





import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_UpperCAmelCase				:       Union[str, Any]	   =				logging.get_logger(__name__)

@dataclass
class    lowerCAmelCase							:
 UpperCAmelCase__													=		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()			)}			)
 UpperCAmelCase__													=		field(
     metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}			)
 UpperCAmelCase__													=		field(
     default=1_28,  metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated, sequences shorter will be padded."""
         )
     },  )
 UpperCAmelCase__													=		field(
     default=__UpperCamelCase,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)
 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     lowerCamelCase__    :      Optional[Any]					 =		self.task_name.lower()


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""train"""
 UpperCAmelCase__													=		"""dev"""
 UpperCAmelCase__													=		"""test"""


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		42
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      GlueDataTrainingArguments			,     UpperCAmelCase  :      PreTrainedTokenizerBase			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      Union[str, Split] = Split.train			,     UpperCAmelCase  :      Optional[str] = None			,     )			->	int:
     warnings.warn(
         'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
         'library. You can have a look at this example script for pointers: '
         'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'			,     UpperCAmelCase			,     )
     lowerCamelCase__    :      List[Any]					 =		args
     lowerCamelCase__    :      List[Any]					 =		glue_processors[args.task_name]()
     lowerCamelCase__    :      Union[str, Any]					 =		glue_output_modes[args.task_name]
     if isinstance(UpperCAmelCase			,     UpperCAmelCase	):
         try:
             lowerCamelCase__    :      Union[str, Any]					 =		Split[mode]
         except KeyError:
             raise KeyError('mode is not a valid split name'	)
        # Load data features from cache or dataset file
     lowerCamelCase__    :      int					 =		os.path.join(
         cache_dir if cache_dir is not None else args.data_dir			,     F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""			,     )
     lowerCamelCase__    :      List[str]					 =		self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		label_list[2], label_list[1]
     lowerCamelCase__    :      int					 =		label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     lowerCamelCase__    :      Union[str, Any]					 =		cached_features_file + '.lock'
     with FileLock(UpperCAmelCase	):
         if os.path.exists(UpperCAmelCase	) and not args.overwrite_cache:
             lowerCamelCase__    :      Tuple					 =		time.time()
             lowerCamelCase__    :      Union[str, Any]					 =		torch.load(UpperCAmelCase	)
             logger.info(
                 F"""Loading features from cached file {cached_features_file} [took %.3f s]"""			,     time.time() - start	)
         else:
             logger.info(F"""Creating features from dataset file at {args.data_dir}"""	)

             if mode == Split.dev:
                 lowerCamelCase__    :      str					 =		self.processor.get_dev_examples(args.data_dir	)
             elif mode == Split.test:
                 lowerCamelCase__    :      str					 =		self.processor.get_test_examples(args.data_dir	)
             else:
                 lowerCamelCase__    :      List[str]					 =		self.processor.get_train_examples(args.data_dir	)
             if limit_length is not None:
                 lowerCamelCase__    :      Any					 =		examples[:limit_length]
             lowerCamelCase__    :      Tuple					 =		glue_convert_examples_to_features(
                 UpperCAmelCase			,     UpperCAmelCase			,     max_length=args.max_seq_length			,     label_list=UpperCAmelCase			,     output_mode=self.output_mode			,     )
             lowerCamelCase__    :      Union[str, Any]					 =		time.time()
             torch.save(self.features			,     UpperCAmelCase	)
             # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
             logger.info(
                 F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""	)
 def __len__(      self  :      Optional[int]	)			->	Union[str, Any]:
     return len(self.features	)
 def __getitem__(      self  :      Any			,     UpperCAmelCase  :      Optional[int]	)			->	InputFeatures:
     return self.features[i]







 def      A_	(      self  :      Tuple	)			->	List[str]:
     return self.label_list









'''simple docstring'''


from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_		:   Optional[int]							   =		'\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


UpperCAmelCase_		:   Tuple							   =		'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


UpperCAmelCase_		:   Dict							   =		'\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n'





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





							'''simple docstring'''


							def UpperCAmelCase_	(    self  ):
											return datasets.MetricInfo(
											    description=_DESCRIPTION    ,					citation=_CITATION    ,					inputs_description=_KWARGS_DESCRIPTION    ,					features=datasets.Features(
											        {
											            """predictions""": datasets.Sequence(datasets.Value("""int32"""  )  ),
											            """references""": datasets.Sequence(datasets.Value("""int32"""  )  ),
											        }
											        if self.config_name == """multilabel"""
											        else {
											            """predictions""": datasets.Value("""int32"""  ),
											            """references""": datasets.Value("""int32"""  ),
											        }  )    ,					reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""]    ,					)






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case=None    ,					__snake_case=1    ,					__snake_case="binary"    ,					__snake_case=None  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      fa_score(
											    __snake_case    ,					__snake_case    ,					labels=__snake_case    ,					pos_label=__snake_case    ,					average=__snake_case    ,					sample_weight=__snake_case  )
											return {"f1": float(__snake_case  ) if score.size == 1 else score}




