



"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			os.path.join(args.tf_model_dir,					"""parameters.json"""	)
						snake_case_ :Any						=			json.loads(open(_lowercase	).read()	)
						if not params:
												raise ValueError(
												    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
						if not args.output.endswith(""".pt"""	):
												snake_case_ :Optional[int]						=			args.output + """.pt"""
						snake_case_ :List[str]						=			OrderedDict()
						with tf.device("""/CPU:0"""	):
												snake_case_ :Dict						=			tf.train.load_checkpoint(args.tf_model_dir	)
												snake_case_ :str						=			reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		snake_case_ :List[Any]						=			reader.get_tensor(_lowercase	).astype(np.floataa	)
																		if key_name.endswith("""/adam_m"""	) or key_name.endswith("""/adam_v"""	):
																								continue
																		if key_name.startswith("""pasts/"""	):
																								if key_name.startswith("""pasts/mlp"""	):
																														snake_case_ :Any						=			int(key_name[9]	)
																								elif key_name.startswith("""pasts/out"""	):
																														snake_case_ :Optional[int]						=			8
																								snake_case_ :List[str]						=			"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								snake_case_ :Optional[Any]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :List[str]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/moe"""	):
																								snake_case_ :Tuple						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/switch_gating/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
																														snake_case_ :Optional[Any]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/softmlp/kernel"""	):
																														snake_case_ :List[Any]						=			"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
																														snake_case_ :Optional[int]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/wo/kernel"""	) or key_name.endswith("""/wi/kernel"""	):
																														snake_case_ :Dict						=			key_name[-9:-7]
																														for i in range(16	):
																																				snake_case_ :str						=			"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
																																				snake_case_ :Tuple						=			(
																																				    vnp[i].transpose([1, 0]	).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/mlp"""	):
																								snake_case_ :Optional[int]						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/p1/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
																														snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p1/bias"""	):
																														snake_case_ :List[Any]						=			"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
																														snake_case_ :str						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p2/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
																														snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p2/bias"""	):
																														snake_case_ :Dict						=			"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
																														snake_case_ :Any						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/ln"""	):
																								snake_case_ :Union[str, Any]						=			int(key_name[8:].split("""/"""	)[0]	)
																								if key_name.endswith("""/b"""	):
																														snake_case_ :str						=			"""model.blocks.%d.feed_forward.norm.bias""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :int						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/g"""	):
																														snake_case_ :Dict						=			"""model.blocks.%d.feed_forward.norm.weight""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/att"""	):
																								snake_case_ :List[str]						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/qkv/kernel"""	):
																														snake_case_ :Optional[int]						=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														snake_case_ :Dict						=			state[:, 0, :, :]
																														snake_case_ :int						=			state[:, 1, :, :]
																														snake_case_ :List[str]						=			state[:, 2, :, :]
																														snake_case_ :str						=			(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Any						=			(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[int]						=			(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :int						=			"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
																														snake_case_ :int						=			torch.tensor(_lowercase	)
																														snake_case_ :Optional[Any]						=			"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
																														snake_case_ :Dict						=			torch.tensor(_lowercase	)
																														snake_case_ :Dict						=			"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/o/kernel"""	):
																														snake_case_ :str						=			"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
																														snake_case_ :str						=			(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Any						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/an"""	):
																								snake_case_ :Optional[int]						=			int(key_name[8:].split("""/"""	)[0]	)
																								if key_name.endswith("""/b"""	):
																														snake_case_ :Any						=			"""model.blocks.%d.self_attn.norm.bias""" % player
																														snake_case_ :Optional[int]						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/g"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.self_attn.norm.weight""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif (
																		    key_name.startswith("""model/wte"""	)
																		    or key_name.startswith("""model/wpe"""	)
																		    or key_name.startswith("""model/ete"""	)
																		):
																								snake_case_ :List[Any]						=			{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
																								    key_name[-3:]
																								]
																								snake_case_ :Optional[Any]						=			"""model.%s.weight""" % nlayer
																								snake_case_ :Any						=			vnp.copy()  # same in embedded
																								snake_case_ :List[Any]						=			torch.tensor(_lowercase	)
																								if key_name.startswith("""model/wte"""	):
																														snake_case_ :Tuple						=			"""lm_head.weight"""
																														snake_case_ :List[str]						=			vnp.copy()  # same in embedded
																														snake_case_ :List[Any]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/wob"""	):
																								snake_case_ :str						=			"""final_logits_bias"""
																								snake_case_ :Any						=			vnp.copy()  # same in embedded
																								snake_case_ :List[Any]						=			state.reshape((1, -1)	)
																								snake_case_ :Union[str, Any]						=			torch.tensor(_lowercase	)
																		elif key_name == "model/dense/kernel":
																								snake_case_ :str						=			"""model.last_project.weight"""
																								snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :int						=			torch.tensor(_lowercase	)
																		elif key_name == "model/dense_1/bias":
																								snake_case_ :Optional[int]						=			"""model.last_project.bias"""
																								snake_case_ :Tuple						=			vnp.copy()  # same because it is one dimensional
																								snake_case_ :Any						=			torch.tensor(_lowercase	)
						torch.save(_lowercase,					args.output	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser(
						    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
						)
						parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
						parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
						__a      =				parser.parse_args()
						convert_tf_gptsan_to_pt(args)





