






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__lowerCamelCase      :			str							       =		"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase      :			str							       =		direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__lowerCamelCase      :			int							       =		re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowerCamelCase      :			str							       =		re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCamelCase      :			Any							       =		re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCamelCase      :			Optional[int]							       =		[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""			,					__UpperCamelCase     )
  return [m.group(0     ) for m in matches]



def      __SCREAMING_SNAKE_CASE  ( )							-> Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  SCREAMING_SNAKE_CASE__      							=				{
      config.replace("""Config"""			,					""""""     ): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  SCREAMING_SNAKE_CASE__      							=				collections.defaultdict(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				collections.defaultdict(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				collections.defaultdict(__UpperCamelCase     )

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				None
    if _re_tf_models.match(__UpperCamelCase     ) is not None:
      SCREAMING_SNAKE_CASE__      							=				tf_models
      SCREAMING_SNAKE_CASE__      							=				_re_tf_models.match(__UpperCamelCase     ).groups()[0]
    elif _re_flax_models.match(__UpperCamelCase     ) is not None:
      SCREAMING_SNAKE_CASE__      							=				flax_models
      SCREAMING_SNAKE_CASE__      							=				_re_flax_models.match(__UpperCamelCase     ).groups()[0]
    elif _re_pt_models.match(__UpperCamelCase     ) is not None:
      SCREAMING_SNAKE_CASE__      							=				pt_models
      SCREAMING_SNAKE_CASE__      							=				_re_pt_models.match(__UpperCamelCase     ).groups()[0]

    if lookup_dict is not None:
      while len(__UpperCamelCase     ) > 0:
        if attr_name in model_prefix_to_model_type:
          SCREAMING_SNAKE_CASE__      							=				True
          break
        # Try again after removing the last word in the name
        SCREAMING_SNAKE_CASE__      							=				"""""".join(camel_case_split(__UpperCamelCase     )[:-1]     )

  SCREAMING_SNAKE_CASE__      							=				set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
  SCREAMING_SNAKE_CASE__      							=				list(__UpperCamelCase     )
  all_models.sort()

  SCREAMING_SNAKE_CASE__      							=				{"""model_type""": all_models}
  SCREAMING_SNAKE_CASE__      							=				[pt_models[t] for t in all_models]
  SCREAMING_SNAKE_CASE__      							=				[tf_models[t] for t in all_models]
  SCREAMING_SNAKE_CASE__      							=				[flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  SCREAMING_SNAKE_CASE__      							=				{}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      SCREAMING_SNAKE_CASE__      							=				"""AutoProcessor"""
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      SCREAMING_SNAKE_CASE__      							=				"""AutoTokenizer"""
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      SCREAMING_SNAKE_CASE__      							=				"""AutoFeatureExtractor"""
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      SCREAMING_SNAKE_CASE__      							=				"""AutoTokenizer"""

  SCREAMING_SNAKE_CASE__      							=				[processors[t] for t in all_models]

  return pd.DataFrame(__UpperCamelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str     )							-> List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    SCREAMING_SNAKE_CASE__      							=				[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
    SCREAMING_SNAKE_CASE__      							=				[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
    # Loop through all three frameworks
    for module, cls, mapping in zip(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     ):
      # The type of pipeline may not exist in this framework
      if not hasattr(__UpperCamelCase			,					__UpperCamelCase     ):
        continue
      # First extract all model_names
      SCREAMING_SNAKE_CASE__      							=				[]
      for name in getattr(__UpperCamelCase			,					__UpperCamelCase     ).values():
        if isinstance(__UpperCamelCase			,					__UpperCamelCase     ):
          model_names.append(__UpperCamelCase     )
        else:
          model_names.extend(list(__UpperCamelCase     )     )

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}     )

  return table



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	Union[str, Any]     )							-> Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				get_frameworks_table()
  SCREAMING_SNAKE_CASE__      							=				Dataset.from_pandas(__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				hf_hub_download(
      """huggingface/transformers-metadata"""			,					"""pipeline_tags.json"""			,					repo_type="""dataset"""			,					token=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				Dataset.from_json(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				{
      tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
      for i in range(len(__UpperCamelCase     )     )
  }
  SCREAMING_SNAKE_CASE__      							=				update_pipeline_and_auto_class_table(__UpperCamelCase     )

