import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.electra.modeling_flax_electra import (
        FlaxElectraForCausalLM,
        FlaxElectraForMaskedLM,
        FlaxElectraForMultipleChoice,
        FlaxElectraForPreTraining,
        FlaxElectraForQuestionAnswering,
        FlaxElectraForSequenceClassification,
        FlaxElectraForTokenClassification,
        FlaxElectraModel,
    )
class 				a_ (						unittest.TestCase						):






 '''simple docstring'''






 def __init__(			self					:  str			,    lowercase__					:  Dict			,    lowercase__					:  int=13			,    lowercase__					:  Union[str, Any]=7			,    lowercase__					:  List[Any]=True			,    lowercase__					:  Dict=True			,    lowercase__					:  Any=True			,    lowercase__					:  Tuple=True			,    lowercase__					:  List[Any]=99			,    lowercase__					:  List[Any]=24			,    lowercase__					:  Union[str, Any]=32			,    lowercase__					:  Tuple=5			,    lowercase__					:  Dict=4			,    lowercase__					:  List[Any]=37			,    lowercase__					:  Optional[Any]="gelu"			,    lowercase__					:  Optional[int]=0.1			,    lowercase__					:  Any=0.1			,    lowercase__					:  int=512			,    lowercase__					:  Any=16			,    lowercase__					:  List[Any]=2			,    lowercase__					:  Optional[int]=0.02			,    lowercase__					:  Optional[int]=4			,    ):







        '''simple docstring'''



        lowerCAmelCase__     	=					parent
        lowerCAmelCase__     	=					batch_size
        lowerCAmelCase__     	=					seq_length
        lowerCAmelCase__     	=					is_training
        lowerCAmelCase__     	=					use_attention_mask
        lowerCAmelCase__     	=					use_token_type_ids
        lowerCAmelCase__     	=					use_labels
        lowerCAmelCase__     	=					vocab_size
        lowerCAmelCase__     	=					hidden_size
        lowerCAmelCase__     	=					embedding_size
        lowerCAmelCase__     	=					num_hidden_layers
        lowerCAmelCase__     	=					num_attention_heads
        lowerCAmelCase__     	=					intermediate_size
        lowerCAmelCase__     	=					hidden_act
        lowerCAmelCase__     	=					hidden_dropout_prob
        lowerCAmelCase__     	=					attention_probs_dropout_prob
        lowerCAmelCase__     	=					max_position_embeddings
        lowerCAmelCase__     	=					type_vocab_size
        lowerCAmelCase__     	=					type_sequence_label_size
        lowerCAmelCase__     	=					initializer_range
        lowerCAmelCase__     	=					num_choices




 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     	=					ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size)

        lowerCAmelCase__     	=					None
        if self.use_attention_mask:
               lowerCAmelCase__     	=					random_attention_mask([self.batch_size, self.seq_length])

        lowerCAmelCase__     	=					None
        if self.use_token_type_ids:
               lowerCAmelCase__     	=					ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size)

        lowerCAmelCase__     	=					ElectraConfig(
            vocab_size=self.vocab_size			,    hidden_size=self.hidden_size			,    embedding_size=self.embedding_size			,    num_hidden_layers=self.num_hidden_layers			,    num_attention_heads=self.num_attention_heads			,    intermediate_size=self.intermediate_size			,    hidden_act=self.hidden_act			,    hidden_dropout_prob=self.hidden_dropout_prob			,    attention_probs_dropout_prob=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    type_vocab_size=self.type_vocab_size			,    initializer_range=self.initializer_range			,    )

        return config, input_ids, token_type_ids, attention_mask




 def 		__snake_case						(			self					:  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.prepare_config_and_inputs()
        lowerCAmelCase__	,					lowerCAmelCase__	,					lowerCAmelCase__	,					lowerCAmelCase__     	=					config_and_inputs
        lowerCAmelCase__     	=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
        return config, inputs_dict






@require_flax
class 				a_ (						SCREAMING_SNAKE_CASE       ,     unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_	 =					True

 UpperCAmelCase_	 =					(
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )




 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     	=					FlaxElectraModelTester(self)




 @slow
 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        for model_class_name in self.all_model_classes:
               if model_class_name == FlaxElectraForMaskedLM:
                      lowerCAmelCase__     	=					model_class_name.from_pretrained('google/electra-small-generator')
               else:
                      lowerCAmelCase__     	=					model_class_name.from_pretrained('google/electra-small-discriminator')
               lowerCAmelCase__     	=					model(np.ones((1, 1)))
               self.assertIsNotNone(lowercase__)







