code: 
  
  
  
  
  
  
  import importlib
  from typing import List, Optional, Union
  
  from seqeval.metrics import accuracy_score, classification_report
  
  import datasets
  
  
  lowerCamelCase										=  '''\
  @inproceedings{ramshaw-marcus-1995-text,
      title = "Text Chunking using Transformation-Based Learning",
      author = "Ramshaw, Lance  and
        Marcus, Mitch",
      booktitle = "Third Workshop on Very Large Corpora",
      year = "1995",
      url = "https://www.aclweb.org/anthology/W95-0107",
  }
  @misc{seqeval,
    title={{seqeval}: A Python framework for sequence labeling evaluation},
    url={https://github.com/chakki-works/seqeval},
    note={Software available from https://github.com/chakki-works/seqeval},
    author={Hiroki Nakayama},
    year={2018},
  }
  '''
  
  lowerCamelCase										=  '''\
  seqeval is a Python framework for sequence labeling evaluation.
  seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.
  
  This is well-tested by using the Perl script conlleval, which can be used for
  measuring the performance of a system that has processed the CoNLL-2000 shared task data.
  
  seqeval supports following formats:
  IOB1
  IOB2
  IOE1
  IOE2
  IOBES
  
  See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
  '''
  
  lowerCamelCase										=  '''
  Produces labelling scores along with its sufficient statistics
  from a source against one or more references.
  
  Args:
      predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
      references: List of List of reference labels (Ground truth (correct) target values)
      suffix: True if the IOB prefix is after type, False otherwise. default: False
      scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
          default: None
      mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
          If you want to only count exact matches, pass mode="strict". default: None.
      sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
      zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
          "warn". "warn" acts as 0, but the warning is raised.
  
  Returns:
      \'scores\': dict. Summary of the scores for overall and per type
          Overall:
              \'accuracy\': accuracy,
              \'precision\': precision,
              \'recall\': recall,
              \'f1\': F1 score, also known as balanced F-score or F-measure,
          Per type:
              \'precision\': precision,
              \'recall\': recall,
              \'f1\': F1 score, also known as balanced F-score or F-measure
  Examples:
  
      >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
      >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
      >>> seqeval = datasets.load_metric("seqeval")
      >>> results = seqeval.compute(predictions=predictions, references=references)
      >>> print(list(results.keys()))
      [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
      >>> print(results["overall_f1"])
      0.5
      >>> print(results["PER"]["f1"])
      1.0
  '''
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,			_KWARGS_DESCRIPTION      )
  class 					snake_case_							(       datasets.Metric      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		Tuple:
  
  
         """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 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	Any   ,       snake_case__:	Optional[Any]   ,       snake_case__:	bool = False   ,       snake_case__:	Optional[str] = None   ,       snake_case__:	Optional[str] = None   ,       snake_case__:	Optional[List[int]] = None   ,       snake_case__:	Union[str, int] = "warn"   ,       )  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         if scheme is not None:
             try:
                 _lowercase     =	importlib.import_module('seqeval.scheme'	)
                 _lowercase     =	getattr(snake_case__   ,       snake_case__	)
             except AttributeError:
                 raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''	)
         _lowercase     =	classification_report(
             y_true=snake_case__   ,       y_pred=snake_case__   ,       suffix=snake_case__   ,       output_dict=snake_case__   ,       scheme=snake_case__   ,       mode=snake_case__   ,       sample_weight=snake_case__   ,       zero_division=snake_case__   ,       )
         report.pop('macro avg'	)
         report.pop('weighted avg'	)
         _lowercase     =	report.pop('micro avg'	)
  
         _lowercase     =	{
             type_name: {
                 'precision': score['precision'],
                 'recall': score['recall'],
                 'f1': score['f1-score'],
                 'number': score['support'],
             }
             for type_name, score in report.items()
         }
         _lowercase     =	overall_score['precision']
         _lowercase     =	overall_score['recall']
         _lowercase     =	overall_score['f1-score']
         _lowercase     =	accuracy_score(y_true=snake_case__   ,       y_pred=snake_case__	)
  
         return scores
  
style_context: 
  
