
"""simple docstring"""

import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_A            = logging.getLogger(__name__)
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		lowerCamelCase     :			List[str]		=    p * torch.log(a_			)
		lowerCamelCase     :			int		=    0
		return -plogp.sum(dim=-1			)
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		logger.info('lv, h >\t' + '\t'.join(F"""{x + 1}""" for x in range(len(a_			)			)			)			)
		for row in range(len(a_			)			):
				if tensor.dtype != torch.long:
						logger.info(F"""layer {row + 1}:\t""" + '\t'.join(F"""{x:.5f}""" for x in tensor[row].cpu().data			)			)
				else:
						logger.info(F"""layer {row + 1}:\t""" + '\t'.join(F"""{x:d}""" for x in tensor[row].cpu().data			)			)
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_=True,						a_=True,						a_=None,						a_=False			):





		'''simple docstring'''


		lowerCamelCase	,    lowerCamelCase     :			str		=    model.config.num_hidden_layers, model.config.num_attention_heads
		lowerCamelCase     :			Tuple		=    torch.zeros(a_,						a_			).to(args.device			)
		lowerCamelCase     :			int		=    torch.zeros(a_,						a_			).to(args.device			)

		if head_mask is None:
				lowerCamelCase     :			Any		=    torch.ones(a_,						a_			).to(args.device			)

		head_mask.requires_grad_(requires_grad=a_			)
		# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
		if actually_pruned:
				lowerCamelCase     :			List[str]		=    None

		lowerCamelCase     :			Optional[int]		=    None
		lowerCamelCase     :			Any		=    None
		lowerCamelCase     :			Optional[Any]		=    0.0

		for step, inputs in enumerate(tqdm(a_,						desc='Iteration',						disable=args.local_rank not in [-1, 0]			)			):
				for k, v in inputs.items():
						lowerCamelCase     :			Optional[Any]		=    v.to(args.device			)

				# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
				lowerCamelCase     :			Optional[Any]		=    model(**a_,						head_mask=a_			)
				lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Tuple		=    (
				    outputs[0],
				    outputs[1],
				    outputs[-1],
				)  # Loss and logits are the first, attention the last
				loss.backward()  # Backpropagate to populate the gradients in the head mask

				if compute_entropy:
						for layer, attn in enumerate(a_			):
								lowerCamelCase     :			int		=    entropy(attn.detach()			) * inputs['attention_mask'].float().unsqueeze(1			)
								attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).detach()

				if compute_importance:
						head_importance += head_mask.grad.abs().detach()

				# Also store our logits/labels if we want to compute metrics afterwards
				if preds is None:
						lowerCamelCase     :			Optional[Any]		=    logits.detach().cpu().numpy()
						lowerCamelCase     :			Dict		=    inputs['labels'].detach().cpu().numpy()
				else:
						lowerCamelCase     :			str		=    np.append(a_,						logits.detach().cpu().numpy(),						axis=0			)
						lowerCamelCase     :			int		=    np.append(a_,						inputs['labels'].detach().cpu().numpy(),						axis=0			)

				tot_tokens += inputs["attention_mask"].float().detach().sum().data

		# Normalize
		attn_entropy /= tot_tokens
		head_importance /= tot_tokens
		# Layerwise importance normalization
		if not args.dont_normalize_importance_by_layer:
				lowerCamelCase     :			str		=    2
				lowerCamelCase     :			Dict		=    torch.pow(torch.pow(a_,						a_			).sum(-1			),						1 / exponent			)
				head_importance /= norm_by_layer.unsqueeze(-1			) + 1E-20

		if not args.dont_normalize_global_importance:
				lowerCamelCase     :			Any		=    (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

