer
	snake_case_      		= Trainer(
	    model=lowercase__			, args=lowercase__			, train_dataset=ds['train'] if training_args.do_train else None			, eval_dataset=ds['validation'] if training_args.do_eval else None			, tokenizer=lowercase__			, data_collator=lowercase__			, )

	# Training
	if training_args.do_train:
		snake_case_      		= None
		if training_args.resume_from_checkpoint is not None:
			snake_case_      		= training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			snake_case_      		= last_checkpoint
		snake_case_      		= trainer.train(resume_from_checkpoint=lowercase__	)
		trainer.save_model()
		trainer.log_metrics('train'			, train_result.metrics	)
		trainer.save_metrics('train'			, train_result.metrics	)
		trainer.save_state()

	# Evaluation
	if training_args.do_eval:
		snake_case_      		= trainer.evaluate()
		trainer.log_metrics('eval'			, lowercase__	)
		trainer.save_metrics('eval'			, lowercase__	)

	# Write model card and (optionally) push to hub
	snake_case_      		= {
	    'tasks': 'masked-auto-encoding',
	    'dataset': data_args.dataset_name,
	    'tags': ['masked-auto-encoding'],
	}
	if training_args.push_to_hub:
		trainer.push_to_hub(**lowercase__	)
	else:
		trainer.create_model_card(**lowercase__	)





def a(lowercase__	):





	'''simple docstring'''
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()



from collections import defaultdict





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= first_str.lower().strip()
	snake_case_      		= second_str.lower().strip()

	# Remove whitespace
	snake_case_      		= first_str.replace(' '			, ''	)
	snake_case_      		= second_str.replace(' '			, ''	)

	# Strings of different lengths are not anagrams
	if len(lowercase__	) != len(lowercase__	):
		return False

	# Default values for count should be 0
	snake_case_      		= defaultdict(lowercase__	)

	# For each character in input strings,
	# increment count in the corresponding
	for i in range(len(lowercase__	)	):
		count[first_str[i]] += 1
		count[second_str[i]] -= 1

	return all(_count == 0 for _count in count.values()	)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		A											=     input('Enter the first string ').strip()
		A											=     input('Enter the second string ').strip()

		A											=     check_anagrams(input_a, input_b)
		print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
