

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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
_UpperCAmelCase						:  Optional[int]									=			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-classification/requirements.txt""")

_UpperCAmelCase						:  Union[str, Any]									=			list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_UpperCAmelCase						:  Any									=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							with open(UpperCamelCase__  ,				'rb' ) as f:
														snake_case_     							=       Image.open(UpperCamelCase__ )
														return im.convert('RGB' )







@dataclass
class 					lowercase							:
				__SCREAMING_SNAKE_CASE   :					Optional[str]			     =     field(
				    default=lowercase_      ,    metadata={
				        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
				    }      ,    )
				__SCREAMING_SNAKE_CASE   :					Optional[str]			     =     field(
				    default=lowercase_      ,    metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}     )
				__SCREAMING_SNAKE_CASE   :					Optional[str]			     =     field(default=lowercase_      ,    metadata={'''help''': '''A folder containing the training data.'''}     )
				__SCREAMING_SNAKE_CASE   :					Optional[str]			     =     field(default=lowercase_      ,    metadata={'''help''': '''A folder containing the validation data.'''}     )
				__SCREAMING_SNAKE_CASE   :					Optional[float]			     =     field(
				    default=0.15      ,    metadata={'''help''': '''Percent to split off of train for validation.'''}     )
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     field(
				    default=lowercase_      ,    metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }      ,    )
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     field(
				    default=lowercase_      ,    metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
				            '''value if set.'''
				        )
				    }      ,    )




				def        a				(   self			):
											if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
																		raise ValueError(
																		    'You must specify either a dataset name from the hub or a train and/or validation directory.'			)







@dataclass
class 					lowercase							:
				__SCREAMING_SNAKE_CASE   :					str			     =     field(
				    default='''google/vit-base-patch16-224-in21k'''      ,    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      ,    )
				__SCREAMING_SNAKE_CASE   :					Optional[str]			     =     field(
				    default=lowercase_      ,    metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowercase_     )}      ,    )
				__SCREAMING_SNAKE_CASE   :					Optional[str]			     =     field(
				    default=lowercase_      ,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
				__SCREAMING_SNAKE_CASE   :					Optional[str]			     =     field(
				    default=lowercase_      ,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}     )
				__SCREAMING_SNAKE_CASE   :					str			     =     field(
				    default='''main'''      ,    metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}      ,    )
				__SCREAMING_SNAKE_CASE   :					str			     =     field(default=lowercase_      ,    metadata={'''help''': '''Name or path of preprocessor config.'''}     )
				__SCREAMING_SNAKE_CASE   :					bool			     =     field(
				    default=lowercase_      ,    metadata={
				        '''help''': (
				            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
				            '''with private models).'''
				        )
				    }      ,    )
				__SCREAMING_SNAKE_CASE   :					bool			     =     field(
				    default=lowercase_      ,    metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}      ,    )





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					):







							'''simple docstring'''






							snake_case_     							=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
							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_image_classification'  ,				UpperCamelCase__  ,				UpperCamelCase__ )

							# 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(UpperCamelCase__ )
							transformers.utils.logging.set_verbosity(UpperCamelCase__ )
							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.' )

    # Set seed before initializing model.
							set_seed(training_args.seed )

							# Initialize our dataset and prepare it for the 'image-classification' task.
							if data_args.dataset_name is not None:
														snake_case_     							=       load_dataset(
														    data_args.dataset_name  ,				data_args.dataset_config_name  ,				cache_dir=model_args.cache_dir  ,				task='image-classification'  ,				use_auth_token=True if model_args.use_auth_token else None  ,				)
							else:
														snake_case_     							=       {}
														if data_args.train_dir is not None:
																					snake_case_     							=       os.path.join(data_args.train_dir  ,				'**' )
														if data_args.validation_dir is not None:
																					snake_case_     							=       os.path.join(data_args.validation_dir  ,				'**' )
														snake_case_     							=       load_dataset(
														    'imagefolder'  ,				data_files=UpperCamelCase__  ,				cache_dir=model_args.cache_dir  ,				task='image-classification'  ,				)