		# Print/save matrices
		np.save(os.path.join(args.output_dir,						'attn_entropy.npy'			),						attn_entropy.detach().cpu().numpy()			)
		np.save(os.path.join(args.output_dir,						'head_importance.npy'			),						head_importance.detach().cpu().numpy()			)

		logger.info('Attention entropies'			)
		print_ad_tensor(a_			)
		logger.info('Head importance scores'			)
		print_ad_tensor(a_			)
		logger.info('Head ranked by importance scores'			)
		lowerCamelCase     :			str		=    torch.zeros(head_importance.numel(),						dtype=torch.long,						device=args.device			)
		lowerCamelCase     :			List[str]		=    torch.arange(
		    head_importance.numel(),						device=args.device			)
		lowerCamelCase     :			List[str]		=    head_ranks.view_as(a_			)
		print_ad_tensor(a_			)

		return attn_entropy, head_importance, preds, labels
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			int		=    compute_heads_importance(a_,						a_,						a_,						compute_entropy=a_			)
		lowerCamelCase     :			Any		=    np.argmax(a_,						axis=1			) if args.output_mode == 'classification' else np.squeeze(a_			)
		lowerCamelCase     :			List[Any]		=    glue_compute_metrics(args.task_name,						a_,						a_			)[args.metric_name]
		logger.info('Pruning: original score: %f, threshold: %f',						a_,						original_score * args.masking_threshold			)

		lowerCamelCase     :			Dict		=    torch.ones_like(a_			)
		lowerCamelCase     :			Tuple		=    max(1,						int(new_head_mask.numel() * args.masking_amount			)			)

		lowerCamelCase     :			Union[str, Any]		=    original_score
		while current_score >= original_score * args.masking_threshold:
				lowerCamelCase     :			Dict		=    new_head_mask.clone()  # save current head mask
				# heads from least important to most - keep only not-masked heads
				lowerCamelCase     :			Optional[Any]		=    float('Inf'			)
				lowerCamelCase     :			List[str]		=    head_importance.view(-1			).sort()[1]

				if len(a_			) <= num_to_mask:
						break

				# mask heads
				lowerCamelCase     :			Tuple		=    current_heads_to_mask[:num_to_mask]
				logger.info('Heads to mask: %s',						str(current_heads_to_mask.tolist()			)			)
				lowerCamelCase     :			Any		=    new_head_mask.view(-1			)
				lowerCamelCase     :			Tuple		=    0.0
				lowerCamelCase     :			str		=    new_head_mask.view_as(a_			)
				lowerCamelCase     :			Dict		=    new_head_mask.clone().detach()
				print_ad_tensor(a_			)

				# Compute metric and head importance again
				lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			str		=    compute_heads_importance(
				    a_,						a_,						a_,						compute_entropy=a_,						head_mask=a_			)
				lowerCamelCase     :			Tuple		=    np.argmax(a_,						axis=1			) if args.output_mode == 'classification' else np.squeeze(a_			)
				lowerCamelCase     :			Optional[Any]		=    glue_compute_metrics(args.task_name,						a_,						a_			)[args.metric_name]
				logger.info(
				    'Masking: current score: %f, remaining heads %d (%.1f percents)',						a_,						new_head_mask.sum(),						new_head_mask.sum() / new_head_mask.numel() * 100,						)

		logger.info('Final head mask'			)
		print_ad_tensor(a_			)
		np.save(os.path.join(args.output_dir,						'head_mask.npy'			),						head_mask.detach().cpu().numpy()			)

		return head_mask
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Optional[Any]		=    datetime.now()
		lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Dict		=    compute_heads_importance(
		    a_,						a_,						a_,						compute_entropy=a_,						compute_importance=a_,						head_mask=a_			)
		lowerCamelCase     :			Optional[int]		=    np.argmax(a_,						axis=1			) if args.output_mode == 'classification' else np.squeeze(a_			)
		lowerCamelCase     :			int		=    glue_compute_metrics(args.task_name,						a_,						a_			)[args.metric_name]
		lowerCamelCase     :			int		=    datetime.now() - before_time

		lowerCamelCase     :			Union[str, Any]		=    sum(p.numel() for p in model.parameters()			)
		lowerCamelCase     :			Any		=    {
		    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(a_			)			)
		}

		assert sum(len(a_			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
		model.prune_heads(a_			)
		lowerCamelCase     :			List[str]		=    sum(p.numel() for p in model.parameters()			)

		lowerCamelCase     :			List[str]		=    datetime.now()
		lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Tuple		=    compute_heads_importance(
		    a_,						a_,						a_,						compute_entropy=a_,						compute_importance=a_,						head_mask=a_,						actually_pruned=a_,						)
		lowerCamelCase     :			Dict		=    np.argmax(a_,						axis=1			) if args.output_mode == 'classification' else np.squeeze(a_			)
		lowerCamelCase     :			Any		=    glue_compute_metrics(args.task_name,						a_,						a_			)[args.metric_name]
		lowerCamelCase     :			Dict		=    datetime.now() - before_time

