"""simple docstring"""







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_							:							Tuple				 =		"""\
@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},
}
"""

UpperCamelCase_							:							List[str]				 =		"""\
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.
"""

UpperCamelCase_							:							List[str]				 =		"""
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       _lowercase		( datasets.Metric	):

				def 							A			(  self							:     int  ):


										'''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							:     Optional[int]							,     _lowercase							:     Any							,     _lowercase							:     Dict							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[str] = None							,     _lowercase							:     Optional[str] = None							,     _lowercase							:     Optional[List[int]] = None							,     _lowercase							:     Union[str, int] = "warn"							,     ):


										'''simple docstring'''
										if scheme is not None:
																try:
																						UpperCAmelCase__       						=		importlib.import_module("""seqeval.scheme"""  )
																						UpperCAmelCase__       						=		getattr(_lowercase							,     _lowercase  )
																except AttributeError:
																						raise ValueError(f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'  )
										UpperCAmelCase__       						=		classification_report(
										    y_true=_lowercase							,     y_pred=_lowercase							,     suffix=_lowercase							,     output_dict=_lowercase							,     scheme=_lowercase							,     mode=_lowercase							,     sample_weight=_lowercase							,     zero_division=_lowercase							,     )
										report.pop("""macro avg"""  )
										report.pop("""weighted avg"""  )
										UpperCAmelCase__       						=		report.pop("""micro avg"""  )

										UpperCAmelCase__       						=		{
										    type_name: {
										        """precision""": score["""precision"""],
										        """recall""": score["""recall"""],
										        """f1""": score["""f1-score"""],
										        """number""": score["""support"""],
										    }
										    for type_name, score in report.items()
										}
										UpperCAmelCase__       						=		overall_score["""precision"""]
										UpperCAmelCase__       						=		overall_score["""recall"""]
										UpperCAmelCase__       						=		overall_score["""f1-score"""]
										UpperCAmelCase__       						=		accuracy_score(y_true=_lowercase							,     y_pred=_lowercase  )

										return scores


"""simple docstring"""







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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCamelCase_							:							Optional[int]				 =		logging.get_logger(__name__)


if is_vision_available():
						import PIL


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							['pixel_values']

				def __init__(  self							:     str							,     _lowercase							:     bool = True							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = PILImageResampling.BILINEAR							,     _lowercase							:     bool = True							,     _lowercase							:     bool = False							,     _lowercase							:     bool = True							,     _lowercase							:     bool = True							,     _lowercase							:     Union[int, float] = 1 / 2_5_5							,     _lowercase							:     bool = True							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     **_lowercase							:     List[str]							,     ):


										'''simple docstring'''
										super().__init__(**_lowercase  )

										UpperCAmelCase__       						=		size if size is not None else {"""height""": 2_5_6_0, """width""": 1_9_2_0}
										if isinstance(_lowercase							,     (tuple, list)  ):
																# The previous feature extractor size parameter was in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )

										UpperCAmelCase__       						=		do_resize
										UpperCAmelCase__       						=		size
										UpperCAmelCase__       						=		resample
										UpperCAmelCase__       						=		do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis
										UpperCAmelCase__       						=		do_pad
										UpperCAmelCase__       						=		do_rescale
										UpperCAmelCase__       						=		rescale_factor
										UpperCAmelCase__       						=		do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										UpperCAmelCase__       						=		image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							A			(  self							:     str							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
																UpperCAmelCase__       						=		np.rotaa(_lowercase							,     3  )

										if data_format is not None:
																UpperCAmelCase__       						=		to_channel_dimension_format(_lowercase							,     _lowercase  )

										return image

				def 							A			(  self							:     Tuple							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )

										UpperCAmelCase__       						=		output_width - input_width
										UpperCAmelCase__       						=		output_height - input_height

										if random_padding:
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_height + 1  )
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_width + 1  )
										else:
																UpperCAmelCase__       						=		delta_height // 2
																UpperCAmelCase__       						=		delta_width // 2

										UpperCAmelCase__       						=		delta_height - pad_top
										UpperCAmelCase__       						=		delta_width - pad_left

