
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_A  =	logging.get_logger(__name__)





@dataclass
class       _lowerCAmelCase   :
				_lowercase     						=field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()			)}			)
				_lowercase     						=field(
				    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}			)
				_lowercase     						=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.'''
				        )
				    }   , )
				_lowercase     						=field(
				    default=__a   , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.task_name.lower()





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''train'''
				_lowercase     						='''dev'''
				_lowercase     						='''test'''





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=42
				_lowercase     						=42
				_lowercase     						=42




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = None							,							_UpperCamelCase = Split.train							,							_UpperCamelCase = None							,							)							-> Any:
							warnings.warn(
							    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
							    "library. You can have a look at this example script for pointers: "
							    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"							,							_UpperCamelCase							,							)
							lowerCAmelCase_         =      args
							lowerCAmelCase_         =      glue_processors[args.task_name]()
							lowerCAmelCase_         =      glue_output_modes[args.task_name]
							if isinstance(_UpperCamelCase							,							_UpperCamelCase	):
										try:
													lowerCAmelCase_         =      Split[mode]
										except KeyError:
													raise KeyError("mode is not a valid split name"	)
        # Load data features from cache or dataset file
							lowerCAmelCase_         =      os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir							,							f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""							,							)
							lowerCAmelCase_         =      self.processor.get_labels()
							if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
							    "RobertaTokenizer",
							    "RobertaTokenizerFast",
							    "XLMRobertaTokenizer",
							    "BartTokenizer",
							    "BartTokenizerFast",
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										lowerCAmelCase_      ,    lowerCAmelCase_         =      label_list[2], label_list[1]
							lowerCAmelCase_         =      label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							lowerCAmelCase_         =      cached_features_file + ".lock"
							with FileLock(_UpperCamelCase	):
										if os.path.exists(_UpperCamelCase	) and not args.overwrite_cache:
													lowerCAmelCase_         =      time.time()
													lowerCAmelCase_         =      torch.load(_UpperCamelCase	)
													logger.info(
													    f"""Loading features from cached file {cached_features_file} [took %.3f s]"""							,							time.time() - start	)
										else:
													logger.info(f"""Creating features from dataset file at {args.data_dir}"""	)

													if mode == Split.dev:
																lowerCAmelCase_         =      self.processor.get_dev_examples(args.data_dir	)
													elif mode == Split.test:
																lowerCAmelCase_         =      self.processor.get_test_examples(args.data_dir	)
													else:
																lowerCAmelCase_         =      self.processor.get_train_examples(args.data_dir	)
													if limit_length is not None:
																lowerCAmelCase_         =      examples[:limit_length]
													lowerCAmelCase_         =      glue_convert_examples_to_features(
													    _UpperCamelCase							,							_UpperCamelCase							,							max_length=args.max_seq_length							,							label_list=_UpperCamelCase							,							output_mode=self.output_mode							,							)
													lowerCAmelCase_         =      time.time()
													torch.save(self.features							,							_UpperCamelCase	)
													# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
													logger.info(
													    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""	)




				def __len__(   self	)							-> List[str]:
							return len(self.features	)




				def __getitem__(   self							,							_UpperCamelCase	)							-> InputFeatures:
							return self.features[i]




				def 	__a		(   self	)							-> Union[str, Any]:
							return self.label_list





import argparse
import struct
import unittest





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase	)							-> None:
							lowerCAmelCase_         =      data

							# Initialize hash values
							lowerCAmelCase_         =      [
							    0x6_A_0_9_E_6_6_7,
							    0xB_B_6_7_A_E_8_5,
							    0x3_C_6_E_F_3_7_2,
							    0xA_5_4_F_F_5_3_A,
							    0x5_1_0_E_5_2_7_F,
							    0x9_B_0_5_6_8_8_C,
							    0x1_F_8_3_D_9_A_B,
							    0x5_B_E_0_C_D_1_9,
							]

