




"""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
lowerCamelCase__           =	"""src/transformers"""


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase__           =	[
    ("""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       (_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' ,						UpperCAmelCase__	)
	return [m.group(0	) for m in matches]
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[Any]		    =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__lowerCAmelCase    :		List[str]		    =   {
	    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    :		List[Any]		    =   collections.defaultdict(UpperCAmelCase__	)
	__lowerCAmelCase    :		int		    =   collections.defaultdict(UpperCAmelCase__	)
	__lowerCAmelCase    :		List[Any]		    =   collections.defaultdict(UpperCAmelCase__	)

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

		if lookup_dict is not None:
			while len(UpperCAmelCase__	) > 0:
				if attr_name in model_prefix_to_model_type:
					__lowerCAmelCase    :		Tuple		    =   True
					break
				# Try again after removing the last word in the name
				__lowerCAmelCase    :		Union[str, Any]		    =   ''.join(camel_case_split(UpperCAmelCase__	)[:-1]	)

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

	__lowerCAmelCase    :		Optional[Any]		    =   {'model_type': all_models}
	__lowerCAmelCase    :		int		    =   [pt_models[t] for t in all_models]
	__lowerCAmelCase    :		str		    =   [tf_models[t] for t in all_models]
	__lowerCAmelCase    :		Union[str, Any]		    =   [flax_models[t] for t in all_models]

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

	__lowerCAmelCase    :		List[Any]		    =   [processors[t] for t in all_models]

	return pd.DataFrame(UpperCAmelCase__	)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   [
	    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    :		str		    =   [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
		__lowerCAmelCase    :		Tuple		    =   [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
		# Loop through all three frameworks
		for module, cls, mapping in zip(UpperCAmelCase__ ,						UpperCAmelCase__ ,						UpperCAmelCase__	):
			# The type of pipeline may not exist in this framework
			if not hasattr(UpperCAmelCase__ ,						UpperCAmelCase__	):
				continue
			# First extract all model_names
			__lowerCAmelCase    :		List[str]		    =   []
			for name in getattr(UpperCAmelCase__ ,						UpperCAmelCase__	).values():
				if isinstance(UpperCAmelCase__ ,						UpperCAmelCase__	):
					model_names.append(UpperCAmelCase__	)
				else:
					model_names.extend(list(UpperCAmelCase__	)	)

            # 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       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   get_frameworks_table()
	__lowerCAmelCase    :		Union[str, Any]		    =   Dataset.from_pandas(UpperCAmelCase__	)

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

	# Sort the model classes to avoid some nondeterministic updates to create false update commits.
	__lowerCAmelCase    :		List[Any]		    =   sorted(table.keys()	)
	__lowerCAmelCase    :		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],
	    }	)
	__lowerCAmelCase    :		Tuple		    =   Dataset.from_pandas(UpperCAmelCase__	)

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

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

		upload_folder(
		    repo_id='huggingface/transformers-metadata' ,						folder_path=UpperCAmelCase__ ,						repo_type='dataset' ,						token=UpperCAmelCase__ ,						commit_message=UpperCAmelCase__ ,						)
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[str]		    =   {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
	__lowerCAmelCase    :		Optional[Any]		    =   transformers_module.pipelines.SUPPORTED_TASKS
	__lowerCAmelCase    :		Dict		    =   []
	for key in pipeline_tasks:
		if key not in in_table:
			__lowerCAmelCase    :		List[str]		    =   pipeline_tasks[key]['pt']
			if isinstance(UpperCAmelCase__ ,						(list, tuple)	):
				__lowerCAmelCase    :		Dict		    =   model[0]
			__lowerCAmelCase    :		Any		    =   model.__name__
			if model not in in_table.values():
				missing.append(UpperCAmelCase__	)

	if len(UpperCAmelCase__	) > 0:
		__lowerCAmelCase    :		Any		    =   ', '.join(UpperCAmelCase__	)
		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__           =	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__           =	parser.parse_args()

