






'''simple docstring'''




import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowerCAmelCase__          =						logging.getLogger(__name__)







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  '''token-classification'''

	def __init__(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						Union[str, Any]):
			if type(UpperCAmelCase_) == dict:
					UpperCamelCase__				:					List[str]					=					Namespace(**UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					import_module('tasks')
			try:
					UpperCamelCase__				:					Optional[int]					=					getattr(UpperCAmelCase_  ,					hparams.task_type)
					UpperCamelCase__				:					TokenClassificationTask					=					token_classification_task_clazz()
			except AttributeError:
					raise ValueError(
					    F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
					    F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}')
			UpperCamelCase__				:					List[str]					=					self.token_classification_task.get_labels(hparams.labels)
			UpperCamelCase__				:					Optional[Any]					=					CrossEntropyLoss().ignore_index
			super().__init__(UpperCAmelCase_  ,					len(self.labels)  ,					self.mode)

	def      __UpperCamelCase							(      self   :						int  ,					**UpperCAmelCase_   :						List[str]):
			return self.model(**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						List[Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Dict					=					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
			if self.config.model_type != "distilbert":
					UpperCamelCase__				:					Optional[Any]					=					(
					    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
					)  # XLM and RoBERTa don"t use token_type_ids

			UpperCamelCase__				:					int					=					self(**UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					outputs[0]
			# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
			return {"loss": loss}

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Optional[Any]					=					self.hparams
			for mode in ["train", "dev", "test"]:
					UpperCamelCase__				:					List[str]					=					self._feature_file(UpperCAmelCase_)
					if os.path.exists(UpperCAmelCase_) and not args.overwrite_cache:
							logger.info('Loading features from cached file %s'  ,					UpperCAmelCase_)
							UpperCamelCase__				:					Any					=					torch.load(UpperCAmelCase_)
					else:
							logger.info('Creating features from dataset file at %s'  ,					args.data_dir)
							UpperCamelCase__				:					Union[str, Any]					=					self.token_classification_task.read_examples_from_file(args.data_dir  ,					UpperCAmelCase_)
							UpperCamelCase__				:					Union[str, Any]					=					self.token_classification_task.convert_examples_to_features(
							    UpperCAmelCase_  ,					self.labels  ,					args.max_seq_length  ,					self.tokenizer  ,					cls_token_at_end=bool(self.config.model_type in ['xlnet'])  ,					cls_token=self.tokenizer.cls_token  ,					cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0  ,					sep_token=self.tokenizer.sep_token  ,					sep_token_extra=UpperCAmelCase_  ,					pad_on_left=bool(self.config.model_type in ['xlnet'])  ,					pad_token=self.tokenizer.pad_token_id  ,					pad_token_segment_id=self.tokenizer.pad_token_type_id  ,					pad_token_label_id=self.pad_token_label_id  ,					)
							logger.info('Saving features into cached file %s'  ,					UpperCAmelCase_)
							torch.save(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						bool = False):
			UpperCamelCase__				:					Optional[int]					=					self._feature_file(UpperCAmelCase_)
			logger.info('Loading features from cached file %s'  ,					UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					torch.load(UpperCAmelCase_)
			UpperCamelCase__				:					List[Any]					=					torch.tensor([f.input_ids for f in features]  ,					dtype=torch.long)
			UpperCamelCase__				:					Tuple					=					torch.tensor([f.attention_mask for f in features]  ,					dtype=torch.long)
			if features[0].token_type_ids is not None:
					UpperCamelCase__				:					List[str]					=					torch.tensor([f.token_type_ids for f in features]  ,					dtype=torch.long)
			else:
					UpperCamelCase__				:					int					=					torch.tensor([0 for f in features]  ,					dtype=torch.long)
					# HACK(we will not use this anymore soon)
			UpperCamelCase__				:					str					=					torch.tensor([f.label_ids for f in features]  ,					dtype=torch.long)
			return DataLoader(
			    TensorDataset(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)  ,					batch_size=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Tuple):
			"""Compute validation""" ""
			UpperCamelCase__				:					Union[str, Any]					=					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
			if self.config.model_type != "distilbert":
					UpperCamelCase__				:					List[str]					=					(
					    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
					)  # XLM and RoBERTa don"t use token_type_ids
			UpperCamelCase__				:					Any					=					self(**UpperCAmelCase_)
			UpperCamelCase__,	UpperCamelCase__				:					Optional[Any]					=					outputs[:2]
			UpperCamelCase__				:					Optional[Any]					=					logits.detach().cpu().numpy()
			UpperCamelCase__				:					Dict					=					inputs['labels'].detach().cpu().numpy()
			return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					Optional[int]					=					torch.stack([x['val_loss'] for x in outputs]).mean()
			UpperCamelCase__				:					Tuple					=					np.concatenate([x['pred'] for x in outputs]  ,					axis=0)
			UpperCamelCase__				:					List[Any]					=					np.argmax(UpperCAmelCase_  ,					axis=2)
			UpperCamelCase__				:					Dict					=					np.concatenate([x['target'] for x in outputs]  ,					axis=0)

