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
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-classification/requirements.txt")

A_	     =   list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A_	     =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 							__UpperCAmelCase     (							UpperCAmelCase							)->							Dict:



			"""simple docstring"""







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






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


									'''simple docstring'''

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






def 							__UpperCAmelCase     (							UpperCAmelCase							)->							List[Any]:



			"""simple docstring"""







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






def 							__UpperCAmelCase     (							)->							Optional[Any]:



			"""simple docstring"""







			lowercase       		=		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.
						lowercase		,lowercase		,lowercase       		=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
			else:
						lowercase		,lowercase		,lowercase       		=		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()

			lowercase       		=		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.
			lowercase       		=		None
			if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
						lowercase       		=		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:
						lowercase       		=		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:
						lowercase       		=		{}
						if data_args.train_dir is not None:
									lowercase       		=		os.path.join(data_args.train_dir,		'''**'''							)
						if data_args.validation_dir is not None:
									lowercase       		=		os.path.join(data_args.validation_dir,		'''**'''							)
						lowercase       		=		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.
			lowercase       		=		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:
						lowercase       		=		dataset['''train'''].train_test_split(data_args.train_val_split							)
						lowercase       		=		split['''train''']
						lowercase       		=		split['''test''']

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

			# Load the accuracy metric from the datasets package
			lowercase       		=		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							)

			lowercase       		=		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,		)
			lowercase       		=		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,		)
			lowercase       		=		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:
						lowercase       		=		image_processor.size['''shortest_edge''']
			else:
						lowercase       		=		(image_processor.size['''height'''], image_processor.size['''width'''])
			lowercase       		=		Normalize(mean=image_processor.image_mean,		std=image_processor.image_std							)
			lowercase       		=		Compose(
			    [
			        RandomResizedCrop(UpperCamelCase__							),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]							)
			lowercase       		=		Compose(
			    [
			        Resize(UpperCamelCase__							),
			        CenterCrop(UpperCamelCase__							),
			        ToTensor(),
			        normalize,
			    ]							)

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

			def val_transforms(UpperCAmelCase							):
						lowercase       		=		[_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:
									lowercase       		=		(
									    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:
									lowercase       		=		(
									    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
			lowercase       		=		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:
						lowercase       		=		None
						if training_args.resume_from_checkpoint is not None:
									lowercase       		=		training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									lowercase       		=		last_checkpoint
						lowercase       		=		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:
						lowercase       		=		trainer.evaluate()
						trainer.log_metrics('''eval''',		UpperCamelCase__							)
						trainer.save_metrics('''eval''',		UpperCamelCase__							)

			# Write model card and (optionally) push to hub
			lowercase       		=		{
			    '''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 argparse
import struct
import unittest







class 					lowercase							:




				def __init__(   self ,  snake_case			):
											snake_case_     							=       data

											# Initialize hash values
											snake_case_     							=       [
											    0x6A09E667,
											    0xBB67AE85,
											    0x3C6EF372,
											    0xA54FF53A,
											    0x510E527F,
											    0x9B05688C,
											    0x1F83D9AB,
											    0x5BE0CD19,
											]

											# Initialize round constants
											snake_case_     							=       [
											    0x428A2F98,
											    0x71374491,
											    0xB5C0FBCF,
											    0xE9B5DBA5,
											    0x3956C25B,
											    0x59F111F1,
											    0x923F82A4,
											    0xAB1C5ED5,
											    0xD807AA98,
											    0x12835B01,
											    0x243185BE,
											    0x550C7DC3,
											    0x72BE5D74,
											    0x80DEB1FE,
											    0x9BDC06A7,
											    0xC19BF174,
											    0xE49B69C1,
											    0xEFBE4786,
											    0x0FC19DC6,
											    0x240CA1CC,
											    0x2DE92C6F,
											    0x4A7484AA,
											    0x5CB0A9DC,
											    0x76F988DA,
											    0x983E5152,
											    0xA831C66D,
											    0xB00327C8,
											    0xBF597FC7,
											    0xC6E00BF3,
											    0xD5A79147,
											    0x06CA6351,
											    0x14292967,
											    0x27B70A85,
											    0x2E1B2138,
											    0x4D2C6DFC,
											    0x53380D13,
											    0x650A7354,
											    0x766A0ABB,
											    0x81C2C92E,
											    0x92722C85,
											    0xA2BFE8A1,
											    0xA81A664B,
											    0xC24B8B70,
											    0xC76C51A3,
											    0xD192E819,
											    0xD6990624,
											    0xF40E3585,
											    0x106AA070,
											    0x19A4C116,
											    0x1E376C08,
											    0x2748774C,
											    0x34B0BCB5,
											    0x391C0CB3,
											    0x4ED8AA4A,
											    0x5B9CCA4F,
											    0x682E6FF3,
											    0x748F82EE,
											    0x78A5636F,
											    0x84C87814,
											    0x8CC70208,
											    0x90BEFFFA,
											    0xA4506CEB,
											    0xBEF9A3F7,
											    0xC67178F2,
											]

											snake_case_     							=       self.preprocessing(self.data			)
											self.final_hash()




