




'''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'''):
     UpperCAmelCase_       :  int      =		True
     from torch.cuda.amp import autocast


UpperCAmelCase_       :  List[str]      =		logging.getLogger(__name__)





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







def 			_UpperCamelCase (_lowerCamelCase							:     ModelArguments   ,	_lowerCamelCase							:     TrainingArguments	)->   Optional[Any]:




     '''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	)]   ,	)
     __snake_case    					=  logging.WARNING
     if model_args.verbose_logging:
          __snake_case    					=  logging.DEBUG
     elif trainer_utils.is_main_process(training_args.local_rank	):
          __snake_case    					=  logging.INFO
     logger.setLevel(_lowerCamelCase	)





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





@dataclass
class     lowerCAmelCase   :
       __lowercase  :      bool        =			False
       __lowercase  :      Optional[str]        =			None
       __lowercase  :      Optional[str]        =			"|"
       __lowercase  :      Optional[Dict[str, str]]        =			field(default_factory=__lowerCAmelCase)
       __lowercase  :      Optional[Set[str]]        =			field(default_factory=__lowerCAmelCase)
       __lowercase  :      Optional[Callable[[str], str]]        =			None


       @classmethod
       def       lowerCAmelCase      (   cls    ,  __SCREAMING_SNAKE_CASE  )      ->  List[str]:



            '''simple docstring'''




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


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE  )      ->  str:



            '''simple docstring'''




            if len(self.translation_table  ) > 0:
                 __snake_case    					=  text.translate(self.translation_table  )
            if len(self.words_to_remove  ) == 0:
                 __snake_case    					=  ''' '''.join(text.split()  )  # clean up whitespaces
            else:
                 __snake_case    					=  ''' '''.join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
            return text




       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE  )      ->  WavaVecaProcessor:



            '''simple docstring'''




            __snake_case    					=  WavaVecaFeatureExtractor.from_pretrained(
                model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir  )
            if self.vocab_file:
                 __snake_case    					=  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:
                 __snake_case    					=  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(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )





@dataclass
class     lowerCAmelCase   :
       __lowercase  :      WavaVecaProcessor
       __lowercase  :      Union[bool, str]        =			True
       __lowercase  :      Optional[int]        =			None
       __lowercase  :      Optional[int]        =			None
       __lowercase  :      Optional[int]        =			None
       __lowercase  :      Optional[int]        =			None


       def __call__(   self    ,  __SCREAMING_SNAKE_CASE  )      ->  Dict[str, torch.Tensor]:



            '''simple docstring'''




            __snake_case    					=  [{'''input_values''': feature['''input_values''']} for feature in features]
            __snake_case    					=  [{'''input_ids''': feature['''labels''']} for feature in features]

            __snake_case    					=  self.processor.pad(
                __SCREAMING_SNAKE_CASE    ,  padding=self.padding    ,  max_length=self.max_length    ,  pad_to_multiple_of=self.pad_to_multiple_of    ,  return_tensors='''pt'''    ,  )
            __snake_case    					=  self.processor.pad(
                labels=__SCREAMING_SNAKE_CASE    ,  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
            __snake_case    					=  labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1  )    ,  -100  )

            __snake_case    					=  labels

            return batch





class     lowerCAmelCase   ( __lowerCAmelCase):


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  torch.Tensor:



            '''simple docstring'''




            model.train()
            __snake_case    					=  self._prepare_inputs(__SCREAMING_SNAKE_CASE  )

            if self.use_amp:
                 with autocast():
                      __snake_case    					=  self.compute_loss(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            else:
                 __snake_case    					=  self.compute_loss(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )

            if self.args.n_gpu > 1:
                 if model.module.config.ctc_loss_reduction == "mean":
                      __snake_case    					=  loss.mean()
                 elif model.module.config.ctc_loss_reduction == "sum":
                      __snake_case    					=  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:
                 __snake_case    					=  loss / self.args.gradient_accumulation_steps

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

            return loss.detach()







def 			_UpperCamelCase ()->   str:




     '''simple docstring'''





     __snake_case    					=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)

     __snake_case					,    __snake_case					,    __snake_case    					=  parser.parse_args_into_dataclasses()
     configure_logger(_lowerCamelCase   ,	_lowerCamelCase	)