			UpperCamelCase__				:					Union[str, Any]					=					dict(enumerate(self.labels))
			UpperCamelCase__				:					Optional[Any]					=					[[] for _ in range(out_label_ids.shape[0])]
			UpperCamelCase__				:					Optional[int]					=					[[] for _ in range(out_label_ids.shape[0])]

			for i in range(out_label_ids.shape[0]):
					for j in range(out_label_ids.shape[1]):
							if out_label_ids[i, j] != self.pad_token_label_id:
									out_label_list[i].append(label_map[out_label_ids[i][j]])
									preds_list[i].append(label_map[preds[i][j]])

			UpperCamelCase__				:					Any					=					{
			    'val_loss': val_loss_mean,
			    'accuracy_score': accuracy_score(UpperCAmelCase_  ,					UpperCAmelCase_),
			    'precision': precision_score(UpperCAmelCase_  ,					UpperCAmelCase_),
			    'recall': recall_score(UpperCAmelCase_  ,					UpperCAmelCase_),
			    'f1': fa_score(UpperCAmelCase_  ,					UpperCAmelCase_),
			}

			UpperCamelCase__				:					Any					=					dict(results.items())
			UpperCamelCase__				:					Tuple					=					results
			return ret, preds_list, out_label_list

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						Any):
			# when stable
			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					self._eval_end(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					ret['log']
			return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						List[str]):
			# updating to test_epoch_end instead of deprecated test_end
			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Optional[int]					=					self._eval_end(UpperCAmelCase_)

			# Converting to the dict required by pl
			# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
			# pytorch_lightning/trainer/logging.py#L139
			UpperCamelCase__				:					Any					=					ret['log']
			# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
			return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	@staticmethod
	def      __UpperCamelCase							(      UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Optional[int]):
			# Add NER specific options
			BaseTransformer.add_model_specific_args(UpperCAmelCase_  ,					UpperCAmelCase_)
			parser.add_argument(
			    '--task_type'  ,					default='NER'  ,					type=UpperCAmelCase_  ,					help='Task type to fine tune in training (e.g. NER, POS, etc)')
			parser.add_argument(
			    '--max_seq_length'  ,					default=128  ,					type=UpperCAmelCase_  ,					help=(
			        'The maximum total input sequence length after tokenization. Sequences longer '
			        'than this will be truncated, sequences shorter will be padded.'
			    )  ,					)

			parser.add_argument(
			    '--labels'  ,					default=''  ,					type=UpperCAmelCase_  ,					help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'  ,					)
			parser.add_argument(
			    '--gpus'  ,					default=0  ,					type=UpperCAmelCase_  ,					help='The number of GPUs allocated for this, it is by default 0 meaning none'  ,					)

			parser.add_argument(
			    '--overwrite_cache'  ,					action='store_true'  ,					help='Overwrite the cached training and evaluation sets')

			return parser


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		lowerCAmelCase__          =						NERTransformer.add_model_specific_args(parser, os.getcwd())
		lowerCAmelCase__          =						parser.parse_args()
		lowerCAmelCase__          =						NERTransformer(args)
		lowerCAmelCase__          =						generic_train(model, args)

		if args.do_predict:
				# See https://github.com/huggingface/transformers/issues/3159
				# pl use this default format to create a checkpoint:
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
				# /pytorch_lightning/callbacks/model_checkpoint.py#L322
				lowerCAmelCase__          =						sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
				lowerCAmelCase__          =						model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)












'''simple docstring'''




import argparse
import struct
import unittest







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					UpperCAmelCase_   :						bytes):
			UpperCamelCase__				:					Dict					=					data

			# Initialize hash values
			UpperCamelCase__				:					Any					=					[
			    0X6A_09E_667,
			    0XBB_67A_E85,
			    0X3C_6EF_372,
			    0XA5_4FF_53A,
			    0X51_0E5_27F,
			    0X9B_056_88C,
			    0X1F_83D_9AB,
			    0X5B_E0C_D19,
			]

