






'''simple docstring'''





import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:
	class 				lowercase       :


					'''simple docstring'''

					@staticmethod
					def    a__				(					*__lowerCamelCase     :     str				,     **__lowerCamelCase     :     Tuple     )							->							Dict:

						'''simple docstring'''




						pass
@is_pipeline_test
@require_vision
@require_torch
class 				lowercase       (							unittest.TestCase      ):


				'''simple docstring'''

				lowerCAmelCase__      				=							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

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

					'''simple docstring'''




					lowerCamelCase__  =     pipeline(
					    "zero-shot-object-detection"				,     model="hf-internal-testing/tiny-random-owlvit-object-detection"     )

					lowerCamelCase__  =     [
					    {
					        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
					        "candidate_labels": ["cat", "remote", "couch"],
					    }
					]
					return object_detector, examples

				def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     int     )							->							Any:

					'''simple docstring'''




					lowerCamelCase__  =     object_detector(examples[0]				,     threshold=0.0     )

					lowerCamelCase__  =     len(__lowerCamelCase     )
					self.assertGreater(__lowerCamelCase				,     0     )
					self.assertEqual(
					    __lowerCamelCase				,     [
					        {
					            "score": ANY(__lowerCamelCase     ),
					            "label": ANY(__lowerCamelCase     ),
					            "box": {"xmin": ANY(__lowerCamelCase     ), "ymin": ANY(__lowerCamelCase     ), "xmax": ANY(__lowerCamelCase     ), "ymax": ANY(__lowerCamelCase     )},
					        }
					        for i in range(__lowerCamelCase     )
					    ]				,     )

				@require_tf
				@unittest.skip("Zero Shot Object Detection not implemented in TF"     )
				def    a__				(					self     :     List[Any]     )							->							List[Any]:

					'''simple docstring'''




					pass

				@require_torch
				def    a__				(					self     :     str     )							->							Dict:

					'''simple docstring'''




					lowerCamelCase__  =     pipeline(
					    "zero-shot-object-detection"				,     model="hf-internal-testing/tiny-random-owlvit-object-detection"     )

					lowerCamelCase__  =     object_detector(
					    "./tests/fixtures/tests_samples/COCO/000000039769.png"				,     candidate_labels=["cat", "remote", "couch"]				,     threshold=0.6_4				,     )

					self.assertEqual(
					    nested_simplify(__lowerCamelCase				,     decimals=4     )				,     [
					        {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
					        {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
					        {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
					        {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
					        {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
					        {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
					        {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
					        {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
					        {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
					    ]				,     )

					lowerCamelCase__  =     object_detector(
					    [
					        {
					            "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
					            "candidate_labels": ["cat", "remote", "couch"],
					        }
					    ]				,     threshold=0.6_4				,     )

					self.assertEqual(
					    nested_simplify(__lowerCamelCase				,     decimals=4     )				,     [
					        [
					            {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
					            {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
					            {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
					            {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
					            {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
					            {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
					            {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
					            {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
					            {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
					        ]
					    ]				,     )

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

					'''simple docstring'''




					lowerCamelCase__  =     pipeline("zero-shot-object-detection"     )

					lowerCamelCase__  =     object_detector(
					    "http://images.cocodataset.org/val2017/000000039769.jpg"				,     candidate_labels=["cat", "remote", "couch"]				,     )
					self.assertEqual(
					    nested_simplify(__lowerCamelCase				,     decimals=4     )				,     [
					        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
					        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
					        {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
					        {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
					        {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
					    ]				,     )

					lowerCamelCase__  =     object_detector(
					    [
					        {
					            "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
					            "candidate_labels": ["cat", "remote", "couch"],
					        },
					        {
					            "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
					            "candidate_labels": ["cat", "remote", "couch"],
					        },
					    ]				,     )
					self.assertEqual(
					    nested_simplify(__lowerCamelCase				,     decimals=4     )				,     [
					        [
					            {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
					            {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
					            {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
					            {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
					            {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
					        ],
					        [
					            {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
					            {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
					            {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
					            {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
					            {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
					        ],
					    ]				,     )

				@require_tf
				@unittest.skip("Zero Shot Object Detection not implemented in TF"     )
				def    a__				(					self     :     Tuple     )							->							List[str]:

					'''simple docstring'''




					pass

				@require_torch
				@slow
				def    a__				(					self     :     Any     )							->							List[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     0.2
					lowerCamelCase__  =     pipeline("zero-shot-object-detection"     )

					lowerCamelCase__  =     object_detector(
					    "http://images.cocodataset.org/val2017/000000039769.jpg"				,     candidate_labels=["cat", "remote", "couch"]				,     threshold=__lowerCamelCase				,     )
					self.assertEqual(
					    nested_simplify(__lowerCamelCase				,     decimals=4     )				,     [
					        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
					        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
					        {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
					    ]				,     )




				@require_torch
				@slow
				def    a__				(					self     :     Union[str, Any]     )							->							Optional[int]:

					'''simple docstring'''




					lowerCamelCase__  =     2
					lowerCamelCase__  =     pipeline("zero-shot-object-detection"     )

					lowerCamelCase__  =     object_detector(
					    "http://images.cocodataset.org/val2017/000000039769.jpg"				,     candidate_labels=["cat", "remote", "couch"]				,     top_k=__lowerCamelCase				,     )
					self.assertEqual(
					    nested_simplify(__lowerCamelCase				,     decimals=4     )				,     [
					        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
					        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
					    ]				,     )









'''simple docstring'''





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
__A							:							List[Any]  							=		"""src/transformers"""


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__A							:							List[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_     (    lowercase__):
 lowerCamelCase__  =     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"				,     lowercase__)
 return [m.group(0) for m in matches]






def    lowerCamelCase_     (    ):
 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    lowerCamelCase_     (    lowercase__):
 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    lowerCamelCase_     (    lowercase__				,     lowercase__):
 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    lowerCamelCase_     (    ):
 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__":
 __A							:							Any  							=		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.""")
 __A							:							Dict  							=		parser.parse_args()

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

