

"""simple docstring"""




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[Any]							=							['image_processor', 'tokenizer']
			lowerCamelCase__   :		List[str]							=							'AutoImageProcessor'
			lowerCamelCase__   :		int							=							'AutoTokenizer'

			def __init__(					self				:						str    ,				__UpperCAmelCase				:						List[Any]=None    ,				__UpperCAmelCase				:						List[str]=None    ,				**__UpperCAmelCase				:						Dict       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""    ,				__UpperCAmelCase    ,				)
									SCREAMING_SNAKE_CASE__								=      kwargs.pop("""feature_extractor"""       )

						SCREAMING_SNAKE_CASE__								=      image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""       )
						if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""       )

						super().__init__(__UpperCAmelCase    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.image_processor
						SCREAMING_SNAKE_CASE__								=      False

			def __call__(					self				:						List[Any]    ,				*__UpperCAmelCase				:						List[str]    ,				**__UpperCAmelCase				:						str       )   ->    List[Any]:
						# For backward compatibility
						if self._in_target_context_manager:
									return self.current_processor(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      kwargs.pop("""images"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      kwargs.pop("""text"""    ,				__UpperCAmelCase       )
						if len(__UpperCAmelCase       ) > 0:
									SCREAMING_SNAKE_CASE__								=      args[0]
									SCREAMING_SNAKE_CASE__								=      args[1:]

						if images is None and text is None:
									raise ValueError("""You need to specify either an `images` or `text` input to process."""       )

						if images is not None:
									SCREAMING_SNAKE_CASE__								=      self.image_processor(__UpperCAmelCase    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase       )
						if text is not None:
									SCREAMING_SNAKE_CASE__								=      self.tokenizer(__UpperCAmelCase    ,				**__UpperCAmelCase       )

						if text is None:
									return inputs
						elif images is None:
									return encodings
						else:
									SCREAMING_SNAKE_CASE__								=      encodings["""input_ids"""]
									return inputs

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				*__UpperCAmelCase				:						List[Any]    ,				**__UpperCAmelCase				:						Any       )   ->    int:
						return self.tokenizer.batch_decode(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						List[str]       )   ->    Optional[int]:
						return self.tokenizer.decode(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Tuple:
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your images inputs, or in a separate call."""       )
						SCREAMING_SNAKE_CASE__								=      True
						SCREAMING_SNAKE_CASE__								=      self.tokenizer
						yield
						SCREAMING_SNAKE_CASE__								=      self.image_processor
						SCREAMING_SNAKE_CASE__								=      False

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						str=False    ,				__UpperCAmelCase				:						Optional[int]=None       )   ->    Tuple:
						if added_vocab is None:
									SCREAMING_SNAKE_CASE__								=      self.tokenizer.get_added_vocab()

						SCREAMING_SNAKE_CASE__								=      {}

						while tokens:
									SCREAMING_SNAKE_CASE__								=      re.search(r"""<s_(.*?)>"""    ,				__UpperCAmelCase    ,				re.IGNORECASE       )
									if start_token is None:
												break
									SCREAMING_SNAKE_CASE__								=      start_token.group(1       )
									SCREAMING_SNAKE_CASE__								=      re.search(rF"""</s_{key}>"""    ,				__UpperCAmelCase    ,				re.IGNORECASE       )
									SCREAMING_SNAKE_CASE__								=      start_token.group()
									if end_token is None:
												SCREAMING_SNAKE_CASE__								=      tokens.replace(__UpperCAmelCase    ,				""""""       )
									else:
												SCREAMING_SNAKE_CASE__								=      end_token.group()
												SCREAMING_SNAKE_CASE__								=      re.escape(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      re.escape(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""    ,				__UpperCAmelCase    ,				re.IGNORECASE       )
												if content is not None:
															SCREAMING_SNAKE_CASE__								=      content.group(1       ).strip()
															if r"<s_" in content and r"</s_" in content:  # non-leaf node
																		SCREAMING_SNAKE_CASE__								=      self.tokenajson(__UpperCAmelCase    ,				is_inner_value=__UpperCAmelCase    ,				added_vocab=__UpperCAmelCase       )
																		if value:
																					if len(__UpperCAmelCase       ) == 1:
																								SCREAMING_SNAKE_CASE__								=      value[0]
																					SCREAMING_SNAKE_CASE__								=      value
															else:  # leaf nodes
																		SCREAMING_SNAKE_CASE__								=      []
																		for leaf in content.split(r"""<sep/>"""       ):
																					SCREAMING_SNAKE_CASE__								=      leaf.strip()
																					if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																								SCREAMING_SNAKE_CASE__								=      leaf[1:-2]  # for categorical special tokens
																					output[key].append(__UpperCAmelCase       )
																		if len(output[key]       ) == 1:
																					SCREAMING_SNAKE_CASE__								=      output[key][0]

