




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
 from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase    :		str			   =       get_tests_dir("fixtures/test_sentencepiece.model")

lowerCamelCase    :		Dict			   =       {"target_lang": "fi", "source_lang": "en"}
lowerCamelCase    :		List[Any]			   =       ">>zh<<"
lowerCamelCase    :		Tuple			   =       "Helsinki-NLP/"

if is_torch_available():
 lowerCamelCase    :		Optional[Any]			   =       "pt"
elif is_tf_available():
 lowerCamelCase    :		List[str]			   =       "tf"
else:
 lowerCamelCase    :		Any			   =       "jax"







@require_sentencepiece
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						MarianTokenizer
 UpperCamelCase						       =						False
 UpperCamelCase						       =						True

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

       """simple docstring"""






       super().setUp()
       lowerCamelCase_							=   ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
       lowerCamelCase_							=   dict(zip(A_						,							range(len(A_  )  )  )  )
       lowerCamelCase_							=   Path(self.tmpdirname  )
       save_json(A_						,							save_dir / VOCAB_FILES_NAMES['vocab']  )
       save_json(A_						,							save_dir / VOCAB_FILES_NAMES['tokenizer_config_file']  )
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
             copyfile(A_						,							save_dir / VOCAB_FILES_NAMES['source_spm']  )
             copyfile(A_						,							save_dir / VOCAB_FILES_NAMES['target_spm']  )

       lowerCamelCase_							=   MarianTokenizer.from_pretrained(self.tmpdirname  )
       tokenizer.save_pretrained(self.tmpdirname  )

 def     a__			(    self :  Any						,							**A_ :  Optional[Any]  )       ->     MarianTokenizer:

       """simple docstring"""






       return MarianTokenizer.from_pretrained(self.tmpdirname						,							**A_  )

 def     a__			(    self :  Dict						,							A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       return (
           "This is a test",
           "This is a test",
       )

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

       """simple docstring"""






       lowerCamelCase_							=   '</s>'
       lowerCamelCase_							=   0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_  )						,							A_  )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_  )						,							A_  )

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

       """simple docstring"""






       lowerCamelCase_							=   list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]						,							'</s>'  )
       self.assertEqual(vocab_keys[1]						,							'<unk>'  )
       self.assertEqual(vocab_keys[-1]						,							'<pad>'  )
       self.assertEqual(len(A_  )						,							9  )

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

       """simple docstring"""






       self.assertEqual(self.get_tokenizer().vocab_size						,							9  )

 def     a__			(    self :  int  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""  )
       lowerCamelCase_							=   en_de_tokenizer(['I am a small frog']						,							return_tensors=A_  )
       self.assertIsInstance(A_						,							A_  )
       lowerCamelCase_							=   [38, 121, 14, 697, 38848, 0]
       self.assertListEqual(A_						,							batch.input_ids[0]  )

       lowerCamelCase_							=   tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(A_  )
       lowerCamelCase_							=   [x.name for x in Path(A_  ).glob('*'  )]
       self.assertIn('source.spm'						,							A_  )
       MarianTokenizer.from_pretrained(A_  )

 def     a__			(    self :  Dict  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.get_tokenizer()

       lowerCamelCase_							=   tok(
           ['I am a small frog' * 1000, 'I am a small frog']						,							padding=A_						,							truncation=A_						,							return_tensors=A_  )
       self.assertIsInstance(A_						,							A_  )
       self.assertEqual(batch.input_ids.shape						,							(2, 512)  )

 def     a__			(    self :  Dict  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   self.get_tokenizer()
       lowerCamelCase_							=   tok(['I am a tiny frog', 'I am a small frog']						,							padding=A_						,							return_tensors=A_  )
       self.assertIsInstance(A_						,							A_  )
       self.assertEqual(batch_smaller.input_ids.shape						,							(2, 10)  )

 @slow
 def     a__			(    self :  int  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   {'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_						,							model_name='Helsinki-NLP/opus-mt-en-de'						,							revision='1a8c2263da11e68e50938f97e10cd57820bd504c'						,							decode_kwargs={'use_source_tokenizer': True}						,							)





 def     a__			(    self :  Any  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'  )

       lowerCamelCase_							=   'Tämä on testi'
       lowerCamelCase_							=   'This is a test'

       lowerCamelCase_							=   [76, 7, 2047, 2]
       lowerCamelCase_							=   [69, 12, 11, 940, 2]

       lowerCamelCase_							=   tokenizer(A_  ).input_ids
       self.assertListEqual(A_						,							A_  )

       lowerCamelCase_							=   tokenizer(text_target=A_  ).input_ids
       self.assertListEqual(A_						,							A_  )

       lowerCamelCase_							=   tokenizer.decode(A_						,							skip_special_tokens=A_  )
       self.assertEqual(A_						,							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)



