


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def        snake_case_					(  snake_case					)  ->    Optional[Any]:
       # vision encoder
       if "img_encoder.pos_embed" in name:
              lowercase__:       int        =       name.replace('img_encoder.pos_embed'      ,							'vision_model.embeddings.position_embeddings'					)
       if "img_encoder.patch_embed.proj" in name:
              lowercase__:       Dict        =       name.replace('img_encoder.patch_embed.proj'      ,							'vision_model.embeddings.patch_embeddings.projection'					)
       if "img_encoder.patch_embed.norm" in name:
              lowercase__:       Union[str, Any]        =       name.replace('img_encoder.patch_embed.norm'      ,							'vision_model.embeddings.layernorm'					)
       if "img_encoder.layers" in name:
              lowercase__:       Any        =       name.replace('img_encoder.layers'      ,							'vision_model.encoder.stages'					)
       if "blocks" in name and "res" not in name:
              lowercase__:       List[Any]        =       name.replace('blocks'      ,							'layers'					)
       if "attn" in name and "pre_assign" not in name:
              lowercase__:       Tuple        =       name.replace('attn'      ,							'self_attn'					)
       if "proj" in name and "self_attn" in name and "text" not in name:
              lowercase__:       int        =       name.replace('proj'      ,							'out_proj'					)
       if "pre_assign_attn.attn.proj" in name:
              lowercase__:       List[str]        =       name.replace('pre_assign_attn.attn.proj'      ,							'pre_assign_attn.attn.out_proj'					)
       if "norm1" in name:
              lowercase__:       Optional[Any]        =       name.replace('norm1'      ,							'layer_norm1'					)
       if "norm2" in name and "pre_assign" not in name:
              lowercase__:       Tuple        =       name.replace('norm2'      ,							'layer_norm2'					)
       if "img_encoder.norm" in name:
              lowercase__:       Any        =       name.replace('img_encoder.norm'      ,							'vision_model.layernorm'					)
       # text encoder
       if "text_encoder.token_embedding" in name:
              lowercase__:       Union[str, Any]        =       name.replace('text_encoder.token_embedding'      ,							'text_model.embeddings.token_embedding'					)
       if "text_encoder.positional_embedding" in name:
              lowercase__:       Optional[Any]        =       name.replace('text_encoder.positional_embedding'      ,							'text_model.embeddings.position_embedding.weight'					)
       if "text_encoder.transformer.resblocks." in name:
              lowercase__:       str        =       name.replace('text_encoder.transformer.resblocks.'      ,							'text_model.encoder.layers.'					)
       if "ln_1" in name:
              lowercase__:       Tuple        =       name.replace('ln_1'      ,							'layer_norm1'					)
       if "ln_2" in name:
              lowercase__:       List[str]        =       name.replace('ln_2'      ,							'layer_norm2'					)
       if "c_fc" in name:
              lowercase__:       Any        =       name.replace('c_fc'      ,							'fc1'					)
       if "c_proj" in name:
              lowercase__:       str        =       name.replace('c_proj'      ,							'fc2'					)
       if "text_encoder" in name:
              lowercase__:       Tuple        =       name.replace('text_encoder'      ,							'text_model'					)
       if "ln_final" in name:
              lowercase__:       Dict        =       name.replace('ln_final'      ,							'final_layer_norm'					)
       # projection layers
       if "img_projector.linear_hidden." in name:
              lowercase__:       str        =       name.replace('img_projector.linear_hidden.'      ,							'visual_projection.'					)
       if "img_projector.linear_out." in name:
              lowercase__:       Any        =       name.replace('img_projector.linear_out.'      ,							'visual_projection.3.'					)
       if "text_projector.linear_hidden" in name:
              lowercase__:       Dict        =       name.replace('text_projector.linear_hidden'      ,							'text_projection'					)
       if "text_projector.linear_out" in name:
              lowercase__:       Union[str, Any]        =       name.replace('text_projector.linear_out'      ,							'text_projection.3'					)

       return name




def        snake_case_					(  snake_case      ,							snake_case					)  ->    List[Any]:
       for key in orig_state_dict.copy().keys():
              lowercase__:       List[Any]        =       orig_state_dict.pop(snake_case					)