     __snake_case    					=  Orthography.from_name(data_args.orthography.lower()	)
     __snake_case    					=  orthography.create_processor(_lowerCamelCase	)
     __snake_case    					=  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	)   ,	)

     __snake_case    					=  datasets.load_dataset(
         data_args.dataset_name   ,	data_args.dataset_config_name   ,	split=data_args.train_split_name	)
     __snake_case    					=  datasets.load_dataset(
         data_args.dataset_name   ,	data_args.dataset_config_name   ,	split=data_args.validation_split_name	)

     __snake_case    					=  datasets.load_metric('''wer'''	)
     __snake_case    					=  processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
     __snake_case    					=  ''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(_lowerCamelCase	) == 1	)
     __snake_case    					=  re.compile(  # remove characters not in vocabulary
         Rf'''[^\s{re.escape(_lowerCamelCase	)}]'''   ,	flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0   ,	)
     __snake_case    					=  []

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

     __snake_case    					=  train_dataset.map(_lowerCamelCase   ,	remove_columns=[data_args.speech_file_column]	)
     __snake_case    					=  val_dataset.map(_lowerCamelCase   ,	remove_columns=[data_args.speech_file_column]	)

     if data_args.max_duration_in_seconds is not None:

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

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

     logger.warning(f'''Updated {len(_lowerCamelCase	)} 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}"'''	)
     __snake_case    					=  None

     def prepare_dataset(_lowerCamelCase							:     List[Any]	):
          # 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}.'''

          __snake_case    					=  processor(
              audio=batch['''speech''']   ,	text=batch[data_args.target_text_column]   ,	sampling_rate=batch['''sampling_rate'''][0]	)
          batch.update(_lowerCamelCase	)
          return batch

     __snake_case    					=  train_dataset.map(
         _lowerCamelCase   ,	batch_size=training_args.per_device_train_batch_size   ,	batched=_lowerCamelCase   ,	num_proc=data_args.preprocessing_num_workers   ,	)
     __snake_case    					=  val_dataset.map(
         _lowerCamelCase   ,	batch_size=training_args.per_device_train_batch_size   ,	batched=_lowerCamelCase   ,	num_proc=data_args.preprocessing_num_workers   ,	)

     __snake_case    					=  DataCollatorCTCWithPadding(processor=_lowerCamelCase   ,	padding=_lowerCamelCase	)

     def compute_metrics(_lowerCamelCase							:     Union[str, Any]	):
          __snake_case    					=  pred.predictions
          __snake_case    					=  np.argmax(_lowerCamelCase   ,	axis=-1	)

          __snake_case    					=  processor.tokenizer.pad_token_id

          __snake_case    					=  processor.batch_decode(_lowerCamelCase	)
          # we do not want to group tokens when computing the metrics
          __snake_case    					=  processor.batch_decode(pred.label_ids   ,	group_tokens=_lowerCamelCase	)
          if logger.isEnabledFor(logging.DEBUG	):
               for reference, predicted in zip(_lowerCamelCase   ,	_lowerCamelCase	):
                    logger.debug(f'''reference: "{reference}"'''	)
                    logger.debug(f'''predicted: "{predicted}"'''	)
                    if orthography.untransliterator is not None:
                         logger.debug(f'''reference (untransliterated): "{orthography.untransliterator(_lowerCamelCase	)}"'''	)
                         logger.debug(f'''predicted (untransliterated): "{orthography.untransliterator(_lowerCamelCase	)}"'''	)

          __snake_case    					=  wer_metric.compute(predictions=_lowerCamelCase   ,	references=_lowerCamelCase	)

          return {"wer": wer}

     if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()

     __snake_case    					=  CTCTrainer(
         model=_lowerCamelCase   ,	data_collator=_lowerCamelCase   ,	args=_lowerCamelCase   ,	compute_metrics=_lowerCamelCase   ,	train_dataset=_lowerCamelCase   ,	eval_dataset=_lowerCamelCase   ,	tokenizer=processor.feature_extractor   ,	)

     trainer.train()


if __name__ == "__main__":
     main()












'''simple docstring'''



from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    '''cross_attention''',
    '''0.20.0''',
    '''Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.''',
    standard_warn=False,
)


UpperCAmelCase_       :  Optional[int]      =		AttentionProcessor





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Any:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[Any]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  List[str]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )






