


"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):





 for attribute in key.split('.'						):
  __lowerCamelCase		:							List[str]		 =						getattr(__snake_case      ,	__snake_case						)

 if weight_type is not None:
  __lowerCamelCase		:							List[str]		 =						getattr(__snake_case      ,	__snake_case						).shape
 else:
  __lowerCamelCase		:							int		 =						hf_pointer.shape

 assert hf_shape == value.shape, (
     f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
     f' {value.shape} for {full_name}'
 )

 if weight_type == "weight":
  __lowerCamelCase		:							Tuple		 =						value
 elif weight_type == "weight_g":
  __lowerCamelCase		:							Tuple		 =						value
 elif weight_type == "weight_v":
  __lowerCamelCase		:							List[Any]		 =						value
 elif weight_type == "bias":
  __lowerCamelCase		:							Tuple		 =						value
 else:
  __lowerCamelCase		:							Any		 =						value

 logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						[]
 __lowerCamelCase		:							str		 =						fairseq_model.state_dict()

 __lowerCamelCase		:							Optional[int]		 =						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCamelCase		:							str		 =						False
  if "conv_layers" in name:
   load_conv_layer(
       __snake_case      ,	__snake_case      ,	__snake_case      ,	__snake_case      ,	hf_model.config.feat_extract_norm == 'group'      ,	)
   __lowerCamelCase		:							Tuple		 =						True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCamelCase		:							int		 =						'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

    if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
     __lowerCamelCase		:							List[Any]		 =						True
     if "*" in mapped_key:
      __lowerCamelCase		:							List[str]		 =						name.split(__snake_case						)[0].split('.'						)[-2]
      __lowerCamelCase		:							str		 =						mapped_key.replace('*'      ,	__snake_case						)
     if "weight_g" in name:
      __lowerCamelCase		:							Optional[int]		 =						'weight_g'
     elif "weight_v" in name:
      __lowerCamelCase		:							str		 =						'weight_v'
     elif "weight" in name:
      __lowerCamelCase		:							List[Any]		 =						'weight'
     elif "bias" in name:
      __lowerCamelCase		:							str		 =						'bias'
     else:
      __lowerCamelCase		:							Tuple		 =						None
     set_recursively(__snake_case      ,	__snake_case      ,	__snake_case      ,	__snake_case      ,	__snake_case						)
    continue
  if not is_used:
   unused_weights.append(__snake_case						)

 logger.warning(f'Unused weights: {unused_weights}'						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						full_name.split('conv_layers.'						)[-1]
 __lowerCamelCase		:							List[Any]		 =						name.split('.'						)
 __lowerCamelCase		:							int		 =						int(items[0]						)
 __lowerCamelCase		:							Union[str, Any]		 =						int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       f'{full_name} has size {value.shape}, but'
       f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
   )
   __lowerCamelCase		:							str		 =						value
   logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       f'{full_name} has size {value.shape}, but'
       f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
   )
   __lowerCamelCase		:							Union[str, Any]		 =						value
   logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'						)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
       " found."
   )
   __lowerCamelCase		:							Optional[int]		 =						value
   logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       f'{full_name} has size {value.shape}, but'
       f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
   )
   __lowerCamelCase		:							Tuple		 =						value
   logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
 else:
  unused_weights.append(__snake_case						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[str]		 =						SEWConfig()
 if is_finetuned:
  __lowerCamelCase		:							Any		 =						model.wav_encoder.wav_model.cfg
 else:
  __lowerCamelCase		:							str		 =						model.cfg