										UpperCAmelCase__       						=		((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(_lowercase							,     _lowercase							,     data_format=_lowercase  )

				def 							A			(  self							:     Union[str, Any]							,     *_lowercase							:     List[str]							,     **_lowercase							:     int  ):


										'''simple docstring'''
										logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""  )
										return self.pad_image(*_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     PILImageResampling = PILImageResampling.BICUBIC							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										# We always resize to the smallest of either the input or output size.
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )

										if height == input_height and width == input_width:
																return image

										if input_height > input_width:
																UpperCAmelCase__       						=		int(input_width * height / input_height  )
										elif input_width > input_height:
																UpperCAmelCase__       						=		int(input_height * width / input_width  )

										return resize(
										    _lowercase							,     size=(height, width)							,     resample=_lowercase							,     reducing_gap=2.0							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     PILImageResampling = PILImageResampling.BICUBIC							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Optional[Any]							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		min(size["""height"""]							,     size["""width"""]  )
										UpperCAmelCase__       						=		get_resize_output_image_size(_lowercase							,     size=_lowercase							,     default_to_square=_lowercase  )
										UpperCAmelCase__       						=		resize(_lowercase							,     size=_lowercase							,     resample=_lowercase							,     data_format=_lowercase							,     **_lowercase  )
										return resized_image

				def 							A			(  self							:     int							,     _lowercase							:     np.ndarray							,     _lowercase							:     Union[int, float]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Union[str, Any]							,     ):


										'''simple docstring'''
										return rescale(_lowercase							,     scale=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Union[float, List[float]]							,     _lowercase							:     Union[float, List[float]]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Tuple							,     ):


										'''simple docstring'''
										return normalize(_lowercase							,     mean=_lowercase							,     std=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     ImageInput							,     _lowercase							:     bool = None							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = False							,     _lowercase							:     bool = None							,     _lowercase							:     float = None							,     _lowercase							:     bool = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[str, TensorType]] = None							,     _lowercase							:     Optional[ChannelDimension] = ChannelDimension.FIRST							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase__       						=		size if size is not None else self.size
										if isinstance(_lowercase							,     (tuple, list)  ):
																# Previous feature extractor had size in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		resample if resample is not None else self.resample
										UpperCAmelCase__       						=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										UpperCAmelCase__       						=		do_pad if do_pad is not None else self.do_pad
										UpperCAmelCase__       						=		do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase__       						=		rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase__       						=		do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase__       						=		image_std if image_std is not None else self.image_std

										UpperCAmelCase__       						=		make_list_of_images(_lowercase  )

										if not valid_images(_lowercase  ):
																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:
																raise ValueError("""Size must be specified if do_resize is True."""  )

										if do_rescale and rescale_factor is None:
																raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

										if do_pad and size is None:
																raise ValueError("""Size must be specified if do_pad 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.
										UpperCAmelCase__       						=		[to_numpy_array(_lowercase  ) for image in images]

										if do_align_long_axis:
																UpperCAmelCase__       						=		[self.align_long_axis(_lowercase							,     size=_lowercase  ) for image in images]

										if do_resize:
																UpperCAmelCase__       						=		[self.resize(image=_lowercase							,     size=_lowercase							,     resample=_lowercase  ) for image in images]

										if do_thumbnail:
																UpperCAmelCase__       						=		[self.thumbnail(image=_lowercase							,     size=_lowercase  ) for image in images]

										if do_pad:
																UpperCAmelCase__       						=		[self.pad_image(image=_lowercase							,     size=_lowercase							,     random_padding=_lowercase  ) for image in images]

										if do_rescale:
																UpperCAmelCase__       						=		[self.rescale(image=_lowercase							,     scale=_lowercase  ) for image in images]

										if do_normalize:
																UpperCAmelCase__       						=		[self.normalize(image=_lowercase							,     mean=_lowercase							,     std=_lowercase  ) for image in images]

										UpperCAmelCase__       						=		[to_channel_dimension_format(_lowercase							,     _lowercase  ) for image in images]

										UpperCAmelCase__       						=		{"""pixel_values""": images}
										return BatchFeature(data=_lowercase							,     tensor_type=_lowercase  )

