




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCamelCase__						=		logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCamelCase__						=		{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCamelCase__						=		{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCamelCase__						=		sorted(arg_to_scheduler.keys())
UpperCamelCase__						=		'{' + ', '.join(arg_to_scheduler_choices) + '}'


class 							__SCREAMING_SNAKE_CASE				(						pl.LightningModule    ):

					def __init__(							self					,			__lowercase					,			__lowercase=None					,			__lowercase="base"					,			__lowercase=None					,			__lowercase=None					,			__lowercase=None					,			**__lowercase					,			):







						'''simple docstring'''




						super().__init__()
						# TODO: move to self.save_hyperparameters()
						# self.save_hyperparameters()
						# can also expand arguments into trainer signature for easier reading

						self.save_hyperparameters(__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				0
						lowerCamelCase_				:						Dict				  =				Path(self.hparams.output_dir   )
						lowerCamelCase_				:						str				  =				self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
							lowerCamelCase_				:						Optional[int]				  =				AutoConfig.from_pretrained(
							    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,			**({'num_labels': num_labels} if num_labels is not None else {})					,			cache_dir=__lowercase					,			**__lowercase					,			)
						else:
							lowerCamelCase_				:						PretrainedConfig				  =				config

						lowerCamelCase_				:						Dict				  =				('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
						for p in extra_model_params:
							if getattr(self.hparams					,			__lowercase					,			__lowercase   ):
								assert hasattr(self.config					,			__lowercase   ), f"model config doesn't have a `{p}` attribute"
								setattr(self.config					,			__lowercase					,			getattr(self.hparams					,			__lowercase   )   )

						if tokenizer is None:
							lowerCamelCase_				:						Any				  =				AutoTokenizer.from_pretrained(
							    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,			cache_dir=__lowercase					,			)
						else:
							lowerCamelCase_				:						PreTrainedTokenizer				  =				tokenizer
						lowerCamelCase_				:						Dict				  =				MODEL_MODES[mode]
						if model is None:
							lowerCamelCase_				:						int				  =				self.model_type.from_pretrained(
							    self.hparams.model_name_or_path					,			from_tf=bool('.ckpt' in self.hparams.model_name_or_path   )					,			config=self.config					,			cache_dir=__lowercase					,			)
						else:
							lowerCamelCase_				:						Optional[int]				  =				model

					def       lowercase_				(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[Any]				  =				self.model_type.from_pretrained(*__lowercase					,			**__lowercase   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				arg_to_scheduler[self.hparams.lr_scheduler]
						lowerCamelCase_				:						List[str]				  =				get_schedule_func(
						    self.opt					,			num_warmup_steps=self.hparams.warmup_steps					,			num_training_steps=self.total_steps()   )
						lowerCamelCase_				:						Optional[Any]				  =				{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
						return scheduler

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				self.model
						lowerCamelCase_				:						List[str]				  =				['bias', 'LayerNorm.weight']
						lowerCamelCase_				:						Optional[int]				  =				[
						    {
						        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
						        'weight_decay': self.hparams.weight_decay,
						    },
						    {
						        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )],
						        'weight_decay': 0.0,
						    },
						]
						if self.hparams.adafactor:
							lowerCamelCase_				:						Dict				  =				Adafactor(
							    __lowercase					,			lr=self.hparams.learning_rate					,			scale_parameter=__lowercase					,			relative_step=__lowercase   )

						else:
							lowerCamelCase_				:						str				  =				AdamW(
							    __lowercase					,			lr=self.hparams.learning_rate					,			eps=self.hparams.adam_epsilon   )
						lowerCamelCase_				:						str				  =				optimizer

						lowerCamelCase_				:						Any				  =				self.get_lr_scheduler()

						return [optimizer], [scheduler]

					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						return self.validation_step(__lowercase					,			__lowercase   )

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						return self.validation_end(__lowercase   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				max(1					,			self.hparams.gpus   )  # TODO: consider num_tpu_cores
						lowerCamelCase_				:						Optional[Any]				  =				self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
						return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						if mode == "test":
							lowerCamelCase_				:						List[str]				  =				len(self.test_dataloader().dataset   )
						else:
							lowerCamelCase_				:						Optional[Any]				  =				self.get_dataloader('train'					,			self.hparams.train_batch_size					,			shuffle=__lowercase   )
							lowerCamelCase_				:						Tuple				  =				len(self.train_dataloader().dataset   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase = False   ):