 __lowerCamelCase		:							Union[str, Any]		 =						fs_config.conv_bias
 __lowerCamelCase		:							List[Any]		 =						eval(fs_config.conv_feature_layers						)
 __lowerCamelCase		:							List[Any]		 =						[x[0] for x in conv_layers]
 __lowerCamelCase		:							Tuple		 =						[x[1] for x in conv_layers]
 __lowerCamelCase		:							Optional[Any]		 =						[x[2] for x in conv_layers]
 __lowerCamelCase		:							List[Any]		 =						'gelu'
 __lowerCamelCase		:							Optional[int]		 =						'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 __lowerCamelCase		:							Tuple		 =						0.0
 __lowerCamelCase		:							str		 =						fs_config.activation_fn.name
 __lowerCamelCase		:							List[Any]		 =						fs_config.encoder_embed_dim
 __lowerCamelCase		:							Any		 =						0.02
 __lowerCamelCase		:							Any		 =						fs_config.encoder_ffn_embed_dim
 __lowerCamelCase		:							Optional[Any]		 =						1e-5
 __lowerCamelCase		:							List[str]		 =						fs_config.encoder_layerdrop
 __lowerCamelCase		:							Any		 =						fs_config.encoder_attention_heads
 __lowerCamelCase		:							int		 =						fs_config.conv_pos_groups
 __lowerCamelCase		:							Any		 =						fs_config.conv_pos
 __lowerCamelCase		:							str		 =						len(__snake_case						)
 __lowerCamelCase		:							Optional[int]		 =						fs_config.encoder_layers
 __lowerCamelCase		:							Tuple		 =						fs_config.squeeze_factor

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  __lowerCamelCase		:							Any		 =						model.cfg
  __lowerCamelCase		:							Optional[Any]		 =						fs_config.final_dropout
  __lowerCamelCase		:							Dict		 =						fs_config.layerdrop
 __lowerCamelCase		:							str		 =						fs_config.activation_dropout
 __lowerCamelCase		:							str		 =						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 __lowerCamelCase		:							str		 =						fs_config.attention_dropout
 __lowerCamelCase		:							Dict		 =						fs_config.dropout_input
 __lowerCamelCase		:							List[Any]		 =						fs_config.dropout
 __lowerCamelCase		:							List[Any]		 =						fs_config.mask_channel_length
 __lowerCamelCase		:							List[str]		 =						fs_config.mask_channel_prob
 __lowerCamelCase		:							List[Any]		 =						fs_config.mask_length
 __lowerCamelCase		:							Tuple		 =						fs_config.mask_prob

 __lowerCamelCase		:							Optional[Any]		 =						'Wav2Vec2FeatureExtractor'
 __lowerCamelCase		:							Union[str, Any]		 =						'Wav2Vec2CTCTokenizer'

 return config
@torch.no_grad()
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=True						):





 if is_finetuned:
  __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]      ,	arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						)}						)
 else:
  __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

 if config_path is not None:
  __lowerCamelCase		:							Any		 =						SEWConfig.from_pretrained(__snake_case						)
 else:
  __lowerCamelCase		:							List[Any]		 =						convert_config(model[0]      ,	__snake_case						)
 __lowerCamelCase		:							List[Any]		 =						model[0].eval()

 __lowerCamelCase		:							List[Any]		 =						True if config.feat_extract_norm == 'layer' else False
 __lowerCamelCase		:							Optional[int]		 =						WavaVecaFeatureExtractor(
     feature_size=1      ,	sampling_rate=16_000      ,	padding_value=0      ,	do_normalize=__snake_case      ,	return_attention_mask=__snake_case      ,	)