												SCREAMING_SNAKE_CASE__								=      tokens[tokens.find(__UpperCAmelCase       ) + len(__UpperCAmelCase       ) :].strip()
												if tokens[:6] == r"<sep/>":  # non-leaf nodes
															return [output] + self.tokenajson(tokens[6:]    ,				is_inner_value=__UpperCAmelCase    ,				added_vocab=__UpperCAmelCase       )

						if len(__UpperCAmelCase       ):
									return [output] if is_inner_value else output
						else:
									return [] if is_inner_value else {"text_sequence": tokens}

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Tuple:
						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,				__UpperCAmelCase    ,				)
						return self.image_processor_class





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[Any]:
						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,				__UpperCAmelCase    ,				)
						return self.image_processor





"""simple docstring"""




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
)
A_							:			Any											= logging.getLogger(__name__)

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      np.argmax(snake_case__		,				axis=1    )
			return np.sum(outputs == labels    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				encoding="""utf_8"""    ) as f:
						SCREAMING_SNAKE_CASE__								=      csv.reader(snake_case__    )
						SCREAMING_SNAKE_CASE__								=      []
						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 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      []
			for dataset in encoded_datasets:
						SCREAMING_SNAKE_CASE__								=      len(snake_case__    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch, 2, input_len)		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch, 2)		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.full((n_batch, 2, input_len)		,				fill_value=-1_00		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch,)		,				dtype=np.intaa    )
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(snake_case__    ):
									SCREAMING_SNAKE_CASE__								=      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE__								=      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) - 1
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) - 1
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      mc_label
						SCREAMING_SNAKE_CASE__								=      (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 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      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=0.01    )
			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."""    )
			SCREAMING_SNAKE_CASE__								=      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    )

			SCREAMING_SNAKE_CASE__								=      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
			SCREAMING_SNAKE_CASE__								=      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
			SCREAMING_SNAKE_CASE__								=      ["""_start_""", """_delimiter_""", """_classify_"""]
			SCREAMING_SNAKE_CASE__								=      OpenAIGPTTokenizer.from_pretrained(args.model_name    )
			tokenizer.add_tokens(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.convert_tokens_to_ids(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      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..."""    )
			SCREAMING_SNAKE_CASE__								=      load_rocstories_dataset(args.train_dataset    )
			SCREAMING_SNAKE_CASE__								=      load_rocstories_dataset(args.eval_dataset    )
			SCREAMING_SNAKE_CASE__								=      (train_dataset, eval_dataset)
			SCREAMING_SNAKE_CASE__								=      tokenize_and_encode(snake_case__    )

			# Compute the max input length for the Transformer
			SCREAMING_SNAKE_CASE__								=      model.config.n_positions // 2 - 2
			SCREAMING_SNAKE_CASE__								=      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    )
			SCREAMING_SNAKE_CASE__								=      min(snake_case__		,				model.config.n_positions    )  # Max size of input for the pre-trained model