		logger.info(
		    'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)',						a_,						a_,						pruned_num_params / original_num_params * 100,						)
		logger.info('Pruning: score with masking: %f score with pruning: %f',						a_,						a_			)
		logger.info('Pruning: speed ratio (new timing / original timing): %f percents',						original_time / new_time * 100			)
def 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			str		=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--data_dir',						default=a_,						type=a_,						required=a_,						help='The input data dir. Should contain the .tsv files (or other data files) for the task.',						)
		parser.add_argument(
		    '--model_name_or_path',						default=a_,						type=a_,						required=a_,						help='Path to pretrained model or model identifier from huggingface.co/models',						)
		parser.add_argument(
		    '--task_name',						default=a_,						type=a_,						required=a_,						help='The name of the task to train selected in the list: ' + ', '.join(glue_processors.keys()			),						)
		parser.add_argument(
		    '--output_dir',						default=a_,						type=a_,						required=a_,						help='The output directory where the model predictions and checkpoints will be written.',						)

		# Other parameters
		parser.add_argument(
		    '--config_name',						default='',						type=a_,						help='Pretrained config name or path if not the same as model_name_or_path',						)
		parser.add_argument(
		    '--tokenizer_name',						default='',						type=a_,						help='Pretrained tokenizer name or path if not the same as model_name_or_path',						)
		parser.add_argument(
		    '--cache_dir',						default=a_,						type=a_,						help='Where do you want to store the pre-trained models downloaded from huggingface.co',						)
		parser.add_argument(
		    '--data_subset',						type=a_,						default=-1,						help='If > 0: limit the data to a subset of data_subset instances.'			)
		parser.add_argument(
		    '--overwrite_output_dir',						action='store_true',						help='Whether to overwrite data in output directory'			)
		parser.add_argument(
		    '--overwrite_cache',						action='store_true',						help='Overwrite the cached training and evaluation sets'			)

		parser.add_argument(
		    '--dont_normalize_importance_by_layer',						action='store_true',						help='Don\'t normalize importance score by layers'			)
		parser.add_argument(
		    '--dont_normalize_global_importance',						action='store_true',						help='Don\'t normalize all importance scores between 0 and 1',						)

		parser.add_argument(
		    '--try_masking',						action='store_true',						help='Whether to try to mask head until a threshold of accuracy.'			)
		parser.add_argument(
		    '--masking_threshold',						default=0.9,						type=a_,						help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).',						)
		parser.add_argument(
		    '--masking_amount',						default=0.1,						type=a_,						help='Amount to heads to masking at each masking step.'			)
		parser.add_argument('--metric_name',						default='acc',						type=a_,						help='Metric to use for head masking.'			)

		parser.add_argument(
		    '--max_seq_length',						default=128,						type=a_,						help=(
		        'The maximum total input sequence length after WordPiece tokenization. \n'
		        'Sequences longer than this will be truncated, sequences shorter padded.'
		    ),						)
		parser.add_argument('--batch_size',						default=1,						type=a_,						help='Batch size.'			)

		parser.add_argument('--seed',						type=a_,						default=42			)
		parser.add_argument('--local_rank',						type=a_,						default=-1,						help='local_rank for distributed training on gpus'			)
		parser.add_argument('--no_cuda',						action='store_true',						help='Whether not to use CUDA when available'			)
		parser.add_argument('--server_ip',						type=a_,						default='',						help='Can be used for distant debugging.'			)
		parser.add_argument('--server_port',						type=a_,						default='',						help='Can be used for distant debugging.'			)
		lowerCamelCase     :			Union[str, Any]		=    parser.parse_args()

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print('Waiting for debugger attach'			)
				ptvsd.enable_attach(address=(args.server_ip, args.server_port),						redirect_output=a_			)
				ptvsd.wait_for_attach()

