




'''simple docstring'''







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_lowerCamelCase							:						int							     =  '\\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'

_lowerCamelCase							:						Any							     =  '\\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'

_lowerCamelCase							:						str							     =  '\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 					lowerCamelCase_							(	datasets.Metric							):




					'''simple docstring'''






					def     __SCREAMING_SNAKE_CASE     (   self			):
							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,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase = False,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = "warn",  ):
							if scheme is not None:
									try:
											_snake_case     	=	importlib.import_module("seqeval.scheme"			)
											_snake_case     	=	getattr(__UpperCamelCase,  __UpperCamelCase			)
									except AttributeError:
											raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''			)
							_snake_case     	=	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"			)
							_snake_case     	=	report.pop("micro avg"			)

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

							return scores











'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


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

_lowerCamelCase							:						int							     =  {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			"""flaubert"""
					lowercase__   =			{
					    """hidden_size""": """emb_dim""",
					    """num_attention_heads""": """n_heads""",
					    """num_hidden_layers""": """n_layers""",
					    """n_words""": """vocab_size""",  # For backward compatibility
					}






					def __init__(   self,  __UpperCamelCase=False,  __UpperCamelCase=0.0,  __UpperCamelCase=30_145,  __UpperCamelCase=2_048,  __UpperCamelCase=12,  __UpperCamelCase=16,  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=1,  __UpperCamelCase=True,  __UpperCamelCase=512,  __UpperCamelCase=2_048**-0.5,  __UpperCamelCase=1E-12,  __UpperCamelCase=0.02,  __UpperCamelCase=0,  __UpperCamelCase=1,  __UpperCamelCase=2,  __UpperCamelCase=3,  __UpperCamelCase=5,  __UpperCamelCase=True,  __UpperCamelCase="first",  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=0.1,  __UpperCamelCase=5,  __UpperCamelCase=5,  __UpperCamelCase=0,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=0,  **__UpperCamelCase,  ):
							_snake_case     	=	pre_norm
							_snake_case     	=	layerdrop
							_snake_case     	=	vocab_size
							_snake_case     	=	emb_dim
							_snake_case     	=	n_layers
							_snake_case     	=	n_heads
							_snake_case     	=	dropout
							_snake_case     	=	attention_dropout
							_snake_case     	=	gelu_activation
							_snake_case     	=	sinusoidal_embeddings
							_snake_case     	=	causal
							_snake_case     	=	asm
							_snake_case     	=	n_langs
							_snake_case     	=	use_lang_emb
							_snake_case     	=	layer_norm_eps
							_snake_case     	=	bos_index
							_snake_case     	=	eos_index
							_snake_case     	=	pad_index
							_snake_case     	=	unk_index
							_snake_case     	=	mask_index
							_snake_case     	=	is_encoder
							_snake_case     	=	max_position_embeddings
							_snake_case     	=	embed_init_std
							_snake_case     	=	init_std
							_snake_case     	=	summary_type
							_snake_case     	=	summary_use_proj
							_snake_case     	=	summary_activation
							_snake_case     	=	summary_proj_to_labels
							_snake_case     	=	summary_first_dropout
							_snake_case     	=	start_n_top
							_snake_case     	=	end_n_top
							_snake_case     	=	mask_token_id
							_snake_case     	=	lang_id

							if "n_words" in kwargs:
									_snake_case     	=	kwargs["n_words"]

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





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							if self.task == "multiple-choice":
									_snake_case     	=	{0: "batch", 1: "choice", 2: "sequence"}
							else:
									_snake_case     	=	{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]			)





