





"""simple docstring"""




import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_	 ="""\
@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_	 ="""\
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_	 ="""
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 					_a		( datasets.Metric							):






				def 							snake_case      (					self						:       List[Any]				)      -> List[str]:




										'''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 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       bool = False,					lowerCAmelCase__						:       Optional[str] = None,					lowerCAmelCase__						:       Optional[str] = None,					lowerCAmelCase__						:       Optional[List[int]] = None,					lowerCAmelCase__						:       Union[str, int] = "warn",					)      -> List[str]:




										'''simple docstring'''



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

										_UpperCamelCase     :   int      =       {
										    type_name: {
										        '''precision''': score['''precision'''],
										        '''recall''': score['''recall'''],
										        '''f1''': score['''f1-score'''],
										        '''number''': score['''support'''],
										    }
										    for type_name, score in report.items()
										}
										_UpperCamelCase     :   Dict      =       overall_score['''precision''']
										_UpperCamelCase     :   int      =       overall_score['''recall''']
										_UpperCamelCase     :   Tuple      =       overall_score['''f1-score''']
										_UpperCamelCase     :   List[Any]      =       accuracy_score(y_true=lowerCAmelCase__,					y_pred=lowerCAmelCase__				)

										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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__							=			logging.get_logger(__name__)


if is_vision_available():
      import PIL






class     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""pixel_values"""]


 def __init__(    self		:			str      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BICUBIC      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[int, float] = 1 / 2_5_5      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			bool = True      ,    **lowerCAmelCase__		:			str      ,    )     ->   None:
   super().__init__(**lowerCAmelCase__     )
   UpperCAmelCase          =	size if size is not None else {"shortest_edge": 2_2_4}
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )

   UpperCAmelCase          =	do_resize
   UpperCAmelCase          =	size
   UpperCAmelCase          =	resample
   UpperCAmelCase          =	do_center_crop
   UpperCAmelCase          =	crop_size
   UpperCAmelCase          =	do_rescale
   UpperCAmelCase          =	rescale_factor
   UpperCAmelCase          =	do_normalize
   UpperCAmelCase          =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   UpperCAmelCase          =	image_std if image_std is not None else OPENAI_CLIP_STD
   UpperCAmelCase          =	do_convert_rgb


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BICUBIC      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			Optional[int]      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	get_resize_output_image_size(
       lowerCAmelCase__      ,    size=(size["height"], size["width"])      ,    default_to_square=lowerCAmelCase__     )
   return resize(lowerCAmelCase__      ,    size=lowerCAmelCase__      ,    resample=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[Any]      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )
   return center_crop(lowerCAmelCase__      ,    size=(size["height"], size["width"])      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[int, float]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[Any]      ,    )     ->   Optional[int]:
   return rescale(lowerCAmelCase__      ,    scale=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[str]      ,    )     ->   np.ndarray:
   return normalize(lowerCAmelCase__      ,    mean=lowerCAmelCase__      ,    std=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			ImageInput      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			int = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			float = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    lowerCAmelCase__		:			Optional[ChannelDimension] = ChannelDimension.FIRST      ,    **lowerCAmelCase__		:			int      ,    )     ->   PIL.Image.Image:
   UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase          =	size if size is not None else self.size
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	resample if resample is not None else self.resample
   UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )
   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          =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   UpperCAmelCase          =	make_list_of_images(lowerCAmelCase__     )

   if not valid_images(lowerCAmelCase__     ):
     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_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."     )

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     UpperCAmelCase          =	[convert_to_rgb(lowerCAmelCase__     ) for image in images]

   # All transformations expect numpy arrays.
   UpperCAmelCase          =	[to_numpy_array(lowerCAmelCase__     ) for image in images]

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

   if do_center_crop:
     UpperCAmelCase          =	[self.center_crop(image=lowerCAmelCase__      ,    size=lowerCAmelCase__     ) for image in images]

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

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

   UpperCAmelCase          =	[to_channel_dimension_format(lowerCAmelCase__      ,    lowerCAmelCase__     ) for image in images]

   UpperCAmelCase          =	{"pixel_values": images}
   return BatchFeature(data=lowerCAmelCase__      ,    tensor_type=lowerCAmelCase__     )