              if "qkv" in key:
                     # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     lowercase__:       int        =       key.split('.'					)
                     lowercase__      ,						lowercase__:       Union[str, Any]        =       int(key_split[2]					), int(key_split[4]					)
                     lowercase__:       str        =       config.vision_config.hidden_size
                     if "weight" in key:
                            lowercase__:       Optional[Any]        =       val[:dim, :]
                            lowercase__:       List[str]        =       val[dim : dim * 2, :]
                            lowercase__:       Any        =       val[-dim:, :]
                     else:
                            lowercase__:       int        =       val[:dim]
                            lowercase__:       Optional[Any]        =       val[dim : dim * 2]
                            lowercase__:       Any        =       val[-dim:]
              elif "in_proj" in key:
                     # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     lowercase__:       Tuple        =       key.split('.'					)
                     lowercase__:       Union[str, Any]        =       int(key_split[3]					)
                     lowercase__:       Optional[int]        =       config.text_config.hidden_size
                     if "weight" in key:
                            lowercase__:       Tuple        =       val[:dim, :]
                            lowercase__:       Optional[int]        =       val[
                                dim : dim * 2, :
                            ]
                            lowercase__:       Optional[Any]        =       val[-dim:, :]
                     else:
                            lowercase__:       str        =       val[:dim]
                            lowercase__:       List[Any]        =       val[dim : dim * 2]
                            lowercase__:       Dict        =       val[-dim:]
              else:
                     lowercase__:       Tuple        =       rename_key(snake_case					)
                     # squeeze if necessary
                     if (
                         "text_projection.0" in new_name
                         or "text_projection.3" in new_name
                         or "visual_projection.0" in new_name
                         or "visual_projection.3" in new_name
                     ):
                            lowercase__:       Union[str, Any]        =       val.squeeze_()
                     else:
                            lowercase__:       Union[str, Any]        =       val

       return orig_state_dict




def        snake_case_					(  )  ->    List[Any]:
       lowercase__:       List[str]        =       'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:       Optional[int]        =       Image.open(requests.get(snake_case      ,							stream=snake_case					).raw					)
       return im




@torch.no_grad()
def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case="groupvit-gcc-yfcc"      ,							snake_case=False					)  ->    Dict:
       lowercase__:       List[str]        =       GroupViTConfig()
       lowercase__:       Tuple        =       GroupViTModel(snake_case					).eval()

       lowercase__:       int        =       torch.load(snake_case      ,							map_location='cpu'					)['model']
       lowercase__:       List[Any]        =       convert_state_dict(snake_case      ,							snake_case					)
       lowercase__      ,						lowercase__:       Optional[int]        =       model.load_state_dict(snake_case      ,							strict=snake_case					)
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(snake_case					) == 0)

       # verify result
       lowercase__:       str        =       CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'					)
       lowercase__:       List[Any]        =       prepare_img()
       lowercase__:       Union[str, Any]        =       processor(text=['a photo of a cat', 'a photo of a dog']      ,							images=snake_case      ,							padding=snake_case      ,							return_tensors='pt'					)

       with torch.no_grad():
              lowercase__:       int        =       model(**snake_case					)

       if model_name == "groupvit-gcc-yfcc":
              lowercase__:       Optional[Any]        =       torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]					)
       elif model_name == "groupvit-gcc-redcaps":
              lowercase__:       Tuple        =       torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]					)
       else:
              raise ValueError(f'Model name {model_name} not supported.'					)
       assert torch.allclose(outputs.logits_per_image      ,							snake_case      ,							atol=1e-3					)

       processor.save_pretrained(snake_case					)
       model.save_pretrained(snake_case					)
       print('Successfully saved processor and model to'      ,							snake_case					)

       if push_to_hub:
              print('Pushing to the hub...'					)
              processor.push_to_hub(snake_case      ,							organization='nielsr'					)
              model.push_to_hub(snake_case      ,							organization='nielsr'					)


if __name__ == "__main__":
 __lowerCAmelCase			=							argparse.ArgumentParser()
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
 )
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
 parser.add_argument(
     '''--model_name''',
     default='''groupvit-gccy-fcc''',
     type=str,
     help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
 )
 parser.add_argument(
     '''--push_to_hub''',
     action='''store_true''',
     help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
 )
 __lowerCAmelCase			=							parser.parse_args()

 convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)






import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowerCAmelCase			=							logging.getLogger(__name__)