							# Initialize round constants
							lowerCAmelCase_         =      [
							    0x4_2_8_A_2_F_9_8,
							    0x7_1_3_7_4_4_9_1,
							    0xB_5_C_0_F_B_C_F,
							    0xE_9_B_5_D_B_A_5,
							    0x3_9_5_6_C_2_5_B,
							    0x5_9_F_1_1_1_F_1,
							    0x9_2_3_F_8_2_A_4,
							    0xA_B_1_C_5_E_D_5,
							    0xD_8_0_7_A_A_9_8,
							    0x1_2_8_3_5_B_0_1,
							    0x2_4_3_1_8_5_B_E,
							    0x5_5_0_C_7_D_C_3,
							    0x7_2_B_E_5_D_7_4,
							    0x8_0_D_E_B_1_F_E,
							    0x9_B_D_C_0_6_A_7,
							    0xC_1_9_B_F_1_7_4,
							    0xE_4_9_B_6_9_C_1,
							    0xE_F_B_E_4_7_8_6,
							    0x0_F_C_1_9_D_C_6,
							    0x2_4_0_C_A_1_C_C,
							    0x2_D_E_9_2_C_6_F,
							    0x4_A_7_4_8_4_A_A,
							    0x5_C_B_0_A_9_D_C,
							    0x7_6_F_9_8_8_D_A,
							    0x9_8_3_E_5_1_5_2,
							    0xA_8_3_1_C_6_6_D,
							    0xB_0_0_3_2_7_C_8,
							    0xB_F_5_9_7_F_C_7,
							    0xC_6_E_0_0_B_F_3,
							    0xD_5_A_7_9_1_4_7,
							    0x0_6_C_A_6_3_5_1,
							    0x1_4_2_9_2_9_6_7,
							    0x2_7_B_7_0_A_8_5,
							    0x2_E_1_B_2_1_3_8,
							    0x4_D_2_C_6_D_F_C,
							    0x5_3_3_8_0_D_1_3,
							    0x6_5_0_A_7_3_5_4,
							    0x7_6_6_A_0_A_B_B,
							    0x8_1_C_2_C_9_2_E,
							    0x9_2_7_2_2_C_8_5,
							    0xA_2_B_F_E_8_A_1,
							    0xA_8_1_A_6_6_4_B,
							    0xC_2_4_B_8_B_7_0,
							    0xC_7_6_C_5_1_A_3,
							    0xD_1_9_2_E_8_1_9,
							    0xD_6_9_9_0_6_2_4,
							    0xF_4_0_E_3_5_8_5,
							    0x1_0_6_A_A_0_7_0,
							    0x1_9_A_4_C_1_1_6,
							    0x1_E_3_7_6_C_0_8,
							    0x2_7_4_8_7_7_4_C,
							    0x3_4_B_0_B_C_B_5,
							    0x3_9_1_C_0_C_B_3,
							    0x4_E_D_8_A_A_4_A,
							    0x5_B_9_C_C_A_4_F,
							    0x6_8_2_E_6_F_F_3,
							    0x7_4_8_F_8_2_E_E,
							    0x7_8_A_5_6_3_6_F,
							    0x8_4_C_8_7_8_1_4,
							    0x8_C_C_7_0_2_0_8,
							    0x9_0_B_E_F_F_F_A,
							    0xA_4_5_0_6_C_E_B,
							    0xB_E_F_9_A_3_F_7,
							    0xC_6_7_1_7_8_F_2,
							]

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




				@staticmethod
				def 	__a		(   _UpperCamelCase	)							-> bytes:
							lowerCAmelCase_         =      B"\x80" + (B"\x00" * (63 - (len(_UpperCamelCase	) + 8) % 64))
							lowerCAmelCase_         =      struct.pack(">Q"							,							(len(_UpperCamelCase	) * 8)	)
							return data + padding + big_endian_integer




				def 	__a		(   self	)							-> None:
							# Convert into blocks of 64 bytes
							lowerCAmelCase_         =      [
							    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
										lowerCAmelCase_         =      list(struct.unpack(">16L"							,							_UpperCamelCase	)	)
										# add 48 0-ed integers
										words += [0] * 48

										lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      self.hashes

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

																lowerCAmelCase_         =      (
																    words[index - 16] + sa + words[index - 7] + sa
																) % 0x1_0_0_0_0_0_0_0_0

													# Compression
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							6	) ^ self.ror(_UpperCamelCase							,							11	) ^ self.ror(_UpperCamelCase							,							25	)
													lowerCAmelCase_         =      (e & f) ^ ((~e & 0xF_F_F_F_F_F_F_F) & g)
													lowerCAmelCase_         =      (
													    h + sa + ch + self.round_constants[index] + words[index]
													) % 0x1_0_0_0_0_0_0_0_0
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							2	) ^ self.ror(_UpperCamelCase							,							13	) ^ self.ror(_UpperCamelCase							,							22	)
													lowerCAmelCase_         =      (a & b) ^ (a & c) ^ (b & c)
													lowerCAmelCase_         =      (sa + maj) % 0x1_0_0_0_0_0_0_0_0

													lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      (
													    g,
													    f,
													    e,
													    ((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
													    c,
													    b,
													    a,
													    ((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
													)

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

										# Modify final values
										lowerCAmelCase_         =      [
										    ((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
										    for index, element in enumerate(self.hashes	)
										]

							lowerCAmelCase_         =      "".join([hex(_UpperCamelCase	)[2:].zfill(8	) for value in self.hashes]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							return 0xF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)





class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> None:
							import hashlib

							lowerCAmelCase_         =      bytes("Test String"							,							"utf-8"	)
							self.assertEqual(SHAaaa(_UpperCamelCase	).hash							,							hashlib.shaaaa(_UpperCamelCase	).hexdigest()	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


			import doctest

			doctest.testmod()

			lowerCAmelCase_         =      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"							)

			lowerCAmelCase_         =      parser.parse_args()

			lowerCAmelCase_         =      args.input_string

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

			print(SHAaaa(__lowerCAmelCase							).hash							)


if __name__ == "__main__":
			main()