			# Prepare inputs tensors and dataloaders
			SCREAMING_SNAKE_CASE__								=      pre_process_datasets(snake_case__		,				snake_case__		,				snake_case__		,				*snake_case__    )
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      tensor_datasets[0], tensor_datasets[1]

			SCREAMING_SNAKE_CASE__								=      TensorDataset(*snake_case__    )
			SCREAMING_SNAKE_CASE__								=      RandomSampler(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				sampler=snake_case__		,				batch_size=args.train_batch_size    )

			SCREAMING_SNAKE_CASE__								=      TensorDataset(*snake_case__    )
			SCREAMING_SNAKE_CASE__								=      SequentialSampler(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				sampler=snake_case__		,				batch_size=args.eval_batch_size    )

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									SCREAMING_SNAKE_CASE__								=      args.max_steps
									SCREAMING_SNAKE_CASE__								=      args.max_steps // (len(snake_case__    ) // args.gradient_accumulation_steps) + 1
						else:
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) // args.gradient_accumulation_steps * args.num_train_epochs

						SCREAMING_SNAKE_CASE__								=      list(model.named_parameters()    )
						SCREAMING_SNAKE_CASE__								=      ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
						SCREAMING_SNAKE_CASE__								=      [
						    {
						        """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},
						]
						SCREAMING_SNAKE_CASE__								=      AdamW(snake_case__		,				lr=args.learning_rate		,				eps=args.adam_epsilon    )
						SCREAMING_SNAKE_CASE__								=      get_linear_schedule_with_warmup(
						    snake_case__		,				num_warmup_steps=args.warmup_steps		,				num_training_steps=snake_case__    )

			if args.do_train:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs    )		,				desc="""Epoch"""    ):
									SCREAMING_SNAKE_CASE__								=      0
									SCREAMING_SNAKE_CASE__								=      0
									SCREAMING_SNAKE_CASE__								=      tqdm(snake_case__		,				desc="""Training"""    )
									for step, batch in enumerate(snake_case__    ):
												SCREAMING_SNAKE_CASE__								=      tuple(t.to(snake_case__    ) for t in batch    )
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      batch
												SCREAMING_SNAKE_CASE__								=      model(snake_case__		,				mc_token_ids=snake_case__		,				lm_labels=snake_case__		,				mc_labels=snake_case__    )
												SCREAMING_SNAKE_CASE__								=      args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												SCREAMING_SNAKE_CASE__								=      (
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												SCREAMING_SNAKE_CASE__								=      """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
						SCREAMING_SNAKE_CASE__								=      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`
						SCREAMING_SNAKE_CASE__								=      os.path.join(args.output_dir		,				snake_case__    )
						SCREAMING_SNAKE_CASE__								=      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
						SCREAMING_SNAKE_CASE__								=      OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
						SCREAMING_SNAKE_CASE__								=      OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
						model.to(snake_case__    )

			if args.do_eval:
						model.eval()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0
						for batch in tqdm(snake_case__		,				desc="""Evaluating"""    ):
									SCREAMING_SNAKE_CASE__								=      tuple(t.to(snake_case__    ) for t in batch    )
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      batch
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      model(
												    snake_case__		,				mc_token_ids=snake_case__		,				lm_labels=snake_case__		,				mc_labels=snake_case__    )

									SCREAMING_SNAKE_CASE__								=      mc_logits.detach().cpu().numpy()
									SCREAMING_SNAKE_CASE__								=      mc_labels.to("""cpu"""    ).numpy()
									SCREAMING_SNAKE_CASE__								=      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

						SCREAMING_SNAKE_CASE__								=      eval_loss / nb_eval_steps
						SCREAMING_SNAKE_CASE__								=      eval_accuracy / nb_eval_examples
						SCREAMING_SNAKE_CASE__								=      tr_loss / nb_tr_steps if args.do_train else None
						SCREAMING_SNAKE_CASE__								=      {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

						SCREAMING_SNAKE_CASE__								=      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()