						'''simple docstring'''




						raise NotImplementedError('You must implement this for your task'   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						return self.train_loader

					def       lowercase_				(							self   ):







						'''simple docstring'''




						return self.get_dataloader('dev'					,			self.hparams.eval_batch_size					,			shuffle=__lowercase   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						return self.get_dataloader('test'					,			self.hparams.eval_batch_size					,			shuffle=__lowercase   )

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						return os.path.join(
						    self.hparams.data_dir					,			'cached_{}_{}_{}'.format(
						        __lowercase					,			list(filter(__lowercase					,			self.hparams.model_name_or_path.split('/'   )   )   ).pop()					,			str(self.hparams.max_seq_length   )					,			)					,			)

					@pl.utilities.rank_zero_only
					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				self.output_dir.joinpath('best_tfmr'   )
						lowerCamelCase_				:						Dict				  =				self.step_count
						self.model.save_pretrained(__lowercase   )
						self.tokenizer.save_pretrained(__lowercase   )





					@staticmethod
					def       lowercase_				(							__lowercase					,			__lowercase   ):







						'''simple docstring'''




						parser.add_argument(
						    '--model_name_or_path'					,			default=__lowercase					,			type=__lowercase					,			required=__lowercase					,			help='Path to pretrained model or model identifier from huggingface.co/models'					,			)
						parser.add_argument(
						    '--config_name'					,			default=''					,			type=__lowercase					,			help='Pretrained config name or path if not the same as model_name'   )
						parser.add_argument(
						    '--tokenizer_name'					,			default=__lowercase					,			type=__lowercase					,			help='Pretrained tokenizer name or path if not the same as model_name'					,			)
						parser.add_argument(
						    '--cache_dir'					,			default=''					,			type=__lowercase					,			help='Where do you want to store the pre-trained models downloaded from huggingface.co'					,			)
						parser.add_argument(
						    '--encoder_layerdrop'					,			type=__lowercase					,			help='Encoder layer dropout probability (Optional). Goes into model.config'					,			)
						parser.add_argument(
						    '--decoder_layerdrop'					,			type=__lowercase					,			help='Decoder layer dropout probability (Optional). Goes into model.config'					,			)
						parser.add_argument(
						    '--dropout'					,			type=__lowercase					,			help='Dropout probability (Optional). Goes into model.config'					,			)
						parser.add_argument(
						    '--attention_dropout'					,			type=__lowercase					,			help='Attention dropout probability (Optional). Goes into model.config'					,			)
						parser.add_argument('--learning_rate'					,			default=5E-5					,			type=__lowercase					,			help='The initial learning rate for Adam.'   )
						parser.add_argument(
						    '--lr_scheduler'					,			default='linear'					,			choices=__lowercase					,			metavar=__lowercase					,			type=__lowercase					,			help='Learning rate scheduler'					,			)
						parser.add_argument('--weight_decay'					,			default=0.0					,			type=__lowercase					,			help='Weight decay if we apply some.'   )
						parser.add_argument('--adam_epsilon'					,			default=1E-8					,			type=__lowercase					,			help='Epsilon for Adam optimizer.'   )
						parser.add_argument('--warmup_steps'					,			default=0					,			type=__lowercase					,			help='Linear warmup over warmup_steps.'   )
						parser.add_argument('--num_workers'					,			default=4					,			type=__lowercase					,			help='kwarg passed to DataLoader'   )
						parser.add_argument('--num_train_epochs'					,			dest='max_epochs'					,			default=3					,			type=__lowercase   )
						parser.add_argument('--train_batch_size'					,			default=32					,			type=__lowercase   )
						parser.add_argument('--eval_batch_size'					,			default=32					,			type=__lowercase   )
						parser.add_argument('--adafactor'					,			action='store_true'   )


class 							__SCREAMING_SNAKE_CASE				(						pl.Callback    ):

					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				trainer.lr_schedulers[0]['scheduler']
						lowerCamelCase_				:						str				  =				{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
						pl_module.logger.log_metrics(__lowercase   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						rank_zero_info('***** Validation results *****'   )
						lowerCamelCase_				:						Optional[Any]				  =				trainer.callback_metrics
						# Log results
						for key in sorted(__lowercase   ):
							if key not in ["log", "progress_bar"]:
								rank_zero_info('{} = {}\n'.format(__lowercase					,			str(metrics[key]   )   )   )