		# Setup devices and distributed training
		if args.local_rank == -1 or args.no_cuda:
				lowerCamelCase     :			Dict		=    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'			)
				lowerCamelCase     :			Optional[Any]		=    0 if args.no_cuda else torch.cuda.device_count()
		else:
				torch.cuda.set_device(args.local_rank			)
				lowerCamelCase     :			Optional[int]		=    torch.device('cuda',						args.local_rank			)
				lowerCamelCase     :			List[str]		=    1
				torch.distributed.init_process_group(backend='nccl'			)  # Initializes the distributed backend

		# Setup logging
		logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN			)
		logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device,						args.n_gpu,						bool(args.local_rank != -1			)			)			)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(args.local_rank			):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

		# Set seeds
		set_seed(args.seed			)

		# Prepare GLUE task
		lowerCamelCase     :			Union[str, Any]		=    args.task_name.lower()
		if args.task_name not in glue_processors:
				raise ValueError('Task not found: %s' % (args.task_name)			)
		lowerCamelCase     :			int		=    glue_processors[args.task_name]()
		lowerCamelCase     :			str		=    glue_output_modes[args.task_name]
		lowerCamelCase     :			int		=    processor.get_labels()
		lowerCamelCase     :			Any		=    len(a_			)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		lowerCamelCase     :			int		=    AutoConfig.from_pretrained(
		    args.config_name if args.config_name else args.model_name_or_path,						num_labels=a_,						finetuning_task=args.task_name,						output_attentions=a_,						cache_dir=args.cache_dir,						)
		lowerCamelCase     :			Optional[int]		=    AutoTokenizer.from_pretrained(
		    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,						cache_dir=args.cache_dir,						)
		lowerCamelCase     :			Tuple		=    AutoModelForSequenceClassification.from_pretrained(
		    args.model_name_or_path,						from_tf=bool('.ckpt' in args.model_name_or_path			),						config=a_,						cache_dir=args.cache_dir,						)

		# Distributed and parallel training
		model.to(args.device			)
		if args.local_rank != -1:
				lowerCamelCase     :			Tuple		=    nn.parallel.DistributedDataParallel(
				    a_,						device_ids=[args.local_rank],						output_device=args.local_rank,						find_unused_parameters=a_			)
		elif args.n_gpu > 1:
				lowerCamelCase     :			Union[str, Any]		=    nn.DataParallel(a_			)

		# Print/save training arguments
		os.makedirs(args.output_dir,						exist_ok=a_			)
		torch.save(a_,						os.path.join(args.output_dir,						'run_args.bin'			)			)
		logger.info('Training/evaluation parameters %s',						a_			)

		# Prepare dataset for the GLUE task
		lowerCamelCase     :			Optional[int]		=    GlueDataset(a_,						tokenizer=a_,						mode='dev'			)
		if args.data_subset > 0:
				lowerCamelCase     :			Union[str, Any]		=    Subset(a_,						list(range(min(args.data_subset,						len(a_			)			)			)			)			)
		lowerCamelCase     :			Dict		=    SequentialSampler(a_			) if args.local_rank == -1 else DistributedSampler(a_			)
		lowerCamelCase     :			int		=    DataLoader(
		    a_,						sampler=a_,						batch_size=args.batch_size,						collate_fn=a_			)

		# Compute head entropy and importance score
		compute_heads_importance(a_,						a_,						a_			)

		# Try head masking (set heads to zero until the score goes under a threshole)
		# and head pruning (remove masked heads and see the effect on the network)
		if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
				lowerCamelCase     :			Union[str, Any]		=    mask_heads(a_,						a_,						a_			)
				prune_heads(a_,						a_,						a_,						a_			)


if __name__ == "__main__":
					main()











"""simple docstring"""
def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							str							)		->				int:
				# The longer word should come first
				if len(lowercase_							) < len(lowercase_							):
								return levenshtein_distance(lowercase_  ,      lowercase_							)

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

				_lowerCamelCase					     = list(range(len(lowercase_							) + 1							)							)

				for i, ca in enumerate(lowercase_							):
								_lowerCamelCase					     = [i + 1]

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

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

								# Store the previous row
								_lowerCamelCase					     = current_row

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


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  int										=					input('''Enter the first word:\n''').strip()
			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					input('''Enter the second word:\n''').strip()

			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					levenshtein_distance(first_word, second_word)
			print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")
