






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


snake_case_   :	Dict							      ='''\
@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},
}
'''

snake_case_   :	Dict							      ='''\
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.
'''

snake_case_   :	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        a__      (	datasets.Metric		):






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				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 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__ = False							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = "warn"							, )				->				Optional[Any]:
				if scheme is not None:
						try:
								__A    =							importlib.import_module("seqeval.scheme"						)
								__A    =							getattr(lowercase__							, lowercase__						)
						except AttributeError:
								raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""						)
				__A    =							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"						)
				__A    =							report.pop("micro avg"						)

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

				return scores









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_   :	Optional[Any]							      =logging.get_logger(__name__)

snake_case_   :	Any							      ={
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Union[str, Any] 					= 'swiftformer'






		def __init__(      self							, lowercase__=3							, lowercase__=[3, 3, 6, 4]							, lowercase__=[48, 56, 112, 220]							, lowercase__=4							, lowercase__=[True, True, True, True]							, lowercase__="gelu"							, lowercase__=3							, lowercase__=2							, lowercase__=1							, lowercase__=0.0							, lowercase__=True							, lowercase__=1e-5							, lowercase__=1e-5							, **lowercase__							, )				->				Optional[Any]:
				super().__init__(**lowercase__						)
				__A    =							num_channels
				__A    =							depths
				__A    =							embed_dims
				__A    =							mlp_ratio
				__A    =							downsamples
				__A    =							hidden_act
				__A    =							down_patch_size
				__A    =							down_stride
				__A    =							down_pad
				__A    =							drop_path_rate
				__A    =							use_layer_scale
				__A    =							layer_scale_init_value
				__A    =							batch_norm_eps





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Union[str, Any] 					= version.parse('1.11'		)






		@property
		def 					_lowerCamelCase					(      self						)				->				Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]						)






		@property
		def 					_lowerCamelCase					(      self						)				->				float:
				return 1e-4