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
     import PIL
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						["""pixel_values"""]
  
     def __init__(    self:	int   ,       snake_case__:	bool = True   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	bool = False   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[int, float] = 1 / 255   ,       snake_case__:	bool = False   ,       snake_case__:	bool = True   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = True   ,       **snake_case__:	Optional[Any]   ,       )  ->		None:
  
  
         """simple docstring"""
  
  
  
         super().__init__(**snake_case__	)
         _lowercase     =	size if size is not None else {'height': 346, 'width': 346}
         _lowercase     =	get_size_dict(snake_case__	)
         _lowercase     =	crop_size if crop_size is not None else {'height': 289, 'width': 289}
         _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)
  
         _lowercase     =	do_resize
         _lowercase     =	size
         _lowercase     =	resample
         _lowercase     =	do_center_crop
         _lowercase     =	crop_size
         _lowercase     =	do_rescale
         _lowercase     =	rescale_factor
         _lowercase     =	rescale_offset
         _lowercase     =	do_normalize
         _lowercase     =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _lowercase     =	image_std if image_std is not None else IMAGENET_STANDARD_STD
         _lowercase     =	include_top
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Optional[int]   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	get_size_dict(snake_case__	)
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
         return resize(
             snake_case__   ,       size=(size['height'], size['width'])   ,       resample=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	str   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	get_size_dict(snake_case__	)
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
         return center_crop(snake_case__   ,       size=(size['height'], size['width'])   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[int, float]   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	List[str]   ,       )  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if offset:
             _lowercase     =	(image - 1_27.5) * scale
             if data_format is not None:
                 _lowercase     =	to_channel_dimension_format(snake_case__   ,       snake_case__	)
             _lowercase     =	rescaled_image.astype(np.floataa	)
         else:
             _lowercase     =	rescale(snake_case__   ,       scale=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
         return rescaled_image
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Any   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         return normalize(snake_case__   ,       mean=snake_case__   ,       std=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	ImageInput   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	bool = None   ,       snake_case__:	float = None   ,       snake_case__:	bool = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[str, TensorType]] = None   ,       snake_case__:	ChannelDimension = ChannelDimension.FIRST   ,       **snake_case__:	int   ,       )  ->		PIL.Image.Image:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	do_resize if do_resize is not None else self.do_resize
         _lowercase     =	resample if resample is not None else self.resample
         _lowercase     =	do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase     =	do_rescale if do_rescale is not None else self.do_rescale
         _lowercase     =	rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase     =	rescale_offset if rescale_offset is not None else self.rescale_offset
         _lowercase     =	do_normalize if do_normalize is not None else self.do_normalize
         _lowercase     =	image_mean if image_mean is not None else self.image_mean
         _lowercase     =	image_std if image_std is not None else self.image_std
         _lowercase     =	include_top if include_top is not None else self.include_top
  
         _lowercase     =	size if size is not None else self.size
         _lowercase     =	get_size_dict(snake_case__	)
         _lowercase     =	crop_size if crop_size is not None else self.crop_size
         _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)
  
         _lowercase     =	make_list_of_images(snake_case__	)
  
         if not valid_images(snake_case__	):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'	)
  
         if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'	)
  
         if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'	)
  
         if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'	)
  
         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'	)
  
         # All transformations expect numpy arrays.
         _lowercase     =	[to_numpy_array(snake_case__	) for image in images]
  
         if do_resize:
             _lowercase     =	[self.resize(image=snake_case__   ,       size=snake_case__   ,       resample=snake_case__	) for image in images]
  
         if do_center_crop:
             _lowercase     =	[self.center_crop(image=snake_case__   ,       size=snake_case__	) for image in images]
  
         if do_rescale:
             _lowercase     =	[self.rescale(image=snake_case__   ,       scale=snake_case__   ,       offset=snake_case__	) for image in images]
  
         if do_normalize:
             _lowercase     =	[self.normalize(image=snake_case__   ,       mean=snake_case__   ,       std=snake_case__	) for image in images]
  
         if include_top:
             _lowercase     =	[self.normalize(image=snake_case__   ,       mean=[0, 0, 0]   ,       std=snake_case__	) for image in images]
  
         _lowercase     =	[to_channel_dimension_format(snake_case__   ,       snake_case__	) for image in images]
  
         _lowercase     =	{'pixel_values': images}
         return BatchFeature(data=snake_case__   ,       tensor_type=snake_case__	)
  