

import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


lowerCAmelCase__             =		logging.getLogger(__name__)






def        lowerCamelCase_  (				)    ->					str:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'						)
	parser.add_argument(
	    '--dataset_name'				,							type=snake_case_				,							default='wikitext'				,							help='Name of the training. Explore datasets at: hf.co/datasets.'				,							)
	parser.add_argument(
	    '--dataset_config'				,							type=snake_case_				,							default='wikitext-103-raw-v1'				,							help='Configuration name of the dataset.'						)
	parser.add_argument(
	    '--batch_size'				,							type=snake_case_				,							default=1_0_0_0				,							help='Batch size during training.'				,							)
	parser.add_argument(
	    '--vocab_size'				,							type=snake_case_				,							default=1_0_0_4_8				,							help='Size of the desired vocabulary.'				,							)
	parser.add_argument(
	    '--limit'				,							default=snake_case_				,							type=snake_case_				,							help='Limit the number of shards (used for debugging).'				,							)
	parser.add_argument(
	    '--export_to_hub'				,							action='store_true'				,							)

	_UpperCamelCase :		Tuple								=    parser.parse_args()
	return args






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]						)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    datasets.load_dataset(args.dataset_name				,							args.dataset_config				,							split='train'						)

	if args.limit is not None:
		_UpperCamelCase :		List[str]								=    min(len(snake_case_						)				,							args.limit						)
		_UpperCamelCase :		Optional[Any]								=    dataset.select(range(snake_case_						)						)
		logger.info(F'''Limiting the dataset to {args.limit} entries.'''						)

	def batch_iterator():
		for i in range(0				,							len(snake_case_						)				,							args.batch_size						):
			yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
	_UpperCamelCase :		Optional[Any]								=    Tokenizer(Unigram()						)
	_UpperCamelCase :		Any								=    normalizers.Sequence([normalizers.Replace('``'				,							'"'						), normalizers.Replace('\'\''				,							'"'						)]						)
	_UpperCamelCase :		int								=    pre_tokenizers.Metaspace()

	# Prepare the trainer.
	_UpperCamelCase :		Any								=    UnigramTrainer(
	    unk_token='<unk>'				,							special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']				,							vocab_size=args.vocab_size				,							)

	logger.info('Training the tokenizer.'						)
	tokenizer.train_from_iterator(batch_iterator()				,							trainer=snake_case_						)
	logger.info('Tokenizer training complete!'						)

	_UpperCamelCase :		List[Any]								=    tokenizer.token_to_id('[CLS]'						)
	_UpperCamelCase :		Optional[int]								=    tokenizer.token_to_id('[SEP]'						)
	_UpperCamelCase :		str								=    processors.TemplateProcessing(
	    single='[CLS]:0 $A:0 [SEP]:0'				,							pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'				,							special_tokens=[
	        ('[CLS]', cls_token_id),
	        ('[SEP]', sep_token_id),
	    ]				,							)
	_UpperCamelCase :		Dict								=    decoders.Metaspace()

	if args.export_to_hub:
		logger.info('Exporting the trained tokenzier to Hub.'						)
		_UpperCamelCase :		Any								=    AlbertTokenizerFast(tokenizer_object=snake_case_						)
		new_tokenizer.push_to_hub('unigram-tokenizer-dataset'						)


if __name__ == "__main__":
							lowerCAmelCase__             =		parse_args()
							main(args)









'''simple docstring'''






import math as m





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any] ,       snake_case_    :     int ,       snake_case_    :     Optional[Any]=0.0_0_1  )		->Tuple:






							'''simple docstring'''
							return (f(a + h  ) - f(a - h  )) / (2 * h)





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Dict ,       snake_case_    :     List[Any]=0 ,       snake_case_    :     Dict=1_00 ,       snake_case_    :     List[str]=0.0_0_0_1 ,       snake_case_    :     str=1E-6 ,       snake_case_    :     Tuple=False  )		->List[Any]:






							'''simple docstring'''
							_lowercase							:		Tuple				=   xa  # set the initial guess
							_lowercase							:		List[str]				=   [a]
							_lowercase							:		str				=   abs(f(snake_case_  )  )
							_lowercase							:		Dict				=   lambda snake_case_  : calc_derivative(snake_case_ ,       snake_case_ ,       h=snake_case_  )  # noqa: E731  Derivative of f(x)
							for _ in range(snake_case_  ):
														if fa(snake_case_  ) == 0:
																					raise ValueError('''No converging solution found'''  )
														_lowercase							:		Tuple				=   a - f(snake_case_  ) / fa(snake_case_  )  # Calculate the next estimate
														if logsteps:
																					steps.append(snake_case_  )
														if error < maxerror:
																					break
							else:
														raise ValueError('''Iteration limit reached, no converging solution found'''  )
							if logsteps:
														# If logstep is true, then log intermediate steps
														return a, error, steps
							return a, error


if __name__ == "__main__":
						from matplotlib import pyplot as plt

						lowerCamelCase__	  =		lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
						lowerCamelCase__  ,      lowerCamelCase__  ,      lowerCamelCase__	  =		newton_raphson(
						    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
						)
						plt.plot([abs(f(x)) for x in steps])
						plt.xlabel('step')
						plt.ylabel('error')
						plt.show()
						print(f'''solution = {{{solution:f}}}, error = {{{error:f}}}''')



