




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						:   List[str]		=   "src/transformers"


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


# Regexes that match TF/Flax/PT model names.
_lowerCAmelCase						:   Any		=   re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowerCAmelCase						:   Any		=   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						:   Union[str, Any]		=   re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_lowerCAmelCase						:   Any		=   [
    ("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 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 UpperCAmelCase__							 =						{
     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.
 UpperCAmelCase__							 =						collections.defaultdict(_lowerCAmelCase	)
 UpperCAmelCase__							 =						collections.defaultdict(_lowerCAmelCase	)
 UpperCAmelCase__							 =						collections.defaultdict(_lowerCAmelCase	)

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

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

 UpperCAmelCase__							 =						set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
 UpperCAmelCase__							 =						list(_lowerCAmelCase	)
 all_models.sort()

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

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

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

 return pd.DataFrame(_lowerCAmelCase	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						[
     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:
  UpperCAmelCase__							 =						[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
  UpperCAmelCase__							 =						[auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
  # Loop through all three frameworks
  for module, cls, mapping in zip(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	):
   # The type of pipeline may not exist in this framework
   if not hasattr(_lowerCAmelCase						,							_lowerCAmelCase	):
    continue
   # First extract all model_names
   UpperCAmelCase__							 =						[]
   for name in getattr(_lowerCAmelCase						,							_lowerCAmelCase	).values():
    if isinstance(_lowerCAmelCase						,							_lowerCAmelCase	):
     model_names.append(_lowerCAmelCase	)
    else:
     model_names.extend(list(_lowerCAmelCase	)	)

            # 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 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_frameworks_table()
 UpperCAmelCase__							 =						Dataset.from_pandas(_lowerCAmelCase	)

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

 # Sort the model classes to avoid some nondeterministic updates to create false update commits.
 UpperCAmelCase__							 =						sorted(table.keys()	)
 UpperCAmelCase__							 =						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],
     }	)
 UpperCAmelCase__							 =						Dataset.from_pandas(_lowerCAmelCase	)

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

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

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



def 						lowerCAmelCase		(		):




 """simple docstring"""







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

 if len(_lowerCAmelCase	) > 0:
  UpperCAmelCase__							 =						", ".join(_lowerCAmelCase	)
  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 ..utils import DummyObject, requires_backends
class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :List[Any] ,      *lowerCamelCase   :int ,      **lowerCamelCase   :List[Any]      )     ->			Dict:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Union[str, Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :List[str]      )     ->			int:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[int] ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :List[Any]      )     ->			Dict:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Dict ,      *lowerCamelCase   :int ,      **lowerCamelCase   :str      )     ->			str:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[int] ,      *lowerCamelCase   :Tuple ,      **lowerCamelCase   :List[Any]      )     ->			Optional[int]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :Optional[int]      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :List[str] ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Any      )     ->			Union[str, Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :str ,      **lowerCamelCase   :List[Any]      )     ->			Tuple:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :str ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :Dict      )     ->			int:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Union[str, Any] ,      *lowerCamelCase   :int ,      **lowerCamelCase   :Dict      )     ->			List[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :int ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :str      )     ->			Optional[int]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[str] ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Union[str, Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :List[str]      )     ->			Union[str, Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Optional[Any]      )     ->			Optional[int]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :Optional[int]      )     ->			List[str]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Tuple ,      *lowerCamelCase   :str ,      **lowerCamelCase   :List[str]      )     ->			List[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Any ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :Any      )     ->			int:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :Optional[Any]      )     ->			Tuple:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Any ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :List[str]      )     ->			List[str]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :Dict      )     ->			Union[str, Any]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :int ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :str      )     ->			str:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :str ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :Union[str, Any]      )     ->			Optional[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :List[str]      )     ->			int:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Union[str, Any]      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Dict ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :int      )     ->			List[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Tuple ,      *lowerCamelCase   :Optional[Any] ,      **lowerCamelCase   :Optional[int]      )     ->			Dict:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :Any ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :List[str]      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :List[Any] ,      *lowerCamelCase   :int ,      **lowerCamelCase   :Optional[Any]      )     ->			List[str]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :int ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :List[Any]      )     ->			Any:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :str ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :str      )     ->			List[str]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :List[Any] ,      *lowerCamelCase   :str ,      **lowerCamelCase   :int      )     ->			List[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[str] ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :Any      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :Tuple      )     ->			int:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Any ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :int ,      **lowerCamelCase   :Union[str, Any]      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :str ,      *lowerCamelCase   :int ,      **lowerCamelCase   :List[str]      )     ->			List[str]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :int ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :Optional[int]      )     ->			str:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Union[str, Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :Union[str, Any]      )     ->			Optional[int]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :int ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Any      )     ->			Optional[Any]:
       requires_backends(cls ,      ["flax"]      )