"""simple docstring"""





import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
    from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
    _lowerCamelCase       =	True
    from torch.cuda.amp import autocast


_lowerCamelCase       =	logging.getLogger(__name__)





@dataclass
class 			snake_case		:
      lowerCamelCase__						= field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}   )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Whether to log verbose messages or not.'''} ,     )







def 		lowerCAmelCase_	(  lowercase_   :							ModelArguments ,      lowercase_   :							TrainingArguments		):

 '''simple docstring'''






 logging.basicConfig(
     format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,      datefmt='''%m/%d/%Y %H:%M:%S''' ,      handlers=[logging.StreamHandler(sys.stdout		)] ,      )
 __SCREAMING_SNAKE_CASE :			Dict												=       logging.WARNING
 if model_args.verbose_logging:
  __SCREAMING_SNAKE_CASE :			List[Any]												=       logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank		):
  __SCREAMING_SNAKE_CASE :			Any												=       logging.INFO
 logger.setLevel(lowercase_		)





@dataclass
class 			snake_case		:
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}   )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}   )
      lowerCamelCase__						= field(
          default='''train''' ,     metadata={
              '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
          } ,     )
      lowerCamelCase__						= field(
          default='''validation''' ,     metadata={
              '''help''': (
                  '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
              )
          } ,     )
      lowerCamelCase__						= field(
          default='''text''' ,     metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''} ,     )
      lowerCamelCase__						= field(
          default='''file''' ,     metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''} ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''} ,     )
      lowerCamelCase__						= field(
          default='''librispeech''' ,     metadata={
              '''help''': (
                  '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
                  ''' \'buckwalter\'.'''
              )
          } ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}   )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''The number of processes to use for the preprocessing.'''} ,     )







@dataclass
class 			snake_case		:
      lowerCamelCase__						= False
      lowerCamelCase__						= None
      lowerCamelCase__						= "|"
      lowerCamelCase__						= field(default_factory=__UpperCAmelCase   )
      lowerCamelCase__						= field(default_factory=__UpperCAmelCase   )
      lowerCamelCase__						= None

      @classmethod
      def 						SCREAMING_SNAKE_CASE_			(			cls   :Any						,							_lowerCamelCase   :str						):
       if name == "librispeech":
        return cls()
       if name == "timit":
        return cls(
            do_lower_case=_lowerCamelCase						,							translation_table=str.maketrans({'''-''': ''' '''}						)						,							)
       if name == "buckwalter":
        __SCREAMING_SNAKE_CASE :			int												=       {
            '''-''': ''' ''',  # sometimes used to represent pauses
            '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
        }
        return cls(
            vocab_file=pathlib.Path(_lowerCamelCase						).parent.joinpath('''vocab/buckwalter.json'''						)						,							word_delimiter_token='''/'''						,							translation_table=str.maketrans(_lowerCamelCase						)						,							words_to_remove={'''sil'''}						,							untransliterator=arabic.buckwalter.untransliterate						,							)
       raise ValueError(f'''Unsupported orthography: \'{name}\'.'''						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :str						):
       # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
       if len(self.translation_table						) > 0:
        __SCREAMING_SNAKE_CASE :			int												=       text.translate(self.translation_table						)
       if len(self.words_to_remove						) == 0:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       ''' '''.join(text.split()						)  # clean up whitespaces
       else:
        __SCREAMING_SNAKE_CASE :			List[str]												=       ''' '''.join(w for w in text.split() if w not in self.words_to_remove						)  # and clean up whilespaces
       return text



      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :ModelArguments						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						)
       if self.vocab_file:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       WavaVecaCTCTokenizer(
            self.vocab_file						,							cache_dir=model_args.cache_dir						,							do_lower_case=self.do_lower_case						,							word_delimiter_token=self.word_delimiter_token						,							)
       else:
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       WavaVecaCTCTokenizer.from_pretrained(
            model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							do_lower_case=self.do_lower_case						,							word_delimiter_token=self.word_delimiter_token						,							)
       return WavaVecaProcessor(_lowerCamelCase						,							_lowerCamelCase						)







@dataclass
class 			snake_case		:
      lowerCamelCase__						= 42
      lowerCamelCase__						= True
      lowerCamelCase__						= None
      lowerCamelCase__						= None
      lowerCamelCase__						= None
      lowerCamelCase__						= None

      def __call__(			self   :Tuple						,							_lowerCamelCase   :List[Dict[str, Union[List[int], torch.Tensor]]]						):
       # split inputs and labels since they have to be of different lenghts and need
       # different padding methods
       __SCREAMING_SNAKE_CASE :			int												=       [{'''input_values''': feature['''input_values''']} for feature in features]
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       [{'''input_ids''': feature['''labels''']} for feature in features]