			# Initialize round constants
			UpperCamelCase__				:					List[Any]					=					[
			    0X42_8A2_F98,
			    0X71_374_491,
			    0XB5_C0F_BCF,
			    0XE9_B5D_BA5,
			    0X39_56C_25B,
			    0X59_F11_1F1,
			    0X92_3F8_2A4,
			    0XAB_1C5_ED5,
			    0XD8_07A_A98,
			    0X12_835_B01,
			    0X24_318_5BE,
			    0X55_0C7_DC3,
			    0X72_BE5_D74,
			    0X80_DEB_1FE,
			    0X9B_DC0_6A7,
			    0XC1_9BF_174,
			    0XE4_9B6_9C1,
			    0XEF_BE4_786,
			    0X0F_C19_DC6,
			    0X24_0CA_1CC,
			    0X2D_E92_C6F,
			    0X4A_748_4AA,
			    0X5C_B0A_9DC,
			    0X76_F98_8DA,
			    0X98_3E5_152,
			    0XA8_31C_66D,
			    0XB0_032_7C8,
			    0XBF_597_FC7,
			    0XC6_E00_BF3,
			    0XD5_A79_147,
			    0X06_CA6_351,
			    0X14_292_967,
			    0X27_B70_A85,
			    0X2E_1B2_138,
			    0X4D_2C6_DFC,
			    0X53_380_D13,
			    0X65_0A7_354,
			    0X76_6A0_ABB,
			    0X81_C2C_92E,
			    0X92_722_C85,
			    0XA2_BFE_8A1,
			    0XA8_1A6_64B,
			    0XC2_4B8_B70,
			    0XC7_6C5_1A3,
			    0XD1_92E_819,
			    0XD6_990_624,
			    0XF4_0E3_585,
			    0X10_6AA_070,
			    0X19_A4C_116,
			    0X1E_376_C08,
			    0X27_487_74C,
			    0X34_B0B_CB5,
			    0X39_1C0_CB3,
			    0X4E_D8A_A4A,
			    0X5B_9CC_A4F,
			    0X68_2E6_FF3,
			    0X74_8F8_2EE,
			    0X78_A56_36F,
			    0X84_C87_814,
			    0X8C_C70_208,
			    0X90_BEF_FFA,
			    0XA4_506_CEB,
			    0XBE_F9A_3F7,
			    0XC6_717_8F2,
			]

			UpperCamelCase__				:					Tuple					=					self.preprocessing(self.data)
			self.final_hash()

	@staticmethod
	def      __UpperCamelCase							(      UpperCAmelCase_   :						bytes):
			UpperCamelCase__				:					List[Any]					=					B'\x80' + (B'\x00' * (63 - (len(UpperCAmelCase_) + 8) % 64))
			UpperCamelCase__				:					List[Any]					=					struct.pack('>Q'  ,					(len(UpperCAmelCase_) * 8))
			return data + padding + big_endian_integer

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			# Convert into blocks of 64 bytes
			UpperCamelCase__				:					int					=					[
			    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
					UpperCamelCase__				:					Tuple					=					list(struct.unpack('>16L'  ,					UpperCAmelCase_))
					# add 48 0-ed integers
					words += [0] * 48

					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					str					=					self.hashes

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

									UpperCamelCase__				:					int					=					(
									    words[index - 16] + sa + words[index - 7] + sa
									) % 0X100_000_000

							# Compression
							UpperCamelCase__				:					Optional[Any]					=					self.ror(UpperCAmelCase_  ,					6) ^ self.ror(UpperCAmelCase_  ,					11) ^ self.ror(UpperCAmelCase_  ,					25)
							UpperCamelCase__				:					List[str]					=					(e & f) ^ ((~e & 0XFF_FFF_FFF) & g)
							UpperCamelCase__				:					List[Any]					=					(
							    h + sa + ch + self.round_constants[index] + words[index]
							) % 0X100_000_000
							UpperCamelCase__				:					List[str]					=					self.ror(UpperCAmelCase_  ,					2) ^ self.ror(UpperCAmelCase_  ,					13) ^ self.ror(UpperCAmelCase_  ,					22)
							UpperCamelCase__				:					Dict					=					(a & b) ^ (a & c) ^ (b & c)
							UpperCamelCase__				:					List[str]					=					(sa + maj) % 0X100_000_000

							UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					(
							    g,
							    f,
							    e,
							    ((d + tempa) % 0X100_000_000),
							    c,
							    b,
							    a,
							    ((tempa + tempa) % 0X100_000_000),
							)

					UpperCamelCase__				:					List[Any]					=					[a, b, c, d, e, f, g, h]

					# Modify final values
					UpperCamelCase__				:					Optional[Any]					=					[
					    ((element + mutated_hash_values[index]) % 0X100_000_000)
					    for index, element in enumerate(self.hashes)
					]

			UpperCamelCase__				:					Any					=					''.join([hex(UpperCAmelCase_)[2:].zfill(8) for value in self.hashes])





	def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			return 0XFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						int):
			import hashlib

			UpperCamelCase__				:					str					=					bytes('Test String'  ,					'utf-8')
			self.assertEqual(SHAaaa(UpperCAmelCase_).hash  ,					hashlib.shaaaa(UpperCAmelCase_).hexdigest())







def       __UpperCAmelCase						(  )							->     None:
		import doctest

		doctest.testmod()

		UpperCamelCase__				:					Union[str, Any]					=					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')

		UpperCamelCase__				:					List[str]					=					parser.parse_args()

		UpperCamelCase__				:					str					=					args.input_string

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

		print(SHAaaa(lowerCamelCase_).hash)


if __name__ == "__main__":
		main()