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Optional[int]:
       lowercase__:       Optional[int]        =       np.argmax(snake_case      ,							axis=1					)
       return np.sum(outputs == labels					)




def        snake_case_					(  snake_case					)  ->    Dict:
       with open(snake_case      ,							encoding='utf_8'					) as f:
              lowercase__:       str        =       csv.reader(snake_case					)
              lowercase__:       int        =       []
              next(snake_case					)  # skip the first line
              for line in tqdm(snake_case					):
                     output.append((' '.join(line[1:5]					), line[5], line[6], int(line[-1]					) - 1)					)
       return output




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Tuple:
       lowercase__:       List[Any]        =       []
       for dataset in encoded_datasets:
              lowercase__:       Dict        =       len(snake_case					)
              lowercase__:       int        =       np.zeros((n_batch, 2, input_len)      ,							dtype=np.intaa					)
              lowercase__:       int        =       np.zeros((n_batch, 2)      ,							dtype=np.intaa					)
              lowercase__:       Optional[int]        =       np.full((n_batch, 2, input_len)      ,							fill_value=-1_00      ,							dtype=np.intaa					)
              lowercase__:       Optional[Any]        =       np.zeros((n_batch,)      ,							dtype=np.intaa					)
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(snake_case					):
                     lowercase__:       List[Any]        =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:       List[Any]        =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:       Union[str, Any]        =       with_conta
                     lowercase__:       List[Any]        =       with_conta
                     lowercase__:       Any        =       len(snake_case					) - 1
                     lowercase__:       Dict        =       len(snake_case					) - 1
                     lowercase__:       Optional[Any]        =       with_conta
                     lowercase__:       Tuple        =       with_conta
                     lowercase__:       int        =       mc_label
              lowercase__:       Any        =       (input_ids, mc_token_ids, lm_labels, mc_labels)
              tensor_datasets.append(tuple(torch.tensor(snake_case					) for t in all_inputs					)					)
       return tensor_datasets




def        snake_case_					(  )  ->    Union[str, Any]:
       lowercase__:       Optional[Any]        =       argparse.ArgumentParser()
       parser.add_argument('--model_name'      ,							type=snake_case      ,							default='openai-gpt'      ,							help='pretrained model name'					)
       parser.add_argument('--do_train'      ,							action='store_true'      ,							help='Whether to run training.'					)
       parser.add_argument('--do_eval'      ,							action='store_true'      ,							help='Whether to run eval on the dev set.'					)
       parser.add_argument(
           '--output_dir'      ,							default=snake_case      ,							type=snake_case      ,							required=snake_case      ,							help='The output directory where the model predictions and checkpoints will be written.'      ,							)
       parser.add_argument('--train_dataset'      ,							type=snake_case      ,							default=''					)
       parser.add_argument('--eval_dataset'      ,							type=snake_case      ,							default=''					)
       parser.add_argument('--seed'      ,							type=snake_case      ,							default=42					)
       parser.add_argument('--num_train_epochs'      ,							type=snake_case      ,							default=3					)
       parser.add_argument('--train_batch_size'      ,							type=snake_case      ,							default=8					)
       parser.add_argument('--eval_batch_size'      ,							type=snake_case      ,							default=16					)
       parser.add_argument('--adam_epsilon'      ,							default=1e-8      ,							type=snake_case      ,							help='Epsilon for Adam optimizer.'					)
       parser.add_argument('--max_grad_norm'      ,							type=snake_case      ,							default=1					)
       parser.add_argument(
           '--max_steps'      ,							default=-1      ,							type=snake_case      ,							help=(
               'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
           )      ,							)
       parser.add_argument(
           '--gradient_accumulation_steps'      ,							type=snake_case      ,							default=1      ,							help='Number of updates steps to accumulate before                        performing a backward/update pass.'      ,							)
       parser.add_argument('--learning_rate'      ,							type=snake_case      ,							default=6.25e-5					)
       parser.add_argument('--warmup_steps'      ,							default=0      ,							type=snake_case      ,							help='Linear warmup over warmup_steps.'					)
       parser.add_argument('--lr_schedule'      ,							type=snake_case      ,							default='warmup_linear'					)
       parser.add_argument('--weight_decay'      ,							type=snake_case      ,							default=0.0_1					)
       parser.add_argument('--lm_coef'      ,							type=snake_case      ,							default=0.9					)
       parser.add_argument('--n_valid'      ,							type=snake_case      ,							default=3_74					)

