

import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


A__							=				"\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

A__							=				"\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

A__							=				"\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,    _KWARGS_DESCRIPTION      )
class 					__UpperCamelCase					(       datasets.Metric      ):







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[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 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			Optional[Any]	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			bool = False	,			__UpperCamelCase:			Optional[str] = None	,			__UpperCamelCase:			Optional[str] = None	,			__UpperCamelCase:			Optional[List[int]] = None	,			__UpperCamelCase:			Union[str, int] = "warn"	,			):

						'''simple docstring'''





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

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

						return scores







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: int												=	"splinter"







	def __init__( self:			str	,			__UpperCamelCase:			Any=3_05_22	,			__UpperCamelCase:			List[Any]=7_68	,			__UpperCamelCase:			str=12	,			__UpperCamelCase:			Optional[Any]=12	,			__UpperCamelCase:			Optional[int]=30_72	,			__UpperCamelCase:			List[str]="gelu"	,			__UpperCamelCase:			Union[str, Any]=0.1	,			__UpperCamelCase:			Dict=0.1	,			__UpperCamelCase:			str=5_12	,			__UpperCamelCase:			Dict=2	,			__UpperCamelCase:			str=0.02	,			__UpperCamelCase:			Tuple=1E-12	,			__UpperCamelCase:			Optional[int]=True	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Optional[int]=1_04	,			**__UpperCamelCase:			Tuple	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_act
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     initializer_range
						__magic_name__				=     type_vocab_size
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     use_cache
						__magic_name__				=     question_token_id




