


"""simple docstring"""






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowercase  		=			"""\
@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},
}
"""

lowercase  		=			"""\
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.
"""

lowercase  		=			"""
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 			SCREAMING_SNAKE_CASE_  (   datasets.Metric):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = False	,					lowerCamelCase__ = None	,					lowerCamelCase__ = None	,					lowerCamelCase__ = None	,					lowerCamelCase__ = "warn"	,					)   ->	Dict:







								'''simple docstring'''




								if scheme is not None:
									try:
										snake_case__  :						Dict		=      importlib.import_module("seqeval.scheme")
										snake_case__  :						Dict		=      getattr(lowerCamelCase__	,					lowerCamelCase__)
									except AttributeError:
										raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}""")
								snake_case__  :						Tuple		=      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")
								snake_case__  :						List[Any]		=      report.pop("micro avg")

								snake_case__  :						List[Any]		=      {
								    type_name: {
								        "precision": score["precision"],
								        "recall": score["recall"],
								        "f1": score["f1-score"],
								        "number": score["support"],
								    }
								    for type_name, score in report.items()
								}
								snake_case__  :						int		=      overall_score["precision"]
								snake_case__  :						List[Any]		=      overall_score["recall"]
								snake_case__  :						Dict		=      overall_score["f1-score"]
								snake_case__  :						Optional[Any]		=      accuracy_score(y_true=lowerCamelCase__	,					y_pred=lowerCamelCase__)

								return scores











from collections import OrderedDict
from typing import Mapping

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


__A    :				List[str]    						=	logging.get_logger(__name__)

__A    :				List[Any]    						=	{
    """mnaylor/mega-base-wikitext""": """https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json""",
}




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  '''mega'''





							def __init__(       self							:						Any							,  a							:						List[str]=30_522							,  a							:						Dict=128							,  a							:						Dict=4							,  a							:						Union[str, Any]=256							,  a							:						Tuple=16							,  a							:						Any=True							,  a							:						Dict=64							,  a							:						Union[str, Any]=False							,  a							:						Tuple=-1							,  a							:						str=None							,  a							:						int=True							,  a							:						Optional[int]="scalenorm"							,  a							:						Optional[int]=True							,  a							:						Dict="silu"							,  a							:						Optional[Any]="softmax"							,  a							:						Any=0.1							,  a							:						List[Any]=0.1							,  a							:						Tuple=0.1							,  a							:						Optional[int]=False							,  a							:						List[str]=True							,  a							:						Optional[int]=256							,  a							:						List[str]=True							,  a							:						int=0.1							,  a							:						str=2_048							,  a							:						Tuple=False							,  a							:						Optional[int]=2							,  a							:						Optional[int]=0.02							,  a							:						Optional[int]=0.2							,  a							:						Union[str, Any]=0.02							,  a							:						str=1.0							,  a							:						Dict=1							,  a							:						Union[str, Any]=0							,  a							:						Dict=2							,  a							:						List[str]="rotary"							,  a							:						Union[str, Any]=None							,  a							:						List[Any]=True							,  a							:						Tuple=True							,  **a							:						Optional[int]							,  )      ->       Dict:
										super().__init__(pad_token_id=a							,  bos_token_id=a							,  eos_token_id=a							,  **a      )

										SCREAMING_SNAKE_CASE     =       vocab_size
										SCREAMING_SNAKE_CASE     =       hidden_size
										SCREAMING_SNAKE_CASE     =       num_hidden_layers
										SCREAMING_SNAKE_CASE     =       activation
										SCREAMING_SNAKE_CASE     =       attention_activation
										SCREAMING_SNAKE_CASE     =       intermediate_size
										SCREAMING_SNAKE_CASE     =       ema_projection_size
										SCREAMING_SNAKE_CASE     =       bidirectional
										SCREAMING_SNAKE_CASE     =       shared_representation_size
										SCREAMING_SNAKE_CASE     =       use_chunking
										SCREAMING_SNAKE_CASE     =       chunk_size
										SCREAMING_SNAKE_CASE     =       truncation
										SCREAMING_SNAKE_CASE     =       normalize_before_mega
										SCREAMING_SNAKE_CASE     =       normalization_type
										SCREAMING_SNAKE_CASE     =       norm_affine
										SCREAMING_SNAKE_CASE     =       dropout_prob
										SCREAMING_SNAKE_CASE     =       hidden_dropout_prob
										SCREAMING_SNAKE_CASE     =       attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE     =       use_feature_dropout
										SCREAMING_SNAKE_CASE     =       use_normalized_ffn
										SCREAMING_SNAKE_CASE     =       nffn_hidden_size
										SCREAMING_SNAKE_CASE     =       normalize_before_ffn
										SCREAMING_SNAKE_CASE     =       nffn_activation_dropout_prob
										SCREAMING_SNAKE_CASE     =       max_positions
										SCREAMING_SNAKE_CASE     =       add_token_type_embeddings
										SCREAMING_SNAKE_CASE     =       type_vocab_size
										SCREAMING_SNAKE_CASE     =       initializer_range
										SCREAMING_SNAKE_CASE     =       ema_delta_alpha_range
										SCREAMING_SNAKE_CASE     =       ema_beta_range
										SCREAMING_SNAKE_CASE     =       ema_gamma_omega_range
										SCREAMING_SNAKE_CASE     =       relative_positional_bias
										SCREAMING_SNAKE_CASE     =       use_cache
										SCREAMING_SNAKE_CASE     =       classifier_dropout
										SCREAMING_SNAKE_CASE     =       add_lm_hidden_dense_layer
										SCREAMING_SNAKE_CASE     =       1  # not used but required by Hugging Face





class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							@property
							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]      )