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  SCREAMING_SNAKE_CASE__      							=				sorted(table.keys()     )
  SCREAMING_SNAKE_CASE__      							=				pd.DataFrame(
      {
          """model_class""": model_classes,
          """pipeline_tag""": [table[m][0] for m in model_classes],
          """auto_class""": [table[m][1] for m in model_classes],
      }     )
  SCREAMING_SNAKE_CASE__      							=				Dataset.from_pandas(__UpperCamelCase     )

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(__UpperCamelCase			,					"""frameworks.json"""     )     )
    tags_dataset.to_json(os.path.join(__UpperCamelCase			,					"""pipeline_tags.json"""     )     )

    if commit_sha is not None:
      SCREAMING_SNAKE_CASE__      							=				(
          f"""Update with commit {commit_sha}\n\nSee: """
          f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
      )
    else:
      SCREAMING_SNAKE_CASE__      							=				"""Update"""

    upload_folder(
        repo_id="""huggingface/transformers-metadata"""			,					folder_path=__UpperCamelCase			,					repo_type="""dataset"""			,					token=__UpperCamelCase			,					commit_message=__UpperCamelCase			,					)



def      __SCREAMING_SNAKE_CASE  ( )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  SCREAMING_SNAKE_CASE__      							=				transformers_module.pipelines.SUPPORTED_TASKS
  SCREAMING_SNAKE_CASE__      							=				[]
  for key in pipeline_tasks:
    if key not in in_table:
      SCREAMING_SNAKE_CASE__      							=				pipeline_tasks[key]["""pt"""]
      if isinstance(__UpperCamelCase			,					(list, tuple)     ):
        SCREAMING_SNAKE_CASE__      							=				model[0]
      SCREAMING_SNAKE_CASE__      							=				model.__name__
      if model not in in_table.values():
        missing.append(__UpperCamelCase     )

  if len(__UpperCamelCase     ) > 0:
    SCREAMING_SNAKE_CASE__      							=				""", """.join(__UpperCamelCase     )
    raise ValueError(
        """The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
        f"""`utils/update_metadata.py`: {msg}. Please add them!"""     )


if __name__ == "__main__":
   __lowerCamelCase      :			Dict							       =		argparse.ArgumentParser()
   parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
   parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
   parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
   __lowerCamelCase      :			Optional[int]							       =		parser.parse_args()

   if args.check_only:
      check_pipeline_tags()
   else:
      update_metadata(args.token, args.commit_sha)

from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__lowerCamelCase      :			Any							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["input_features", "attention_mask"]




   def __init__(   self      :    int	,     _lowercase      :    Dict=80	,     _lowercase      :    Union[str, Any]=1_60_00	,     _lowercase      :    str=0.0	,     _lowercase      :    int=10	,     _lowercase      :    str=25	,     _lowercase      :    Union[str, Any]="hamming_window"	,     _lowercase      :    Dict=3_27_68.0	,     _lowercase      :    Optional[int]=0.97	,     _lowercase      :    Optional[int]=1.0	,     _lowercase      :    int=True	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    Optional[int]=False	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     super().__init__(feature_size=_lowercase	,     sampling_rate=_lowercase	,     padding_value=_lowercase	,     **_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				feature_size
     SCREAMING_SNAKE_CASE__      							=				sampling_rate
     SCREAMING_SNAKE_CASE__      							=				padding_value
     SCREAMING_SNAKE_CASE__      							=				hop_length
     SCREAMING_SNAKE_CASE__      							=				win_length
     SCREAMING_SNAKE_CASE__      							=				frame_signal_scale
     SCREAMING_SNAKE_CASE__      							=				preemphasis_coeff
     SCREAMING_SNAKE_CASE__      							=				mel_floor
     SCREAMING_SNAKE_CASE__      							=				normalize_means
     SCREAMING_SNAKE_CASE__      							=				normalize_vars
     SCREAMING_SNAKE_CASE__      							=				win_function
     SCREAMING_SNAKE_CASE__      							=				return_attention_mask

     SCREAMING_SNAKE_CASE__      							=				win_length * sampling_rate // 10_00
     SCREAMING_SNAKE_CASE__      							=				hop_length * sampling_rate // 10_00

     SCREAMING_SNAKE_CASE__      							=				optimal_fft_length(self.sample_size				)
     SCREAMING_SNAKE_CASE__      							=				(self.n_fft // 2) + 1




   def      __a						(   self      :    str	,     _lowercase      :    np.array				):



     """simple docstring"""






     if self.win_function == "hamming_window":
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function	,     periodic=_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function				)

