




import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
lowerCamelCase    :		Union[str, Any]			   =       sys.version_info >= (3, 10)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]=None     ,							lowercase     :	int=None					):


      '''simple docstring'''



      return field(default_factory=lambda: default     ,							metadata=lowercase					)







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						42
 UpperCamelCase						       =						42
 UpperCamelCase						       =						42
 UpperCamelCase						       =						42







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						42
 UpperCamelCase						       =						field(default='''toto'''							,				metadata={'''help''': '''help message'''}       )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						False
 UpperCamelCase						       =						True
 UpperCamelCase						       =						None







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''titi'''
 UpperCamelCase						       =						'''toto'''







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''titi'''
 UpperCamelCase						       =						'''toto'''
 UpperCamelCase						       =						42







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						"toto"

 def     a__			(    self :  str  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   BasicEnum(self.foo  )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						"toto"

 def     a__			(    self :  Optional[Any]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   MixedTypeEnum(self.foo  )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						None
 UpperCamelCase						       =						field(default=UpperCamelCase							,				metadata={'''help''': '''help message'''}       )
 UpperCamelCase						       =						None
 UpperCamelCase						       =						list_field(default=[]       )
 UpperCamelCase						       =						list_field(default=[]       )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						list_field(default=[]       )
 UpperCamelCase						       =						list_field(default=[1, 2, 3]       )
 UpperCamelCase						       =						list_field(default=['''Hallo''', '''Bonjour''', '''Hello''']       )
 UpperCamelCase						       =						list_field(default=[0.1, 0.2, 0.3]       )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						field()
 UpperCamelCase						       =						field()
 UpperCamelCase						       =						field()

 def     a__			(    self :  Optional[int]  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   BasicEnum(self.required_enum  )







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						       =						42
 UpperCamelCase						       =						field()
 UpperCamelCase						       =						None
 UpperCamelCase						       =						field(default='''toto'''							,				metadata={'''help''': '''help message'''}       )
 UpperCamelCase						       =						list_field(default=['''Hallo''', '''Bonjour''', '''Hello''']       )


if is_python_no_less_than_3_10:







 @dataclass
 class   A:



  '''simple docstring'''


  UpperCamelCase						       =						False
  UpperCamelCase						       =						True
  UpperCamelCase						       =						None







 @dataclass
 class   A:



  '''simple docstring'''


  UpperCamelCase						       =						None
  UpperCamelCase						       =						field(default=UpperCamelCase							,				metadata={'''help''': '''help message'''}       )
  UpperCamelCase						       =						None
  UpperCamelCase						       =						list_field(default=[]       )
  UpperCamelCase						       =						list_field(default=[]       )







class   A(						unittest.TestCase       ):



 '''simple docstring'''


 def     a__			(    self :  List[str]						,							A_ :  argparse.ArgumentParser						,							A_ :  argparse.ArgumentParser  )       ->     Optional[Any]:

       """simple docstring"""






       self.assertEqual(len(a._actions  )						,							len(b._actions  )  )
       for x, y in zip(a._actions						,							b._actions  ):
             lowerCamelCase_							=   {k: v for k, v in vars(A_  ).items() if k != 'container'}
             lowerCamelCase_							=   {k: v for k, v in vars(A_  ).items() if k != 'container'}

             # Choices with mixed type have custom function as "type"
             # So we need to compare results directly for equality
             if xx.get('choices'						,							A_  ) and yy.get('choices'						,							A_  ):
                   for expected_choice in yy["choices"] + xx["choices"]:
                         self.assertEqual(xx['type'](A_  )						,							yy['type'](A_  )  )
                   del xx["type"], yy["type"]

             self.assertEqual(A_						,							A_  )

 def     a__			(    self :  List[str]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument('--foo'						,							type=A_						,							required=A_  )
       expected.add_argument('--bar'						,							type=A_						,							required=A_  )
       expected.add_argument('--baz'						,							type=A_						,							required=A_  )
       expected.add_argument('--flag'						,							type=A_						,							default=A_						,							const=A_						,							nargs='?'  )
       self.argparsersEqual(A_						,							A_  )

       lowerCamelCase_							=   ['--foo', '1', '--baz', 'quux', '--bar', '0.5']
       ((lowerCamelCase_)					,    )							=   parser.parse_args_into_dataclasses(A_						,							look_for_args_file=A_  )
       self.assertFalse(example.flag  )

 def     a__			(    self :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument('--foo'						,							default=42						,							type=A_  )
       expected.add_argument('--baz'						,							default='toto'						,							type=A_						,							help='help message'  )
       self.argparsersEqual(A_						,							A_  )

 def     a__			(    self :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument('--foo'						,							type=A_						,							default=A_						,							const=A_						,							nargs='?'  )
       expected.add_argument('--baz'						,							type=A_						,							default=A_						,							const=A_						,							nargs='?'  )
       # A boolean no_* argument always has to come after its "default: True" regular counter-part
       # and its default must be set to False
       expected.add_argument('--no_baz'						,							action='store_false'						,							default=A_						,							dest='baz'  )
       expected.add_argument('--opt'						,							type=A_						,							default=A_  )

