


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      os.path.abspath(SCREAMING_SNAKE_CASE     )
		logger.info(f'Converting TensorFlow checkpoint from {tf_path}'     )
		# Load weights from TF model
		lowercase__            =      tf.train.list_variables(SCREAMING_SNAKE_CASE     )
		lowercase__            =      []
		lowercase__            =      []
		lowercase__            =      []
		for full_name, shape in init_vars:
				# logger.info(f"Loading TF weight {name} with shape {shape}")
				lowercase__            =      full_name.split('''/'''     )
				if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
						logger.info(f'Skipping non-model layer {full_name}'     )
						continue
				if "optimizer" in full_name:
						logger.info(f'Skipping optimization layer {full_name}'     )
						continue
				if name[0] == "model":
						# ignore initial 'model'
						lowercase__            =      name[1:]
				# figure out how many levels deep the name is
				lowercase__            =      0
				for _name in name:
						if _name.startswith('''layer_with_weights'''     ):
								depth += 1
						else:
								break
				layer_depth.append(SCREAMING_SNAKE_CASE     )
				# read data
				lowercase__            =      tf.train.load_variable(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
				names.append('''/'''.join(SCREAMING_SNAKE_CASE     )     )
				arrays.append(SCREAMING_SNAKE_CASE     )
		logger.info(f'Read a total of {len(SCREAMING_SNAKE_CASE     ):,} layers'     )

		# Sanity check
		if len(set(SCREAMING_SNAKE_CASE     )     ) != 1:
				raise ValueError(f'Found layer names with different depths (layer depth {list(set(SCREAMING_SNAKE_CASE     )     )})'     )
		lowercase__            =      list(set(SCREAMING_SNAKE_CASE     )     )[0]
		if layer_depth != 1:
				raise ValueError(
				    '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
				    ''' heads.'''     )

		# convert layers
		logger.info('''Converting weights...'''     )
		for full_name, array in zip(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):
				lowercase__            =      full_name.split('''/'''     )
				lowercase__            =      model
				lowercase__            =      []
				for i, m_name in enumerate(SCREAMING_SNAKE_CASE     ):
						if m_name == ".ATTRIBUTES":
								# variable names end with .ATTRIBUTES/VARIABLE_VALUE
								break
						if m_name.startswith('''layer_with_weights'''     ):
								lowercase__            =      int(m_name.split('''-'''     )[-1]     )
								if layer_num <= 2:
										# embedding layers
										# layer_num 0: word_embeddings
										# layer_num 1: position_embeddings
										# layer_num 2: token_type_embeddings
										continue
								elif layer_num == 3:
										# embedding LayerNorm
										trace.extend(['''embeddings''', '''LayerNorm''']     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''embeddings'''     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''LayerNorm'''     )
								elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
										# encoder layers
										trace.extend(['''encoder''', '''layer''', str(layer_num - 4     )]     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''encoder'''     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''layer'''     )
										lowercase__            =      pointer[layer_num - 4]
								elif layer_num == config.num_hidden_layers + 4:
										# pooler layer
										trace.extend(['''pooler''', '''dense''']     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''pooler'''     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''dense'''     )
						elif m_name == "embeddings":
								trace.append('''embeddings'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''embeddings'''     )
								if layer_num == 0:
										trace.append('''word_embeddings'''     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''word_embeddings'''     )
								elif layer_num == 1:
										trace.append('''position_embeddings'''     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''position_embeddings'''     )
								elif layer_num == 2:
										trace.append('''token_type_embeddings'''     )
										lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''token_type_embeddings'''     )
								else:
										raise ValueError(f'Unknown embedding layer with name {full_name}'     )
								trace.append('''weight'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''weight'''     )
						elif m_name == "_attention_layer":
								# self-attention layer
								trace.extend(['''attention''', '''self''']     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''attention'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''self'''     )
						elif m_name == "_attention_layer_norm":
								# output attention norm
								trace.extend(['''attention''', '''output''', '''LayerNorm''']     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''attention'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''output'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''LayerNorm'''     )
						elif m_name == "_attention_output_dense":
								# output attention dense
								trace.extend(['''attention''', '''output''', '''dense''']     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''attention'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''output'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''dense'''     )
						elif m_name == "_output_dense":
								# output dense
								trace.extend(['''output''', '''dense''']     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''output'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''dense'''     )
						elif m_name == "_output_layer_norm":
								# output dense
								trace.extend(['''output''', '''LayerNorm''']     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''output'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''LayerNorm'''     )
						elif m_name == "_key_dense":
								# attention key
								trace.append('''key'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''key'''     )
						elif m_name == "_query_dense":
								# attention query
								trace.append('''query'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''query'''     )
						elif m_name == "_value_dense":
								# attention value
								trace.append('''value'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''value'''     )
						elif m_name == "_intermediate_dense":
								# attention intermediate dense
								trace.extend(['''intermediate''', '''dense''']     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''intermediate'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''dense'''     )
						elif m_name == "_output_layer_norm":
								# output layer norm
								trace.append('''output'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''output'''     )
						# weights & biases
						elif m_name in ["bias", "beta"]:
								trace.append('''bias'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''bias'''     )
						elif m_name in ["kernel", "gamma"]:
								trace.append('''weight'''     )
								lowercase__            =      getattr(SCREAMING_SNAKE_CASE						,				'''weight'''     )
						else:
								logger.warning(f'Ignored {m_name}'     )
        # for certain layers reshape is necessary
				lowercase__            =      '''.'''.join(SCREAMING_SNAKE_CASE     )
				if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''						,				SCREAMING_SNAKE_CASE     ) or re.match(
				    R'''(\S+)\.attention\.output\.dense\.weight'''						,				SCREAMING_SNAKE_CASE     ):
						lowercase__            =      array.reshape(pointer.data.shape     )
				if "kernel" in full_name:
						lowercase__            =      array.transpose()
				if pointer.shape == array.shape:
						lowercase__            =      torch.from_numpy(SCREAMING_SNAKE_CASE     )
				else:
						raise ValueError(
						    f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
						    f' {array.shape}'     )
				logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'     )
		return model
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		logger.info(f'Loading model based on config from {config_path}...'     )
		lowercase__            =      BertConfig.from_json_file(SCREAMING_SNAKE_CASE     )
		lowercase__            =      BertModel(SCREAMING_SNAKE_CASE     )