 if is_finetuned:
  if dict_path:
   __lowerCamelCase		:							Tuple		 =						Dictionary.load(__snake_case						)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   __lowerCamelCase		:							List[str]		 =						target_dict.pad_index
   __lowerCamelCase		:							Any		 =						target_dict.bos_index
   __lowerCamelCase		:							List[Any]		 =						target_dict.pad_index
   __lowerCamelCase		:							Dict		 =						target_dict.bos_index
   __lowerCamelCase		:							Tuple		 =						target_dict.eos_index
   __lowerCamelCase		:							Dict		 =						len(target_dict.symbols						)
   __lowerCamelCase		:							List[Any]		 =						os.path.join(__snake_case      ,	'vocab.json'						)
   if not os.path.isdir(__snake_case						):
    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__snake_case						)						)
    return
   os.makedirs(__snake_case      ,	exist_ok=__snake_case						)
   with open(__snake_case      ,	'w'      ,	encoding='utf-8'						) as vocab_handle:
    json.dump(target_dict.indices      ,	__snake_case						)
   __lowerCamelCase		:							Union[str, Any]		 =						WavaVecaCTCTokenizer(
       __snake_case      ,	unk_token=target_dict.unk_word      ,	pad_token=target_dict.pad_word      ,	bos_token=target_dict.bos_word      ,	eos_token=target_dict.eos_word      ,	word_delimiter_token='|'      ,	do_lower_case=__snake_case      ,	)
   __lowerCamelCase		:							Dict		 =						WavaVecaProcessor(feature_extractor=__snake_case      ,	tokenizer=__snake_case						)
   processor.save_pretrained(__snake_case						)

  __lowerCamelCase		:							Dict		 =						SEWForCTC(__snake_case						)
 else:
  __lowerCamelCase		:							str		 =						SEWModel(__snake_case						)
  feature_extractor.save_pretrained(__snake_case						)

 recursively_load_weights(__snake_case      ,	__snake_case      ,	__snake_case						)

 hf_model.save_pretrained(__snake_case						)


if __name__ == "__main__":
       lowercase_	   =     argparse.ArgumentParser()
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
       parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
       )
       lowercase_	   =     parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )





import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit_text_model"""




      def __init__(    self:	Optional[int]						,				a:	Dict=4_9408						,				a:	Optional[Any]=512						,				a:	Dict=2048						,				a:	Optional[Any]=12						,				a:	Tuple=8						,				a:	Union[str, Any]=16						,				a:	str="quick_gelu"						,				a:	List[Any]=1e-5						,				a:	Dict=0.0						,				a:	Optional[int]=0.0_2						,				a:	Dict=1.0						,				a:	Any=0						,				a:	Union[str, Any]=4_9406						,				a:	Any=4_9407						,				**a:	Dict						,				):
       super().__init__(pad_token_id=a						,				bos_token_id=a						,				eos_token_id=a						,				**a    )

       __lowerCamelCase		:							List[Any]		 =						vocab_size
       __lowerCamelCase		:							int		 =						hidden_size
       __lowerCamelCase		:							Optional[Any]		 =						intermediate_size
       __lowerCamelCase		:							List[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							Any		 =						num_attention_heads
       __lowerCamelCase		:							Union[str, Any]		 =						max_position_embeddings
       __lowerCamelCase		:							List[Any]		 =						hidden_act
       __lowerCamelCase		:							List[str]		 =						layer_norm_eps
       __lowerCamelCase		:							Tuple		 =						attention_dropout
       __lowerCamelCase		:							Optional[int]		 =						initializer_range
       __lowerCamelCase		:							Tuple		 =						initializer_factor




      @classmethod
      def 					_snake_case     (    cls:	Dict						,				a:	Union[str, os.PathLike]						,				**a:	Optional[int]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						cls.get_config_dict(a						,				**a    )

       # get the text config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'    ) == "owlvit":
        __lowerCamelCase		:							Optional[Any]		 =						config_dict['text_config']

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit_vision_model"""




      def __init__(    self:	int						,				a:	Tuple=768						,				a:	int=3072						,				a:	List[str]=12						,				a:	Optional[Any]=12						,				a:	Optional[int]=3						,				a:	Optional[int]=768						,				a:	Optional[Any]=32						,				a:	Optional[int]="quick_gelu"						,				a:	Union[str, Any]=1e-5						,				a:	Union[str, Any]=0.0						,				a:	Union[str, Any]=0.0_2						,				a:	int=1.0						,				**a:	Union[str, Any]						,				):
       super().__init__(**a    )