       lowerCamelCase_							=   [WithDefaultBoolExample]
       if is_python_no_less_than_3_10:
             dataclass_types.append(A_  )

       for dataclass_type in dataclass_types:
             lowerCamelCase_							=   HfArgumentParser(A_  )
             self.argparsersEqual(A_						,							A_  )

             lowerCamelCase_							=   parser.parse_args([]  )
             self.assertEqual(A_						,							Namespace(foo=A_						,							baz=A_						,							opt=A_  )  )

             lowerCamelCase_							=   parser.parse_args(['--foo', '--no_baz']  )
             self.assertEqual(A_						,							Namespace(foo=A_						,							baz=A_						,							opt=A_  )  )

             lowerCamelCase_							=   parser.parse_args(['--foo', '--baz']  )
             self.assertEqual(A_						,							Namespace(foo=A_						,							baz=A_						,							opt=A_  )  )

             lowerCamelCase_							=   parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True']  )
             self.assertEqual(A_						,							Namespace(foo=A_						,							baz=A_						,							opt=A_  )  )

             lowerCamelCase_							=   parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False']  )
             self.assertEqual(A_						,							Namespace(foo=A_						,							baz=A_						,							opt=A_  )  )

 def     a__			(    self :  List[str]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument(
           '--foo'						,							default='toto'						,							choices=['titi', 'toto', 42]						,							type=make_choice_type_function(['titi', 'toto', 42]  )						,							)
       self.argparsersEqual(A_						,							A_  )

       lowerCamelCase_							=   parser.parse_args([]  )
       self.assertEqual(args.foo						,							'toto'  )
       lowerCamelCase_							=   parser.parse_args_into_dataclasses([]  )[0]
       self.assertEqual(enum_ex.foo						,							MixedTypeEnum.toto  )

       lowerCamelCase_							=   parser.parse_args(['--foo', 'titi']  )
       self.assertEqual(args.foo						,							'titi'  )
       lowerCamelCase_							=   parser.parse_args_into_dataclasses(['--foo', 'titi']  )[0]
       self.assertEqual(enum_ex.foo						,							MixedTypeEnum.titi  )

       lowerCamelCase_							=   parser.parse_args(['--foo', '42']  )
       self.assertEqual(args.foo						,							42  )
       lowerCamelCase_							=   parser.parse_args_into_dataclasses(['--foo', '42']  )[0]
       self.assertEqual(enum_ex.foo						,							MixedTypeEnum.fourtytwo  )

 def     a__			(    self :  str  )       ->     Union[str, Any]:

       """simple docstring"""






       @dataclass
       class   A:



        '''simple docstring'''


        UpperCamelCase						       =						"toto"

       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument(
           '--foo'						,							default='toto'						,							choices=('titi', 'toto', 42)						,							type=make_choice_type_function(['titi', 'toto', 42]  )						,							)
       self.argparsersEqual(A_						,							A_  )

       lowerCamelCase_							=   parser.parse_args([]  )
       self.assertEqual(args.foo						,							'toto'  )

       lowerCamelCase_							=   parser.parse_args(['--foo', 'titi']  )
       self.assertEqual(args.foo						,							'titi'  )

       lowerCamelCase_							=   parser.parse_args(['--foo', '42']  )
       self.assertEqual(args.foo						,							42  )

 def     a__			(    self :  int  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument('--foo_int'						,							nargs='+'						,							default=[]						,							type=A_  )
       expected.add_argument('--bar_int'						,							nargs='+'						,							default=[1, 2, 3]						,							type=A_  )
       expected.add_argument('--foo_str'						,							nargs='+'						,							default=['Hallo', 'Bonjour', 'Hello']						,							type=A_  )
       expected.add_argument('--foo_float'						,							nargs='+'						,							default=[0.1, 0.2, 0.3]						,							type=A_  )

       self.argparsersEqual(A_						,							A_  )

       lowerCamelCase_							=   parser.parse_args([]  )
       self.assertEqual(
           A_						,							Namespace(foo_int=[]						,							bar_int=[1, 2, 3]						,							foo_str=['Hallo', 'Bonjour', 'Hello']						,							foo_float=[0.1, 0.2, 0.3]  )						,							)

       lowerCamelCase_							=   parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split()  )
       self.assertEqual(A_						,							Namespace(foo_int=[1]						,							bar_int=[2, 3]						,							foo_str=['a', 'b', 'c']						,							foo_float=[0.1, 0.7]  )  )

 def     a__			(    self :  Dict  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument('--foo'						,							default=A_						,							type=A_  )
       expected.add_argument('--bar'						,							default=A_						,							type=A_						,							help='help message'  )
       expected.add_argument('--baz'						,							default=A_						,							type=A_  )
       expected.add_argument('--ces'						,							nargs='+'						,							default=[]						,							type=A_  )
       expected.add_argument('--des'						,							nargs='+'						,							default=[]						,							type=A_  )

       lowerCamelCase_							=   [OptionalExample]
       if is_python_no_less_than_3_10:
             dataclass_types.append(A_  )

       for dataclass_type in dataclass_types:
             lowerCamelCase_							=   HfArgumentParser(A_  )