		# Load weights from checkpoint
		logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'     )
		load_tfa_weights_in_bert(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		# Save pytorch-model
		logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'     )
		torch.save(model.state_dict()						,				SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			parser.add_argument(
			    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
			)
			parser.add_argument(
			    '--bert_config_file',
			    type=str,
			    required=True,
			    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
			)
			parser.add_argument(
			    '--pytorch_dump_path',
			    type=str,
			    required=True,
			    help='Path to the output PyTorch model (must include filename).',
			)
			lowerCAmelCase       =							parser.parse_args()
			convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase       =							logging.getLogger(__name__)


lowerCAmelCase       =							list(MODEL_WITH_LM_HEAD_MAPPING.keys())
lowerCAmelCase       =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class        _a							:
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
							            ''' scratch.'''
							        )
							    }     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase__			)}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,	)


@dataclass
class        _a							:
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The input training data file (a text file).'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''The input training data files (multiple files in glob format). '''
							            '''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
							        )
							    }     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''}     ,	)
							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''}     ,	)

							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''}			)
							_lowercase	:			bool        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''Whether ot not to use whole word mask.'''}			)
							_lowercase	:			float        = field(
							    default=0.15     ,	metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}			)
							_lowercase	:			float        = field(
							    default=1 / 6     ,	metadata={
							        '''help''': (
							            '''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
							            ''' modeling.'''
							        )
							    }     ,	)
							_lowercase	:			int        = field(
							    default=5     ,	metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''}			)

							_lowercase	:			int        = field(
							    default=-1     ,	metadata={
							        '''help''': (
							            '''Optional input sequence length after tokenization.'''
							            '''The training dataset will be truncated in block of this size for training.'''
							            '''Default to the model max input length for single sentence inputs (take into account special tokens).'''
							        )
							    }     ,	)
							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE = False						,				SCREAMING_SNAKE_CASE = None						,				):


		"""simple docstring"""




		def _dataset(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=None     ):
				if args.line_by_line:
						if ref_path is not None:
								if not args.whole_word_mask or not args.mlm:
										raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''     )
								return LineByLineWithRefDataset(
								    tokenizer=SCREAMING_SNAKE_CASE						,				file_path=SCREAMING_SNAKE_CASE						,				block_size=args.block_size						,				ref_path=SCREAMING_SNAKE_CASE						,				)

						return LineByLineTextDataset(tokenizer=SCREAMING_SNAKE_CASE						,				file_path=SCREAMING_SNAKE_CASE						,				block_size=args.block_size     )
				else:
						return TextDataset(
						    tokenizer=SCREAMING_SNAKE_CASE						,				file_path=SCREAMING_SNAKE_CASE						,				block_size=args.block_size						,				overwrite_cache=args.overwrite_cache						,				cache_dir=SCREAMING_SNAKE_CASE						,				)

		if evaluate:
				return _dataset(args.eval_data_file						,				args.eval_ref_file     )
		elif args.train_data_files:
				return ConcatDataset([_dataset(SCREAMING_SNAKE_CASE     ) for f in glob(args.train_data_files     )]     )
		else:
				return _dataset(args.train_data_file						,				args.train_ref_file     )
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
		lowercase__     ,		lowercase__     ,		lowercase__            =      parser.parse_args_into_dataclasses()

		if data_args.eval_data_file is None and training_args.do_eval:
				raise ValueError(
				    '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
				    '''or remove the --do_eval argument.'''     )
		if (
		    os.path.exists(training_args.output_dir     )
		    and os.listdir(training_args.output_dir     )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
				    ''' --overwrite_output_dir to overcome.'''     )