       parser.add_argument('--server_ip'      ,							type=snake_case      ,							default=''      ,							help='Can be used for distant debugging.'					)
       parser.add_argument('--server_port'      ,							type=snake_case      ,							default=''      ,							help='Can be used for distant debugging.'					)
       lowercase__:       List[str]        =       parser.parse_args()
       print(snake_case					)

       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=snake_case					)
              ptvsd.wait_for_attach()

       random.seed(args.seed					)
       np.random.seed(args.seed					)
       torch.manual_seed(args.seed					)
       torch.cuda.manual_seed_all(args.seed					)

       lowercase__:       Any        =       torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
       lowercase__:       Tuple        =       torch.cuda.device_count()
       logger.info('device: {}, n_gpu {}'.format(snake_case      ,							snake_case					)					)

       if not args.do_train and not args.do_eval:
              raise ValueError('At least one of `do_train` or `do_eval` must be True.'					)

       if not os.path.exists(args.output_dir					):
              os.makedirs(args.output_dir					)

       # Load tokenizer and model
       # This loading functions also add new tokens and embeddings called `special tokens`
       # These new embeddings will be fine-tuned on the RocStories dataset
       lowercase__:       Any        =       ['_start_', '_delimiter_', '_classify_']
       lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.model_name					)
       tokenizer.add_tokens(snake_case					)
       lowercase__:       int        =       tokenizer.convert_tokens_to_ids(snake_case					)
       lowercase__:       int        =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
       model.resize_token_embeddings(len(snake_case					)					)
       model.to(snake_case					)

       # Load and encode the datasets
       def tokenize_and_encode(snake_case					):
              if isinstance(snake_case      ,							snake_case					):
                     return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(snake_case					)					)
              elif isinstance(snake_case      ,							snake_case					):
                     return obj
              return [tokenize_and_encode(snake_case					) for o in obj]

       logger.info('Encoding dataset...'					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.train_dataset					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.eval_dataset					)
       lowercase__:       str        =       (train_dataset, eval_dataset)
       lowercase__:       Any        =       tokenize_and_encode(snake_case					)

       # Compute the max input length for the Transformer
       lowercase__:       Optional[Any]        =       model.config.n_positions // 2 - 2
       lowercase__:       Optional[int]        =       max(
           len(story[:max_length]					) + max(len(conta[:max_length]					)      ,							len(conta[:max_length]					)					) + 3
           for dataset in encoded_datasets
           for story, conta, conta, _ in dataset					)
       lowercase__:       List[str]        =       min(snake_case      ,							model.config.n_positions					)  # Max size of input for the pre-trained model

       # Prepare inputs tensors and dataloaders
       lowercase__:       str        =       pre_process_datasets(snake_case      ,							snake_case      ,							snake_case      ,							*snake_case					)
       lowercase__      ,						lowercase__:       Optional[Any]        =       tensor_datasets[0], tensor_datasets[1]

       lowercase__:       List[str]        =       TensorDataset(*snake_case					)
       lowercase__:       Dict        =       RandomSampler(snake_case					)
       lowercase__:       Optional[int]        =       DataLoader(snake_case      ,							sampler=snake_case      ,							batch_size=args.train_batch_size					)

       lowercase__:       str        =       TensorDataset(*snake_case					)
       lowercase__:       str        =       SequentialSampler(snake_case					)
       lowercase__:       Optional[Any]        =       DataLoader(snake_case      ,							sampler=snake_case      ,							batch_size=args.eval_batch_size					)

