






'''simple docstring'''





import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


__A							:							int  							=		logging.getLogger(__name__)

__A							:							List[Any]  							=		50  # max width of layer names
__A							:							Union[str, Any]  							=		70  # max width of quantizer names






def    lowerCamelCase_     (    lowercase__):
	lowerCamelCase__  =     parser.add_argument_group("quant_trainer arguments")
	group.add_argument("--wprec"				,     type=lowercase__				,     default=8				,     help="weight precision")
	group.add_argument("--aprec"				,     type=lowercase__				,     default=8				,     help="activation precision")
	group.add_argument("--quant-per-tensor"				,     action="store_true"				,     help="per tensor weight scaling")
	group.add_argument("--quant-disable"				,     action="store_true"				,     help="disable all quantizers")
	group.add_argument("--quant-disable-embeddings"				,     action="store_true"				,     help="disable all embeddings quantizers")
	group.add_argument("--quant-disable-keyword"				,     type=lowercase__				,     nargs="+"				,     help="disable quantizers by keyword")
	group.add_argument("--quant-disable-layer-module"				,     type=lowercase__				,     help="disable quantizers by keyword under layer.")
	group.add_argument("--quant-enable-layer-module"				,     type=lowercase__				,     help="enable quantizers by keyword under layer")
	group.add_argument("--calibrator"				,     default="max"				,     help="which quantization range calibrator to use")
	group.add_argument("--percentile"				,     default=lowercase__				,     type=lowercase__				,     help="percentile for PercentileCalibrator")
	group.add_argument("--fuse-qkv"				,     action="store_true"				,     help="use the same scale factor for qkv")
	group.add_argument("--clip-gelu"				,     metavar="N"				,     type=lowercase__				,     help="clip gelu output maximum value to N")
	group.add_argument(
	    "--recalibrate-weights"				,     action="store_true"				,     help=(
	        "recalibrate weight amaxes by taking the max of the weights."
	        " amaxes will be computed with the current quantization granularity (axis)."
	    )				,     )






def    lowerCamelCase_     (    lowercase__):

	if args.calibrator == "max":
		lowerCamelCase__  =     "max"
	elif args.calibrator == "percentile":
		if args.percentile is None:
			raise ValueError("Specify --percentile when using percentile calibrator")
		lowerCamelCase__  =     "histogram"
	elif args.calibrator == "mse":
		lowerCamelCase__  =     "histogram"
	else:
		raise ValueError(F'''Invalid calibrator {args.calibrator}''')

	lowerCamelCase__  =     QuantDescriptor(num_bits=args.aprec				,     calib_method=lowercase__)
	lowerCamelCase__  =     QuantDescriptor(num_bits=args.wprec				,     axis=(None if args.quant_per_tensor else (0,)))
	quant_nn.QuantLinear.set_default_quant_desc_input(lowercase__)
	quant_nn.QuantLinear.set_default_quant_desc_weight(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__=False				,     lowercase__=False):
	logger.info("Configuring Model for Quantization")
	logger.info(F'''using quantization package {pytorch_quantization.__file__}''')

	if not calib:
		if args.quant_disable_embeddings:
			set_quantizer_by_name(lowercase__				,     ["embeddings"]				,     which="weight"				,     _disabled=lowercase__)

		if args.quant_disable:
			set_quantizer_by_name(lowercase__				,     [""]				,     _disabled=lowercase__)

		if args.quant_disable_keyword:
			set_quantizer_by_name(lowercase__				,     args.quant_disable_keyword				,     _disabled=lowercase__)

		if args.quant_disable_layer_module:
			set_quantizer_by_name(lowercase__				,     [r"layer.\d+." + args.quant_disable_layer_module]				,     _disabled=lowercase__)

		if args.quant_enable_layer_module:
			set_quantizer_by_name(lowercase__				,     [r"layer.\d+." + args.quant_enable_layer_module]				,     _disabled=lowercase__)

		if args.recalibrate_weights:
			recalibrate_weights(lowercase__)

		if args.fuse_qkv:
			fuse_qkv(lowercase__				,     lowercase__)

	if args.clip_gelu:
		clip_gelu(lowercase__				,     args.clip_gelu)

	# if args.local_rank in [-1, 0] and not calib:
	print_quant_summary(lowercase__)






def    lowerCamelCase_     (    lowercase__):
	logger.info("Enabling Calibration")
	for name, module in model.named_modules():
		if name.endswith("_quantizer"):
			if module._calibrator is not None:
				module.disable_quant()
				module.enable_calib()
			else:
				module.disable()
			logger.info(F'''{name:80}: {module}''')






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
	logger.info("Loading calibrated amax")
	for name, module in model.named_modules():
		if name.endswith("_quantizer"):
			if module._calibrator is not None:
				if isinstance(module._calibrator				,     calib.MaxCalibrator):
					module.load_calib_amax()
				else:
					module.load_calib_amax("percentile"				,     percentile=args.percentile)
				module.enable_quant()
				module.disable_calib()
			else:
				module.enable()
	model.cuda()
	print_quant_summary(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):

	def fusea(lowercase__				,     lowercase__				,     lowercase__):
		for mod in [qq, qk, qv]:
			if not hasattr(lowercase__				,     "_amax"):
				print("          WARNING: NO AMAX BUFFER")
				return
		lowerCamelCase__  =     qq._amax.detach().item()
		lowerCamelCase__  =     qk._amax.detach().item()
		lowerCamelCase__  =     qv._amax.detach().item()