		# Setup logging
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,				datefmt='''%m/%d/%Y %H:%M:%S'''						,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,				)
		logger.warning(
		    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''						,				training_args.local_rank						,				training_args.device						,				training_args.n_gpu						,				bool(training_args.local_rank != -1     )						,				training_args.fpaa						,				)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank     ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info('''Training/evaluation parameters %s'''						,				SCREAMING_SNAKE_CASE     )

		# Set seed
		set_seed(training_args.seed     )

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

		if model_args.config_name:
				lowercase__            =      AutoConfig.from_pretrained(model_args.config_name						,				cache_dir=model_args.cache_dir     )
		elif model_args.model_name_or_path:
				lowercase__            =      AutoConfig.from_pretrained(model_args.model_name_or_path						,				cache_dir=model_args.cache_dir     )
		else:
				lowercase__            =      CONFIG_MAPPING[model_args.model_type]()
				logger.warning('''You are instantiating a new config instance from scratch.'''     )

		if model_args.tokenizer_name:
				lowercase__            =      AutoTokenizer.from_pretrained(model_args.tokenizer_name						,				cache_dir=model_args.cache_dir     )
		elif model_args.model_name_or_path:
				lowercase__            =      AutoTokenizer.from_pretrained(model_args.model_name_or_path						,				cache_dir=model_args.cache_dir     )
		else:
				raise ValueError(
				    '''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
				    ''' script, save it,and load it from here, using --tokenizer_name'''     )

		if model_args.model_name_or_path:
				lowercase__            =      AutoModelWithLMHead.from_pretrained(
				    model_args.model_name_or_path						,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )						,				config=SCREAMING_SNAKE_CASE						,				cache_dir=model_args.cache_dir						,				)
		else:
				logger.info('''Training new model from scratch'''     )
				lowercase__            =      AutoModelWithLMHead.from_config(SCREAMING_SNAKE_CASE     )

		model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE     )     )

		if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
				raise ValueError(
				    '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
				    '''--mlm flag (masked language modeling).'''     )

		if data_args.block_size <= 0:
				lowercase__            =      tokenizer.max_len
				# Our input block size will be the max possible for the model
		else:
				lowercase__            =      min(data_args.block_size						,				tokenizer.max_len     )

		# Get datasets

		lowercase__            =      (
		    get_dataset(SCREAMING_SNAKE_CASE						,				tokenizer=SCREAMING_SNAKE_CASE						,				cache_dir=model_args.cache_dir     ) if training_args.do_train else None
		)
		lowercase__            =      (
		    get_dataset(SCREAMING_SNAKE_CASE						,				tokenizer=SCREAMING_SNAKE_CASE						,				evaluate=SCREAMING_SNAKE_CASE						,				cache_dir=model_args.cache_dir     )
		    if training_args.do_eval
		    else None
		)
		if config.model_type == "xlnet":
				lowercase__            =      DataCollatorForPermutationLanguageModeling(
				    tokenizer=SCREAMING_SNAKE_CASE						,				plm_probability=data_args.plm_probability						,				max_span_length=data_args.max_span_length						,				)
		else:
				if data_args.mlm and data_args.whole_word_mask:
						lowercase__            =      DataCollatorForWholeWordMask(
						    tokenizer=SCREAMING_SNAKE_CASE						,				mlm_probability=data_args.mlm_probability     )
				else:
						lowercase__            =      DataCollatorForLanguageModeling(
						    tokenizer=SCREAMING_SNAKE_CASE						,				mlm=data_args.mlm						,				mlm_probability=data_args.mlm_probability     )

    # Initialize our Trainer
		lowercase__            =      Trainer(
		    model=SCREAMING_SNAKE_CASE						,				args=SCREAMING_SNAKE_CASE						,				data_collator=SCREAMING_SNAKE_CASE						,				train_dataset=SCREAMING_SNAKE_CASE						,				eval_dataset=SCREAMING_SNAKE_CASE						,				prediction_loss_only=SCREAMING_SNAKE_CASE						,				)

		# Training
		if training_args.do_train:
				lowercase__            =      (
				    model_args.model_name_or_path
				    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     )
				    else None
				)
				trainer.train(model_path=SCREAMING_SNAKE_CASE     )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir     )

    # Evaluation
		lowercase__            =      {}
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''     )

				lowercase__            =      trainer.evaluate()

				lowercase__            =      math.exp(eval_output['''eval_loss''']     )
				lowercase__            =      {'''perplexity''': perplexity}

				lowercase__            =      os.path.join(training_args.output_dir						,				'''eval_results_lm.txt'''     )
				if trainer.is_world_master():
						with open(SCREAMING_SNAKE_CASE						,				'''w'''     ) as writer:
								logger.info('''***** Eval results *****'''     )
								for key in sorted(result.keys()     ):
										logger.info('''  %s = %s'''						,				SCREAMING_SNAKE_CASE						,				str(result[key]     )     )
										writer.write('''%s = %s\n''' % (key, str(result[key]     ))     )

				results.update(SCREAMING_SNAKE_CASE     )

		return results
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		main()


if __name__ == "__main__":
			main()


