






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








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__	)
