code: 
  
  
  import importlib
  from typing import List, Optional, Union
  
  from seqeval.metrics import accuracy_score, classification_report
  
  import datasets
  
  
  SCREAMING_SNAKE_CASE : List[Any]          =    "\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"
  
  SCREAMING_SNAKE_CASE : Any          =    "\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"
  
  SCREAMING_SNAKE_CASE : Tuple          =    "\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,    _KWARGS_DESCRIPTION       )
  class 					snake_case__  ( datasets.Metric       ):
  
  
  
  
  
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return datasets.MetricInfo(
  								    description=_DESCRIPTION				,    citation=_CITATION				,    homepage="""https://github.com/chakki-works/seqeval"""				,    inputs_description=_KWARGS_DESCRIPTION				,    features=datasets.Features(
  								        {
  								            """predictions""": datasets.Sequence(datasets.Value("""string"""				,    id="""label"""							)				,    id="""sequence"""							),
  								            """references""": datasets.Sequence(datasets.Value("""string"""				,    id="""label"""							)				,    id="""sequence"""							),
  								        }							)				,    codebase_urls=["""https://github.com/chakki-works/seqeval"""]				,    reference_urls=["""https://github.com/chakki-works/seqeval"""]				,    )
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ = False				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = "warn"				,    )		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if scheme is not None:
  															try:
  																						a_       :   Optional[int]			=	importlib.import_module("""seqeval.scheme"""							)
  																						a_       :   Tuple			=	getattr(UpperCamelCase_				,    UpperCamelCase_							)
  															except AttributeError:
  																						raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""							)
  								a_       :   Union[str, Any]			=	classification_report(
  								    y_true=UpperCamelCase_				,    y_pred=UpperCamelCase_				,    suffix=UpperCamelCase_				,    output_dict=UpperCamelCase_				,    scheme=UpperCamelCase_				,    mode=UpperCamelCase_				,    sample_weight=UpperCamelCase_				,    zero_division=UpperCamelCase_				,    )
  								report.pop("""macro avg"""							)
  								report.pop("""weighted avg"""							)
  								a_       :   Optional[Any]			=	report.pop("""micro avg"""							)
  
  								a_       :   str			=	{
  								    type_name: {
  								        """precision""": score["""precision"""],
  								        """recall""": score["""recall"""],
  								        """f1""": score["""f1-score"""],
  								        """number""": score["""support"""],
  								    }
  								    for type_name, score in report.items()
  								}
  								a_       :   Optional[int]			=	overall_score["""precision"""]
  								a_       :   int			=	overall_score["""recall"""]
  								a_       :   List[Any]			=	overall_score["""f1-score"""]
  								a_       :   Optional[int]			=	accuracy_score(y_true=UpperCamelCase_				,    y_pred=UpperCamelCase_							)
  
  								return scores
  
  
  
style_context: 
  
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : List[Any]          =    {
      "shi-labs/oneformer_ade20k_swin_tiny": (
          "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
      ),
      # See all OneFormer models at https://huggingface.co/models?filter=oneformer
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Tuple         = """oneformer"""
  	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if backbone_config is None:
  															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
  															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
  															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
  								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  															a_       :   str			=	backbone_config.get("""model_type"""							)
  															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
  															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)
  
  								a_       :   str			=	backbone_config
  
  								a_       :   List[str]			=	ignore_value
  								a_       :   Optional[int]			=	num_queries
  								a_       :   Optional[Any]			=	no_object_weight
  								a_       :   List[Any]			=	class_weight
  								a_       :   Union[str, Any]			=	mask_weight
  								a_       :   Union[str, Any]			=	dice_weight
  								a_       :   Optional[Any]			=	contrastive_weight
  								a_       :   Any			=	contrastive_temperature
  								a_       :   Optional[int]			=	train_num_points
  								a_       :   Tuple			=	oversample_ratio
  								a_       :   str			=	importance_sample_ratio
  								a_       :   List[Any]			=	init_std
  								a_       :   int			=	init_xavier_std
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   Optional[int]			=	is_training
  								a_       :   List[Any]			=	use_auxiliary_loss
  								a_       :   str			=	output_auxiliary_logits
  								a_       :   Dict			=	strides
  								a_       :   List[Any]			=	task_seq_len
  								a_       :   int			=	text_encoder_width
  								a_       :   List[Any]			=	text_encoder_context_length
  								a_       :   List[str]			=	text_encoder_num_layers
  								a_       :   Union[str, Any]			=	text_encoder_vocab_size
  								a_       :   Optional[Any]			=	text_encoder_proj_layers
  								a_       :   Optional[int]			=	text_encoder_n_ctx
  								a_       :   Optional[Any]			=	conv_dim
  								a_       :   str			=	mask_dim
  								a_       :   Optional[Any]			=	hidden_dim
  								a_       :   Any			=	encoder_feedforward_dim
  								a_       :   Dict			=	norm
  								a_       :   List[Any]			=	encoder_layers
  								a_       :   int			=	decoder_layers
  								a_       :   Any			=	use_task_norm
  								a_       :   Union[str, Any]			=	num_attention_heads
  								a_       :   List[str]			=	dropout
  								a_       :   List[Any]			=	dim_feedforward
  								a_       :   Union[str, Any]			=	pre_norm
  								a_       :   Dict			=	enforce_input_proj
  								a_       :   Tuple			=	query_dec_layers
  								a_       :   Any			=	common_stride
  								a_       :   List[str]			=	decoder_layers
  
  								super().__init__(**UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict[str, any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
  								a_       :   Any			=	self.backbone_config.to_dict()
  								a_       :   Union[str, Any]			=	self.__class__.model_type
  								return output
  
  
  