				@staticmethod
				def        a				(   snake_case			):
											snake_case_     							=       b'\x80' + (b'\x00' * (63 - (len(snake_case			) + 8) % 64))
											snake_case_     							=       struct.pack('>Q' ,  (len(snake_case			) * 8)			)
											return data + padding + big_endian_integer




				def        a				(   self			):
											# Convert into blocks of 64 bytes
											snake_case_     							=       [
											    self.preprocessed_data[x : x + 64]
											    for x in range(0 ,  len(self.preprocessed_data			) ,  64			)
											]

											for block in self.blocks:
																		# Convert the given block into a list of 4 byte integers
																		snake_case_     							=       list(struct.unpack('>16L' ,  snake_case			)			)
																		# add 48 0-ed integers
																		words += [0] * 48

																		snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_     							=       self.hashes

																		for index in range(0 ,  64			):
																									if index > 15:
																																# modify the zero-ed indexes at the end of the array
																																snake_case_     							=       (
																																    self.ror(words[index - 15] ,  7			)
																																    ^ self.ror(words[index - 15] ,  18			)
																																    ^ (words[index - 15] >> 3)
																																)
																																snake_case_     							=       (
																																    self.ror(words[index - 2] ,  17			)
																																    ^ self.ror(words[index - 2] ,  19			)
																																    ^ (words[index - 2] >> 10)
																																)

																																snake_case_     							=       (
																																    words[index - 16] + sa + words[index - 7] + sa
																																) % 0x100000000

																									# Compression
																									snake_case_     							=       self.ror(snake_case ,  6			) ^ self.ror(snake_case ,  11			) ^ self.ror(snake_case ,  25			)
																									snake_case_     							=       (e & f) ^ ((~e & 0xFFFFFFFF) & g)
																									snake_case_     							=       (
																									    h + sa + ch + self.round_constants[index] + words[index]
																									) % 0x100000000
																									snake_case_     							=       self.ror(snake_case ,  2			) ^ self.ror(snake_case ,  13			) ^ self.ror(snake_case ,  22			)
																									snake_case_     							=       (a & b) ^ (a & c) ^ (b & c)
																									snake_case_     							=       (sa + maj) % 0x100000000

																									snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_     							=       (
																									    g,
																									    f,
																									    e,
																									    ((d + tempa) % 0x100000000),
																									    c,
																									    b,
																									    a,
																									    ((tempa + tempa) % 0x100000000),
																									)

																		snake_case_     							=       [a, b, c, d, e, f, g, h]

																		# Modify final values
																		snake_case_     							=       [
																		    ((element + mutated_hash_values[index]) % 0x100000000)
																		    for index, element in enumerate(self.hashes			)
																		]

											snake_case_     							=       ''.join([hex(snake_case			)[2:].zfill(8			) for value in self.hashes]			)




				def        a				(   self ,  snake_case ,  snake_case			):
											return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											import hashlib

											snake_case_     							=       bytes('Test String' ,  'utf-8'			)
											self.assertEqual(SHAaaa(snake_case			).hash ,  hashlib.shaaaa(snake_case			).hexdigest()			)





def    __lowerCamelCase      (					):







							'''simple docstring'''






							import doctest

							doctest.testmod()

							snake_case_     							=       argparse.ArgumentParser()
							parser.add_argument(
							    '-s'  ,				'--string'  ,				dest='input_string'  ,				default='Hello World!! Welcome to Cryptography'  ,				help='Hash the string'  ,				)
							parser.add_argument(
							    '-f'  ,				'--file'  ,				dest='input_file'  ,				help='Hash contents of a file' )

							snake_case_     							=       parser.parse_args()

							snake_case_     							=       args.input_string

							# hash input should be a bytestring
							if args.input_file:
														with open(args.input_file  ,				'rb' ) as f:
																					snake_case_     							=       f.read()
							else:
														snake_case_     							=       bytes(UpperCamelCase__  ,				'utf-8' )

							print(SHAaaa(UpperCamelCase__ ).hash )


if __name__ == "__main__":
					main()