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     lowercase__:       Union[str, Any]        =       args.max_steps
                     lowercase__:       Tuple        =       args.max_steps // (len(snake_case					) // args.gradient_accumulation_steps) + 1
              else:
                     lowercase__:       Optional[Any]        =       len(snake_case					) // args.gradient_accumulation_steps * args.num_train_epochs

              lowercase__:       str        =       list(model.named_parameters()					)
              lowercase__:       Any        =       ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
              lowercase__:       str        =       [
                  {
                      'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay					)],
                      'weight_decay': args.weight_decay,
                  },
                  {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay					)], 'weight_decay': 0.0},
              ]
              lowercase__:       Tuple        =       AdamW(snake_case      ,							lr=args.learning_rate      ,							eps=args.adam_epsilon					)
              lowercase__:       Tuple        =       get_linear_schedule_with_warmup(
                  snake_case      ,							num_warmup_steps=args.warmup_steps      ,							num_training_steps=snake_case					)

       if args.do_train:
              lowercase__      ,						lowercase__      ,						lowercase__:       int        =       0, 0, None
              model.train()
              for _ in trange(int(args.num_train_epochs					)      ,							desc='Epoch'					):
                     lowercase__:       str        =       0
                     lowercase__:       Optional[Any]        =       0
                     lowercase__:       List[Any]        =       tqdm(snake_case      ,							desc='Training'					)
                     for step, batch in enumerate(snake_case					):
                            lowercase__:       Union[str, Any]        =       tuple(t.to(snake_case					) for t in batch					)
                            lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       List[Any]        =       batch
                            lowercase__:       List[str]        =       model(snake_case      ,							mc_token_ids=snake_case      ,							lm_labels=snake_case      ,							mc_labels=snake_case					)
                            lowercase__:       Optional[Any]        =       args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            lowercase__:       Union[str, Any]        =       (
                                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                            )
                            nb_tr_steps += 1
                            lowercase__:       Tuple        =       'Training loss: {:.2e} lr: {:.2e}'.format(snake_case      ,							scheduler.get_lr()[0]					)

    # Save a trained model
       if args.do_train:
              # Save a trained model, configuration and tokenizer
              lowercase__:       Any        =       model.module if hasattr(snake_case      ,							'module'					) else model  # Only save the model itself

              # If we save using the predefined names, we can load using `from_pretrained`
              lowercase__:       Tuple        =       os.path.join(args.output_dir      ,							snake_case					)
              lowercase__:       List[str]        =       os.path.join(args.output_dir      ,							snake_case					)

              torch.save(model_to_save.state_dict()      ,							snake_case					)
              model_to_save.config.to_json_file(snake_case					)
              tokenizer.save_vocabulary(args.output_dir					)

              # Load a trained model and vocabulary that you have fine-tuned
              lowercase__:       Optional[Any]        =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir					)
              lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.output_dir					)
              model.to(snake_case					)

       if args.do_eval:
              model.eval()
              lowercase__      ,						lowercase__:       Optional[Any]        =       0, 0
              lowercase__      ,						lowercase__:       List[Any]        =       0, 0
              for batch in tqdm(snake_case      ,							desc='Evaluating'					):
                     lowercase__:       str        =       tuple(t.to(snake_case					) for t in batch					)
                     lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       Union[str, Any]        =       batch
                     with torch.no_grad():
                            lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       Any        =       model(
                                snake_case      ,							mc_token_ids=snake_case      ,							lm_labels=snake_case      ,							mc_labels=snake_case					)

                     lowercase__:       Dict        =       mc_logits.detach().cpu().numpy()
                     lowercase__:       Tuple        =       mc_labels.to('cpu'					).numpy()
                     lowercase__:       Dict        =       accuracy(snake_case      ,							snake_case					)

                     eval_loss += mc_loss.mean().item()
                     eval_accuracy += tmp_eval_accuracy

                     nb_eval_examples += input_ids.size(0					)
                     nb_eval_steps += 1

              lowercase__:       Optional[int]        =       eval_loss / nb_eval_steps
              lowercase__:       Optional[int]        =       eval_accuracy / nb_eval_examples
              lowercase__:       int        =       tr_loss / nb_tr_steps if args.do_train else None
              lowercase__:       Optional[Any]        =       {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

              lowercase__:       Dict        =       os.path.join(args.output_dir      ,							'eval_results.txt'					)
              with open(snake_case      ,							'w'					) as writer:
                     logger.info('***** Eval results *****'					)
                     for key in sorted(result.keys()					):
                            logger.info('  %s = %s'      ,							snake_case      ,							str(result[key]					)					)
                            writer.write('%s = %s\n' % (key, str(result[key]					))					)


if __name__ == "__main__":
 main()