       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.processor.pad(
           _lowerCamelCase						,							padding=self.padding						,							max_length=self.max_length						,							pad_to_multiple_of=self.pad_to_multiple_of						,							return_tensors='''pt'''						,							)
       __SCREAMING_SNAKE_CASE :			int												=       self.processor.pad(
           labels=_lowerCamelCase						,							padding=self.padding						,							max_length=self.max_length_labels						,							pad_to_multiple_of=self.pad_to_multiple_of_labels						,							return_tensors='''pt'''						,							)

       # replace padding with -100 to ignore loss correctly
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1						)						,							-1_0_0						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       labels

       return batch







class 			snake_case		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :nn.Module						,							_lowerCamelCase   :Dict[str, Union[torch.Tensor, Any]]						):
       model.train()
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self._prepare_inputs(_lowerCamelCase						)

       if self.use_amp:
        with autocast():
         __SCREAMING_SNAKE_CASE :			Dict												=       self.compute_loss(_lowerCamelCase						,							_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.compute_loss(_lowerCamelCase						,							_lowerCamelCase						)

       if self.args.n_gpu > 1:
        if model.module.config.ctc_loss_reduction == "mean":
         __SCREAMING_SNAKE_CASE :			str												=       loss.mean()
        elif model.module.config.ctc_loss_reduction == "sum":
         __SCREAMING_SNAKE_CASE :			List[Any]												=       loss.sum() / (inputs['''labels'''] >= 0).sum()
        else:
         raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''						)

       if self.args.gradient_accumulation_steps > 1:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       loss / self.args.gradient_accumulation_steps

       if self.use_amp:
        self.scaler.scale(_lowerCamelCase						).backward()
       elif self.use_apex:
        with amp.scale_loss(_lowerCamelCase						,							self.optimizer						) as scaled_loss:
         scaled_loss.backward()
       elif self.deepspeed:
        self.deepspeed.backward(_lowerCamelCase						)
       else:
        loss.backward()

       return loss.detach()







def 		lowerCAmelCase_	(  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Dict												=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)

 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       parser.parse_args_into_dataclasses()
 configure_logger(lowercase_ ,      lowercase_		)

 __SCREAMING_SNAKE_CASE :			Tuple												=       Orthography.from_name(data_args.orthography.lower()		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       orthography.create_processor(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       WavaVecaForCTC.from_pretrained(
     model_args.model_name_or_path ,      cache_dir=model_args.cache_dir ,      gradient_checkpointing=training_args.gradient_checkpointing ,      vocab_size=len(processor.tokenizer		) ,      )

 __SCREAMING_SNAKE_CASE :			Dict												=       datasets.load_dataset(
     data_args.dataset_name ,      data_args.dataset_config_name ,      split=data_args.train_split_name		)
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       datasets.load_dataset(
     data_args.dataset_name ,      data_args.dataset_config_name ,      split=data_args.validation_split_name		)

 __SCREAMING_SNAKE_CASE :			Tuple												=       datasets.load_metric('''wer'''		)
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       ''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(lowercase_		) == 1		)
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       re.compile(  # remove characters not in vocabulary
     rF'''[^\s{re.escape(lowercase_		)}]''' ,      flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0 ,      )
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       []

 def prepare_example(lowercase_   :							List[str]		):  # TODO(elgeish) make use of multiprocessing?
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       librosa.load(example[data_args.speech_file_column] ,      sr=lowercase_		)
  if data_args.max_duration_in_seconds is not None:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       len(example['''speech''']		) / example['''sampling_rate''']
  # Normalize and clean up text; order matters!
  __SCREAMING_SNAKE_CASE :			List[Any]												=       orthography.preprocess_for_training(example[data_args.target_text_column]		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       vocabulary_text_cleaner.sub('''''' ,      lowercase_		)
  if updated_text != example[data_args.target_text_column]:
   text_updates.append((example[data_args.target_text_column], updated_text)		)
   __SCREAMING_SNAKE_CASE :			Any												=       updated_text
  return example

 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       train_dataset.map(lowercase_ ,      remove_columns=[data_args.speech_file_column]		)
 __SCREAMING_SNAKE_CASE :			Tuple												=       val_dataset.map(lowercase_ ,      remove_columns=[data_args.speech_file_column]		)

 if data_args.max_duration_in_seconds is not None:

  def filter_by_max_duration(lowercase_   :							Optional[Any]		):
   return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