		lowerCamelCase__  =     max(lowercase__				,     lowercase__				,     lowercase__)
		qq._amax.fill_(lowercase__)
		qk._amax.fill_(lowercase__)
		qv._amax.fill_(lowercase__)
		logger.info(F'''          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''')

	for name, mod in model.named_modules():
		if name.endswith(".attention.self"):
			logger.info(F'''FUSE_QKV: {name:{name_width}}''')
			fusea(mod.matmul_q_input_quantizer				,     mod.matmul_k_input_quantizer				,     mod.matmul_v_input_quantizer)
			if args.quant_per_tensor:
				fusea(mod.query._weight_quantizer				,     mod.key._weight_quantizer				,     mod.value._weight_quantizer)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):

	for name, mod in model.named_modules():
		if name.endswith(".output.dense") and not name.endswith("attention.output.dense"):
			lowerCamelCase__  =     mod._input_quantizer._amax.data.detach().item()
			mod._input_quantizer._amax.data.detach().clamp_(max=lowercase__)
			lowerCamelCase__  =     mod._input_quantizer._amax.data.detach().item()
			logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''')






def    lowerCamelCase_     (    lowercase__):

	for name, mod in model.named_modules():
		if hasattr(lowercase__				,     "_weight_quantizer") and mod._weight_quantizer.axis is not None:
			lowerCamelCase__  =     mod.weight.shape[0]
			lowerCamelCase__  =     mod._weight_quantizer._amax.detach()
			lowerCamelCase__  =     torch.ones(lowercase__				,     dtype=amax.dtype				,     device=amax.device) * amax
			print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''')






def    lowerCamelCase_     (    lowercase__):

	for name, mod in model.named_modules():
		if hasattr(lowercase__				,     "_weight_quantizer"):
			if not hasattr(mod.weight_quantizer				,     "_amax"):
				print("RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER")
				continue

			# determine which axes to reduce across
			# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
			lowerCamelCase__  =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis)
			lowerCamelCase__  =     set(range(len(mod.weight.size()))) - axis_set
			lowerCamelCase__  =     pytorch_quantization.utils.reduce_amax(mod.weight				,     axis=lowercase__				,     keepdims=lowercase__).detach()
			logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''')
			lowerCamelCase__  =     amax






def    lowerCamelCase_     (    lowercase__				,     lowercase__=25				,     lowercase__=180				,     lowercase__=None):

	if ignore is None:
		lowerCamelCase__  =     []
	elif not isinstance(lowercase__				,     lowercase__):
		lowerCamelCase__  =     [ignore]

	lowerCamelCase__  =     0
	for name, mod in model.named_modules():
		if not hasattr(lowercase__				,     "weight"):
			continue
		lowerCamelCase__  =     max(lowercase__				,     len(lowercase__))

	for name, mod in model.named_modules():
		lowerCamelCase__  =     getattr(lowercase__				,     "_input_quantizer"				,     lowercase__)
		lowerCamelCase__  =     getattr(lowercase__				,     "_weight_quantizer"				,     lowercase__)
		if not hasattr(lowercase__				,     "weight"):
			continue
		if type(lowercase__) in ignore:
			continue
		if [True for s in ignore if type(lowercase__) is str and s in name]:
			continue
		lowerCamelCase__  =     F'''Act:{input_q.extra_repr()}'''
		lowerCamelCase__  =     F'''Wgt:{weight_q.extra_repr()}'''
		lowerCamelCase__  =     F'''{name:{name_width}} {act_str} {wgt_str}'''
		if len(lowercase__) <= line_width:
			logger.info(lowercase__)
		else:
			logger.info(F'''{name:{name_width}} {act_str}''')
			logger.info(F'''{"  ":{name_width}} {wgt_str}''')






def    lowerCamelCase_     (    lowercase__):
	lowerCamelCase__  =     0
	for name, mod in model.named_modules():
		if isinstance(lowercase__				,     pytorch_quantization.nn.TensorQuantizer):
			print(F'''{name:80} {mod}''')
			count += 1
	print(F'''{count} TensorQuantizers found in model''')






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
	lowerCamelCase__  =     getattr(lowercase__				,     lowercase__				,     lowercase__)
	if quantizer_mod is not None:
		assert hasattr(lowercase__				,     lowercase__)
		setattr(lowercase__				,     lowercase__				,     lowercase__)
	else:
		logger.warning(F'''{name} has no {quantizer}''')






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__="both"				,     **lowercase__):
	lowerCamelCase__  =     F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
	for k, v in kwargs.items():
		s += F''' {k}={v}'''
		if which in ["input", "both"]:
			set_quantizer(lowercase__				,     lowercase__				,     "_input_quantizer"				,     lowercase__				,     lowercase__)
		if which in ["weight", "both"]:
			set_quantizer(lowercase__				,     lowercase__				,     "_weight_quantizer"				,     lowercase__				,     lowercase__)
	logger.info(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     **lowercase__):

	for name, mod in model.named_modules():
		if hasattr(lowercase__				,     "_input_quantizer") or hasattr(lowercase__				,     "_weight_quantizer"):
			for n in names:
				if re.search(lowercase__				,     lowercase__):
					set_quantizers(lowercase__				,     lowercase__				,     **lowercase__)
		elif name.endswith("_quantizer"):
			for n in names:
				if re.search(lowercase__				,     lowercase__):
					lowerCamelCase__  =     F'''Warning: changing {name:{name_width}}'''
					for k, v in kwargs.items():
						s += F''' {k}={v}'''
						setattr(lowercase__				,     lowercase__				,     lowercase__)
					logger.info(lowercase__)









'''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)