       __lowerCamelCase		:							str		 =						hidden_size
       __lowerCamelCase		:							Tuple		 =						intermediate_size
       __lowerCamelCase		:							Dict		 =						num_hidden_layers
       __lowerCamelCase		:							Optional[Any]		 =						num_attention_heads
       __lowerCamelCase		:							int		 =						num_channels
       __lowerCamelCase		:							Optional[Any]		 =						image_size
       __lowerCamelCase		:							Tuple		 =						patch_size
       __lowerCamelCase		:							List[str]		 =						hidden_act
       __lowerCamelCase		:							Tuple		 =						layer_norm_eps
       __lowerCamelCase		:							List[Any]		 =						attention_dropout
       __lowerCamelCase		:							Tuple		 =						initializer_range
       __lowerCamelCase		:							List[Any]		 =						initializer_factor




      @classmethod
      def 					_snake_case     (    cls:	Optional[int]						,				a:	Union[str, os.PathLike]						,				**a:	int    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						cls.get_config_dict(a						,				**a    )

       # get the vision config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'    ) == "owlvit":
        __lowerCamelCase		:							Any		 =						config_dict['vision_config']

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit"""
      __snake_case 	=       True




      def __init__(    self:	Dict						,				a:	int=None						,				a:	str=None						,				a:	Tuple=512						,				a:	Tuple=2.6_5_9_2						,				a:	int=True						,				**a:	int						,				):
       super().__init__(**a    )

       if text_config is None:
        __lowerCamelCase		:							List[str]		 =						{}
        logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'    )

       if vision_config is None:
        __lowerCamelCase		:							str		 =						{}
        logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'    )

       __lowerCamelCase		:							List[Any]		 =						OwlViTTextConfig(**a    )
       __lowerCamelCase		:							str		 =						OwlViTVisionConfig(**a    )

       __lowerCamelCase		:							Union[str, Any]		 =						projection_dim
       __lowerCamelCase		:							Tuple		 =						logit_scale_init_value
       __lowerCamelCase		:							Dict		 =						return_dict
       __lowerCamelCase		:							Tuple		 =						1.0




      @classmethod
      def 					_snake_case     (    cls:	str						,				a:	Union[str, os.PathLike]						,				**a:	List[Any]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						cls.get_config_dict(a						,				**a    )

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )




      @classmethod
      def 					_snake_case     (    cls:	Tuple						,				a:	Dict						,				a:	Dict						,				**a:	str    ):
       __lowerCamelCase		:							List[str]		 =						{}
       __lowerCamelCase		:							List[str]		 =						text_config
       __lowerCamelCase		:							Optional[int]		 =						vision_config

       return cls.from_dict(a						,				**a    )




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							Optional[Any]		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							List[Any]		 =						self.text_config.to_dict()
       __lowerCamelCase		:							List[str]		 =						self.vision_config.to_dict()
       __lowerCamelCase		:							Optional[Any]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	str    ):
       return OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'sequence'}),
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
               ('attention_mask', {0: 'batch', 1: 'sequence'}),
           ]    )




      @property
      def 					_snake_case     (    self:	Dict    ):
       return OrderedDict(
           [
               ('logits_per_image', {0: 'batch'}),
               ('logits_per_text', {0: 'batch'}),
               ('text_embeds', {0: 'batch'}),
               ('image_embeds', {0: 'batch'}),
           ]    )




      @property
      def 					_snake_case     (    self:	int    ):
       return 1e-4




      def 					_snake_case     (    self:	Any						,				a:	"ProcessorMixin"						,				a:	int = -1						,				a:	int = -1						,				a:	Optional["TensorType"] = None						,				):
       __lowerCamelCase		:							List[str]		 =						super().generate_dummy_inputs(
           processor.tokenizer						,				batch_size=a						,				seq_length=a						,				framework=a    )
       __lowerCamelCase		:							int		 =						super().generate_dummy_inputs(
           processor.image_processor						,				batch_size=a						,				framework=a    )
       return {**text_input_dict, **image_input_dict}




      @property
      def 					_snake_case     (    self:	int    ):
       return 14