             self.argparsersEqual(A_						,							A_  )

             lowerCamelCase_							=   parser.parse_args([]  )
             self.assertEqual(A_						,							Namespace(foo=A_						,							bar=A_						,							baz=A_						,							ces=[]						,							des=[]  )  )

             lowerCamelCase_							=   parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split()  )
             self.assertEqual(A_						,							Namespace(foo=12						,							bar=3.14						,							baz='42'						,							ces=['a', 'b', 'c']						,							des=[1, 2, 3]  )  )

 def     a__			(    self :  str  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument('--required_list'						,							nargs='+'						,							type=A_						,							required=A_  )
       expected.add_argument('--required_str'						,							type=A_						,							required=A_  )
       expected.add_argument(
           '--required_enum'						,							type=make_choice_type_function(['titi', 'toto']  )						,							choices=['titi', 'toto']						,							required=A_						,							)
       self.argparsersEqual(A_						,							A_  )

 def     a__			(    self :  Any  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   argparse.ArgumentParser()
       expected.add_argument('--foo'						,							type=A_						,							required=A_  )
       expected.add_argument(
           '--required_enum'						,							type=make_choice_type_function(['titi', 'toto']  )						,							choices=['titi', 'toto']						,							required=A_						,							)
       expected.add_argument('--opt'						,							type=A_						,							default=A_  )
       expected.add_argument('--baz'						,							default='toto'						,							type=A_						,							help='help message'  )
       expected.add_argument('--foo_str'						,							nargs='+'						,							default=['Hallo', 'Bonjour', 'Hello']						,							type=A_  )
       self.argparsersEqual(A_						,							A_  )

 def     a__			(    self :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   {
           'foo': 12,
           'bar': 3.14,
           'baz': '42',
           'flag': True,
       }

       lowerCamelCase_							=   parser.parse_dict(A_  )[0]
       lowerCamelCase_							=   BasicExample(**A_  )
       self.assertEqual(A_						,							A_  )

 def     a__			(    self :  Union[str, Any]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   {
           'foo': 12,
           'bar': 3.14,
           'baz': '42',
           'flag': True,
           'extra': 42,
       }

       self.assertRaises(A_						,							parser.parse_dict						,							A_						,							allow_extra_keys=A_  )

 def     a__			(    self :  Optional[Any]  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   {
           'foo': 12,
           'bar': 3.14,
           'baz': '42',
           'flag': True,
       }
       with tempfile.TemporaryDirectory() as tmp_dir:
             lowerCamelCase_							=   os.path.join(A_						,							'temp_json'  )
             os.mkdir(A_  )
             with open(temp_local_path + '.json'						,							'w+'  ) as f:
                   json.dump(A_						,							A_  )
             lowerCamelCase_							=   parser.parse_yaml_file(Path(temp_local_path + '.json'  )  )[0]

       lowerCamelCase_							=   BasicExample(**A_  )
       self.assertEqual(A_						,							A_  )

 def     a__			(    self :  int  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )

       lowerCamelCase_							=   {
           'foo': 12,
           'bar': 3.14,
           'baz': '42',
           'flag': True,
       }
       with tempfile.TemporaryDirectory() as tmp_dir:
             lowerCamelCase_							=   os.path.join(A_						,							'temp_yaml'  )
             os.mkdir(A_  )
             with open(temp_local_path + '.yaml'						,							'w+'  ) as f:
                   yaml.dump(A_						,							A_  )
             lowerCamelCase_							=   parser.parse_yaml_file(Path(temp_local_path + '.yaml'  )  )[0]
       lowerCamelCase_							=   BasicExample(**A_  )
       self.assertEqual(A_						,							A_  )





 def     a__			(    self :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   HfArgumentParser(A_  )
       self.assertIsNotNone(A_  )









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[Any]			   =       "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase    :		List[Any]			   =       direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCamelCase    :		Optional[Any]			   =       re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCamelCase    :		List[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    :		Tuple			   =       [
    ("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      (							lowercase     :	int					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      lowerCamelCase_							=   {
          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.
      lowerCamelCase_							=   collections.defaultdict(lowercase					)
      lowerCamelCase_							=   collections.defaultdict(lowercase					)
      lowerCamelCase_							=   collections.defaultdict(lowercase					)

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

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

      lowerCamelCase_							=   set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
      lowerCamelCase_							=   list(lowercase					)
      all_models.sort()

      lowerCamelCase_							=   {'model_type': all_models}
      lowerCamelCase_							=   [pt_models[t] for t in all_models]
      lowerCamelCase_							=   [tf_models[t] for t in all_models]
      lowerCamelCase_							=   [flax_models[t] for t in all_models]

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

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

      return pd.DataFrame(lowercase					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



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

            # 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      (							lowercase     :	str     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



      lowerCamelCase_							=   get_frameworks_table()
      lowerCamelCase_							=   Dataset.from_pandas(lowercase					)

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

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

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

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

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

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



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

      if len(lowercase					) > 0:
            lowerCamelCase_							=   ', '.join(lowercase					)
            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    :		List[str]			   =       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)