					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						rank_zero_info('***** Test results *****'   )
						lowerCamelCase_				:						Tuple				  =				trainer.callback_metrics
						# Log and save results to file
						lowerCamelCase_				:						Optional[Any]				  =				os.path.join(pl_module.hparams.output_dir					,			'test_results.txt'   )
						with open(__lowercase					,			'w'   ) as writer:
							for key in sorted(__lowercase   ):
								if key not in ["log", "progress_bar"]:
									rank_zero_info('{} = {}\n'.format(__lowercase					,			str(metrics[key]   )   )   )
									writer.write('{} = {}\n'.format(__lowercase					,			str(metrics[key]   )   )   )






def    snake_case   (				A		,  A     )				->				None:




	'''simple docstring'''


	parser.add_argument(
	    '--output_dir'		,  default=A		,  type=A		,  required=A		,  help='The output directory where the model predictions and checkpoints will be written.'		,  )
	parser.add_argument(
	    '--fp16'		,  action='store_true'		,  help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'		,  )

	parser.add_argument(
	    '--fp16_opt_level'		,  type=A		,  default='O2'		,  help=(
	        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
	        'See details at https://nvidia.github.io/apex/amp.html'
	    )		,  )
	parser.add_argument('--n_tpu_cores'		,  dest='tpu_cores'		,  type=A     )
	parser.add_argument('--max_grad_norm'		,  dest='gradient_clip_val'		,  default=1.0		,  type=A		,  help='Max gradient norm'     )
	parser.add_argument('--do_train'		,  action='store_true'		,  help='Whether to run training.'     )
	parser.add_argument('--do_predict'		,  action='store_true'		,  help='Whether to run predictions on the test set.'     )
	parser.add_argument(
	    '--gradient_accumulation_steps'		,  dest='accumulate_grad_batches'		,  type=A		,  default=1		,  help='Number of updates steps to accumulate before performing a backward/update pass.'		,  )
	parser.add_argument('--seed'		,  type=A		,  default=42		,  help='random seed for initialization'     )
	parser.add_argument(
	    '--data_dir'		,  default=A		,  type=A		,  required=A		,  help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'		,  )






def    snake_case   (				A		,  A		,  A=None		,  A=True		,  A=[]		,  A=None		,  A=None		,  **A		,  )				->				Optional[Any]:




	'''simple docstring'''


	pl.seed_everything(args.seed     )

	# init model
	lowerCamelCase_				:						Dict				  =				Path(model.hparams.output_dir     )
	odir.mkdir(exist_ok=A     )

	# add custom checkpoints
	if checkpoint_callback is None:
		lowerCamelCase_				:						str				  =				pl.callbacks.ModelCheckpoint(
		    filepath=args.output_dir		,  prefix='checkpoint'		,  monitor='val_loss'		,  mode='min'		,  save_top_k=1     )
	if early_stopping_callback:
		extra_callbacks.append(A     )
	if logging_callback is None:
		lowerCamelCase_				:						Optional[Any]				  =				LoggingCallback()

	lowerCamelCase_				:						Any				  =				{}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		lowerCamelCase_				:						List[str]				  =				16
		lowerCamelCase_				:						Optional[int]				  =				args.fpaa_opt_level

	if args.gpus > 1:
		lowerCamelCase_				:						List[Any]				  =				'ddp'

	lowerCamelCase_				:						Dict				  =				args.accumulate_grad_batches
	lowerCamelCase_				:						Any				  =				extra_train_kwargs.get('accelerator'		,  A     )
	lowerCamelCase_				:						Union[str, Any]				  =				extra_train_kwargs.get('profiler'		,  A     )

	lowerCamelCase_				:						str				  =				pl.Trainer.from_argparse_args(
	    A		,  weights_summary=A		,  callbacks=[logging_callback] + extra_callbacks		,  logger=A		,  checkpoint_callback=A		,  **A		,  )

	if args.do_train:
		trainer.fit(A     )

	return trainer




"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

     if len(_lowerCAmelCase							) == 0:
          return len(_lowerCAmelCase							)

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

          for j, ca in enumerate(_lowerCAmelCase							):
               # Calculate insertions, deletions and substitutions
               _a     						=					previous_row[j + 1] + 1
               _a     						=					current_row[j] + 1
               _a     						=					previous_row[j] + (ca != ca)

               # Get the minimum to append to the current row
               current_row.append(min(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)							)

          # Store the previous row
          _a     						=					current_row

     # Returns the last element (distance)
     return previous_row[-1]


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')