     SCREAMING_SNAKE_CASE__      							=				mel_filter_bank(
         num_frequency_bins=self.n_freqs	,     num_mel_filters=self.feature_size	,     min_frequency=0.0	,     max_frequency=self.sampling_rate / 2.0	,     sampling_rate=self.sampling_rate	,     )

     SCREAMING_SNAKE_CASE__      							=				spectrogram(
         one_waveform * self.frame_signal_scale	,     window=_lowercase	,     frame_length=self.sample_size	,     hop_length=self.sample_stride	,     fft_length=self.n_fft	,     center=_lowercase	,     preemphasis=self.preemphasis_coeff	,     mel_filters=_lowercase	,     mel_floor=self.mel_floor	,     log_mel="""log"""	,     )
     return msfc_features.T




   def      __a						(   self      :    Tuple	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    int				):



     """simple docstring"""






     if self.normalize_means:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].mean(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.subtract(_lowercase	,     _lowercase				)
     if self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].std(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.divide(_lowercase	,     _lowercase				)

     if input_length < x.shape[0]:
       SCREAMING_SNAKE_CASE__      							=				padding_value

     # make sure array is in float32
     SCREAMING_SNAKE_CASE__      							=				x.astype(np.floataa				)

     return x




   def      __a						(   self      :    int	,     _lowercase      :    List[np.ndarray]	,     _lowercase      :    Optional[np.ndarray] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [self._normalize_one(_lowercase	,     _lowercase	,     self.padding_value				) for x, n in zip(_lowercase	,     _lowercase				)]




   def __call__(   self      :    Dict	,     _lowercase      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,     _lowercase      :    Union[bool, str, PaddingStrategy] = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    Optional[bool] = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    Optional[int] = None	,     **_lowercase      :    List[str]	,     ):



     """simple docstring"""






     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
             f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
             f""" {self.sampling_rate} and not {sampling_rate}."""				)
     else:
       logger.warning(
           """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
           """Failing to do so can result in silent errors that might be hard to debug."""				)

     SCREAMING_SNAKE_CASE__      							=				isinstance(_lowercase	,     np.ndarray				) and len(raw_speech.shape				) > 1
     if is_batched_numpy and len(raw_speech.shape				) > 2:
       raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""				)
     SCREAMING_SNAKE_CASE__      							=				is_batched_numpy or (
         isinstance(_lowercase	,     (list, tuple)				) and (isinstance(raw_speech[0]	,     (np.ndarray, tuple, list)				))
     )

     if is_batched:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for speech in raw_speech]
     elif not is_batched and not isinstance(_lowercase	,     np.ndarray				):
       SCREAMING_SNAKE_CASE__      							=				np.asarray(_lowercase	,     dtype=np.floataa				)
     elif isinstance(_lowercase	,     np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
       SCREAMING_SNAKE_CASE__      							=				raw_speech.astype(np.floataa				)

     # always return batch
     if not is_batched:
       SCREAMING_SNAKE_CASE__      							=				[raw_speech]

     # extract fbank features
     SCREAMING_SNAKE_CASE__      							=				[self._extract_mfsc_features(_lowercase				) for one_waveform in raw_speech]

     # convert into correct format for padding
     SCREAMING_SNAKE_CASE__      							=				BatchFeature({"""input_features""": features}				)

     SCREAMING_SNAKE_CASE__      							=				self.pad(
         _lowercase	,     padding=_lowercase	,     max_length=_lowercase	,     truncation=_lowercase	,     pad_to_multiple_of=_lowercase	,     return_attention_mask=_lowercase	,     **_lowercase	,     )
     # make sure list is in array format
     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""input_features"""				)
     if isinstance(input_features[0]	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for feature in input_features]

     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""attention_mask"""				)
     if attention_mask is not None:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.intaa				) for array in attention_mask]

     if self.normalize_means or self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				(
           np.array(_lowercase	,     dtype=np.intaa				)
           if self._get_padding_strategies(_lowercase	,     max_length=_lowercase				) is not PaddingStrategy.DO_NOT_PAD
           and padding
           else None
       )
       SCREAMING_SNAKE_CASE__      							=				self.normalize(
           padded_inputs["""input_features"""]	,     attention_mask=_lowercase				)

     if return_tensors is not None:
       SCREAMING_SNAKE_CASE__      							=				padded_inputs.convert_to_tensors(_lowercase				)

     return padded_inputs