  __SCREAMING_SNAKE_CASE :			List[Any]												=       len(lowercase_		)
  __SCREAMING_SNAKE_CASE :			str												=       len(lowercase_		)
  __SCREAMING_SNAKE_CASE :			Any												=       train_dataset.filter(lowercase_ ,      remove_columns=['''duration_in_seconds''']		)
  __SCREAMING_SNAKE_CASE :			List[str]												=       val_dataset.filter(lowercase_ ,      remove_columns=['''duration_in_seconds''']		)
  if len(lowercase_		) > old_train_size:
   logger.warning(
       F'''Filtered out {len(lowercase_		) - old_train_size} train example(s) longer than'''
       F''' {data_args.max_duration_in_seconds} second(s).'''		)
  if len(lowercase_		) > old_val_size:
   logger.warning(
       F'''Filtered out {len(lowercase_		) - old_val_size} validation example(s) longer than'''
       F''' {data_args.max_duration_in_seconds} second(s).'''		)
 logger.info(F'''Split sizes: {len(lowercase_		)} train and {len(lowercase_		)} validation.'''		)

 logger.warning(F'''Updated {len(lowercase_		)} transcript(s) using \'{data_args.orthography}\' orthography rules.'''		)
 if logger.isEnabledFor(logging.DEBUG		):
  for original_text, updated_text in text_updates:
   logger.debug(F'''Updated text: "{original_text}" -> "{updated_text}"'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       None

 def prepare_dataset(lowercase_   :							Tuple		):
  # check that all files have the correct sampling rate
  assert (
      len(set(batch['''sampling_rate''']		)		) == 1
  ), F'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

  __SCREAMING_SNAKE_CASE :			List[Any]												=       processor(
      audio=batch['''speech'''] ,      text=batch[data_args.target_text_column] ,      sampling_rate=batch['''sampling_rate'''][0]		)
  batch.update(lowercase_		)
  return batch

 __SCREAMING_SNAKE_CASE :			Any												=       train_dataset.map(
     lowercase_ ,      batch_size=training_args.per_device_train_batch_size ,      batched=lowercase_ ,      num_proc=data_args.preprocessing_num_workers ,      )
 __SCREAMING_SNAKE_CASE :			Dict												=       val_dataset.map(
     lowercase_ ,      batch_size=training_args.per_device_train_batch_size ,      batched=lowercase_ ,      num_proc=data_args.preprocessing_num_workers ,      )

 __SCREAMING_SNAKE_CASE :			Optional[int]												=       DataCollatorCTCWithPadding(processor=lowercase_ ,      padding=lowercase_		)

 def compute_metrics(lowercase_   :							int		):
  __SCREAMING_SNAKE_CASE :			List[Any]												=       pred.predictions
  __SCREAMING_SNAKE_CASE :			Any												=       np.argmax(lowercase_ ,      axis=-1		)

  __SCREAMING_SNAKE_CASE :			Tuple												=       processor.tokenizer.pad_token_id

  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       processor.batch_decode(lowercase_		)
  # we do not want to group tokens when computing the metrics
  __SCREAMING_SNAKE_CASE :			Dict												=       processor.batch_decode(pred.label_ids ,      group_tokens=lowercase_		)
  if logger.isEnabledFor(logging.DEBUG		):
   for reference, predicted in zip(lowercase_ ,      lowercase_		):
    logger.debug(F'''reference: "{reference}"'''		)
    logger.debug(F'''predicted: "{predicted}"'''		)
    if orthography.untransliterator is not None:
     logger.debug(F'''reference (untransliterated): "{orthography.untransliterator(lowercase_		)}"'''		)
     logger.debug(F'''predicted (untransliterated): "{orthography.untransliterator(lowercase_		)}"'''		)

  __SCREAMING_SNAKE_CASE :			str												=       wer_metric.compute(predictions=lowercase_ ,      references=lowercase_		)

  return {"wer": wer}

 if model_args.freeze_feature_extractor:
  model.freeze_feature_extractor()

 __SCREAMING_SNAKE_CASE :			str												=       CTCTrainer(
     model=lowercase_ ,      data_collator=lowercase_ ,      args=lowercase_ ,      compute_metrics=lowercase_ ,      train_dataset=lowercase_ ,      eval_dataset=lowercase_ ,      tokenizer=processor.feature_extractor ,      )

 trainer.train()


if __name__ == "__main__":
    main()