"""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      =				"src/transformers"


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


# Regexes that match TF/Flax/PT model names.
__a      =				re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__a      =				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      =				re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__a      =				[
    ("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    A_  (			_lowercase	):







						'''simple docstring'''


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







						'''simple docstring'''


						snake_case_ :int						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_ :Dict						=			{
						    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.
						snake_case_ :Optional[Any]						=			collections.defaultdict(_lowercase	)
						snake_case_ :int						=			collections.defaultdict(_lowercase	)
						snake_case_ :List[str]						=			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	):
												snake_case_ :int						=			None
												if _re_tf_models.match(_lowercase	) is not None:
																		snake_case_ :int						=			tf_models
																		snake_case_ :List[str]						=			_re_tf_models.match(_lowercase	).groups()[0]
												elif _re_flax_models.match(_lowercase	) is not None:
																		snake_case_ :List[Any]						=			flax_models
																		snake_case_ :Any						=			_re_flax_models.match(_lowercase	).groups()[0]
												elif _re_pt_models.match(_lowercase	) is not None:
																		snake_case_ :Optional[Any]						=			pt_models
																		snake_case_ :int						=			_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:
																														snake_case_ :Optional[int]						=			True
																														break
																								# Try again after removing the last word in the name
																								snake_case_ :Optional[Any]						=			"""""".join(camel_case_split(_lowercase	)[:-1]	)

						snake_case_ :Optional[int]						=			set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
						snake_case_ :Optional[Any]						=			list(_lowercase	)
						all_models.sort()

						snake_case_ :Optional[int]						=			{"""model_type""": all_models}
						snake_case_ :Optional[int]						=			[pt_models[t] for t in all_models]
						snake_case_ :Any						=			[tf_models[t] for t in all_models]
						snake_case_ :Dict						=			[flax_models[t] for t in all_models]

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

						snake_case_ :int						=			[processors[t] for t in all_models]

						return pd.DataFrame(_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			[
						    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:
												snake_case_ :Optional[int]						=			[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
												snake_case_ :List[str]						=			[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
																		snake_case_ :Tuple						=			[]
																		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    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			get_frameworks_table()
						snake_case_ :str						=			Dataset.from_pandas(_lowercase	)

						snake_case_ :List[Any]						=			hf_hub_download(
						    """huggingface/transformers-metadata""",					"""pipeline_tags.json""",					repo_type="""dataset""",					token=_lowercase	)
						snake_case_ :List[str]						=			Dataset.from_json(_lowercase	)
						snake_case_ :int						=			{
						    tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
						    for i in range(len(_lowercase	)	)
						}
						snake_case_ :Optional[int]						=			update_pipeline_and_auto_class_table(_lowercase	)

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						snake_case_ :Tuple						=			sorted(table.keys()	)
						snake_case_ :Tuple						=			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],
						    }	)
						snake_case_ :Union[str, Any]						=			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:
																		snake_case_ :Union[str, Any]						=			(
																		    f"""Update with commit {commit_sha}\n\nSee: """
																		    f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
																		)
												else:
																		snake_case_ :List[Any]						=			"""Update"""

												upload_folder(
												    repo_id="""huggingface/transformers-metadata""",					folder_path=_lowercase,					repo_type="""dataset""",					token=_lowercase,					commit_message=_lowercase,					)
def    A_  (			):







						'''simple docstring'''


						snake_case_ :List[Any]						=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
						snake_case_ :Dict						=			transformers_module.pipelines.SUPPORTED_TASKS
						snake_case_ :List[str]						=			[]
						for key in pipeline_tasks:
												if key not in in_table:
																		snake_case_ :int						=			pipeline_tasks[key]["""pt"""]
																		if isinstance(_lowercase,					(list, tuple)	):
																								snake_case_ :Any						=			model[0]
																		snake_case_ :str						=			model.__name__
																		if model not in in_table.values():
																								missing.append(_lowercase	)

						if len(_lowercase	) > 0:
												snake_case_ :Optional[int]						=			""", """.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      =				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      =				parser.parse_args()

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