



import argparse
import struct
import unittest


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       List[str]				,					_snake_case					:       bytes					):






								"""simple docstring"""


								A__       =    data

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

								# Initialize round constants
								A__       =    [
								    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,
								]

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



					@staticmethod
					def     _a							(     _snake_case					:       bytes					):






								"""simple docstring"""


								A__       =    B'\x80' + (B'\x00' * (63 - (len(_snake_case					) + 8) % 64))
								A__       =    struct.pack('>Q'				,					(len(_snake_case					) * 8)					)
								return data + padding + big_endian_integer



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    [
								    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
											A__       =    list(struct.unpack('>16L'				,					_snake_case					)					)
											# add 48 0-ed integers
											words += [0] * 48

											A__     , A__     , A__     , A__     , A__     , A__     , A__     , A__       =    self.hashes

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

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

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

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

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

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

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



					def     _a							(     self					:       Dict				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)







class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def     _a							(     self					:       str					):






								"""simple docstring"""


								import hashlib

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







def A						(       )						->      None:
			import doctest

			doctest.testmod()

			A__       =    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'					)

			A__       =    parser.parse_args()

			A__       =    args.input_string

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

			print(SHAaaa(__UpperCamelCase					).hash					)


if __name__ == "__main__":
							main()









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()