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

"""simple docstring"""







import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'detr'
		A_		: Union[str, Any]            =			['past_key_values']
		A_		: Any            =			{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=1_00 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1.0 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE="sine" ,	_SCREAMING_SNAKE_CASE="resnet50" ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.1 ,	**_SCREAMING_SNAKE_CASE ,	):
			if backbone_config is not None and use_timm_backbone:
				raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'				)

			if not use_timm_backbone:
				if backbone_config is None:
					logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING['resnet'](out_features=['stage4']				)
				elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		str		    =   backbone_config.get('model_type'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING[backbone_model_type]
					__lowerCAmelCase    :		str		    =   config_class.from_dict(_SCREAMING_SNAKE_CASE				)
				# set timm attributes to None
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   None, None, None

			__lowerCAmelCase    :		int		    =   use_timm_backbone
			__lowerCAmelCase    :		str		    =   backbone_config
			__lowerCAmelCase    :		Tuple		    =   num_channels
			__lowerCAmelCase    :		Union[str, Any]		    =   num_queries
			__lowerCAmelCase    :		Tuple		    =   d_model
			__lowerCAmelCase    :		Dict		    =   encoder_ffn_dim
			__lowerCAmelCase    :		Dict		    =   encoder_layers
			__lowerCAmelCase    :		Optional[int]		    =   encoder_attention_heads
			__lowerCAmelCase    :		Union[str, Any]		    =   decoder_ffn_dim
			__lowerCAmelCase    :		Union[str, Any]		    =   decoder_layers
			__lowerCAmelCase    :		Any		    =   decoder_attention_heads
			__lowerCAmelCase    :		Tuple		    =   dropout
			__lowerCAmelCase    :		Union[str, Any]		    =   attention_dropout
			__lowerCAmelCase    :		Any		    =   activation_dropout
			__lowerCAmelCase    :		Tuple		    =   activation_function
			__lowerCAmelCase    :		Any		    =   init_std
			__lowerCAmelCase    :		Any		    =   init_xavier_std
			__lowerCAmelCase    :		str		    =   encoder_layerdrop
			__lowerCAmelCase    :		Tuple		    =   decoder_layerdrop
			__lowerCAmelCase    :		Any		    =   encoder_layers
			__lowerCAmelCase    :		List[Any]		    =   auxiliary_loss
			__lowerCAmelCase    :		Tuple		    =   position_embedding_type
			__lowerCAmelCase    :		Dict		    =   backbone
			__lowerCAmelCase    :		Dict		    =   use_pretrained_backbone
			__lowerCAmelCase    :		Optional[Any]		    =   dilation
			# Hungarian matcher
			__lowerCAmelCase    :		Dict		    =   class_cost
			__lowerCAmelCase    :		str		    =   bbox_cost
			__lowerCAmelCase    :		Optional[Any]		    =   giou_cost
			# Loss coefficients
			__lowerCAmelCase    :		Tuple		    =   mask_loss_coefficient
			__lowerCAmelCase    :		List[Any]		    =   dice_loss_coefficient
			__lowerCAmelCase    :		List[str]		    =   bbox_loss_coefficient
			__lowerCAmelCase    :		Optional[int]		    =   giou_loss_coefficient
			__lowerCAmelCase    :		Union[str, Any]		    =   eos_coefficient
			super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		@property
		def 							__lowerCamelCase							(   self				):
			return self.encoder_attention_heads

		@property
		def 							__lowerCamelCase							(   self				):
			return self.d_model

		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return cls(backbone_config=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   copy.deepcopy(self.__dict__				)
			if output["backbone_config"] is not None:
				__lowerCAmelCase    :		Dict		    =   self.backbone_config.to_dict()
			__lowerCAmelCase    :		List[str]		    =   self.__class__.model_type
			return output




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

		@property
		def 							__lowerCamelCase							(   self				):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			        ('pixel_mask', {0: 'batch'}),
			    ]				)

		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-5







		@property
		def 							__lowerCamelCase							(   self				):
			return 12