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

							# Prepare label mappings.
							# We'll include these in the model's config to get human readable labels in the Inference API.
							snake_case_     							=       dataset['train'].features['labels'].names
							snake_case_							,       snake_case_     							=       {}, {}
							for i, label in enumerate(UpperCamelCase__ ):
														snake_case_     							=       str(UpperCamelCase__ )
														snake_case_     							=       label

							# Load the accuracy metric from the datasets package
							snake_case_     							=       evaluate.load('accuracy' )

							# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(UpperCamelCase__ ):
														return metric.compute(predictions=np.argmax(p.predictions  ,				axis=1 )  ,				references=p.label_ids )

							snake_case_     							=       AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path  ,				num_labels=len(UpperCamelCase__ )  ,				labelaid=UpperCamelCase__  ,				idalabel=UpperCamelCase__  ,				finetuning_task='image-classification'  ,				cache_dir=model_args.cache_dir  ,				revision=model_args.model_revision  ,				use_auth_token=True if model_args.use_auth_token else None  ,				)
							snake_case_     							=       AutoModelForImageClassification.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  ,				revision=model_args.model_revision  ,				use_auth_token=True if model_args.use_auth_token else None  ,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes  ,				)
							snake_case_     							=       AutoImageProcessor.from_pretrained(
							    model_args.image_processor_name or model_args.model_name_or_path  ,				cache_dir=model_args.cache_dir  ,				revision=model_args.model_revision  ,				use_auth_token=True if model_args.use_auth_token else None  ,				)

							# Define torchvision transforms to be applied to each image.
							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_     							=       Normalize(mean=image_processor.image_mean  ,				std=image_processor.image_std )
							snake_case_     							=       Compose(
							    [
							        RandomResizedCrop(UpperCamelCase__ ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ] )
							snake_case_     							=       Compose(
							    [
							        Resize(UpperCamelCase__ ),
							        CenterCrop(UpperCamelCase__ ),
							        ToTensor(),
							        normalize,
							    ] )

							def train_transforms(UpperCamelCase__ ):
														snake_case_     							=       [
														    _train_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']
														]
														return example_batch

							def val_transforms(UpperCamelCase__ ):
														snake_case_     							=       [_val_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']]
														return example_batch

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

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

							# Initalize our trainer
							snake_case_     							=       Trainer(
							    model=UpperCamelCase__  ,				args=UpperCamelCase__  ,				train_dataset=dataset['train'] if training_args.do_train else None  ,				eval_dataset=dataset['validation'] if training_args.do_eval else None  ,				compute_metrics=UpperCamelCase__  ,				tokenizer=UpperCamelCase__  ,				data_collator=UpperCamelCase__  ,				)

							# 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=UpperCamelCase__ )
														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'  ,				UpperCamelCase__ )
														trainer.save_metrics('eval'  ,				UpperCamelCase__ )

							# Write model card and (optionally) push to hub
							snake_case_     							=       {
							    'finetuned_from': model_args.model_name_or_path,
							    'tasks': 'image-classification',
							    'dataset': data_args.dataset_name,
							    'tags': ['image-classification', 'vision'],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**UpperCamelCase__ )
							else:
														trainer.create_model_card(**UpperCamelCase__ )


if __name__ == "__main__":
					main()







import string





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       ''
							for i in sequence:
														snake_case_     							=       ord(UpperCamelCase__ )
														if 65 <= extract <= 90:
																					output += chr(155 - extract )
														elif 97 <= extract <= 122:
																					output += chr(219 - extract )
														else:
																					output += i
							return output





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       string.ascii_letters
							snake_case_     							=       string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(UpperCamelCase__ )] if c in letters else c for c in sequence )





def    __lowerCamelCase      (					):







							'''simple docstring'''






							from timeit import timeit

							print('Running performance benchmarks...' )
							snake_case_     							=       'from string import printable ; from __main__ import atbash, atbash_slow'
							print(F'''> atbash_slow(): {timeit("atbash_slow(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )
							print(F'''>      atbash(): {timeit("atbash(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )


if __name__ == "__main__":
					for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
										print(F'''{example} encrypted in atbash: {atbash(example)}''')
					benchmark()




