'''simple docstring'''


import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )






class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=1_3				,		snake_case_=7				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		snake_case_=9_9				,		snake_case_=3_2				,		snake_case_=5				,		snake_case_=4				,		snake_case_=3_7				,		snake_case_="gelu"				,		snake_case_=0.1				,		snake_case_=0.1				,		snake_case_=5_1_2				,		snake_case_=1_6				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=4				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				parent
       UpperCAmelCase_							:       Tuple       =				batch_size
       UpperCAmelCase_							:       List[Any]       =				seq_length
       UpperCAmelCase_							:       Optional[int]       =				is_training
       UpperCAmelCase_							:       List[str]       =				use_attention_mask
       UpperCAmelCase_							:       List[Any]       =				use_token_type_ids
       UpperCAmelCase_							:       Optional[int]       =				use_labels
       UpperCAmelCase_							:       List[str]       =				vocab_size
       UpperCAmelCase_							:       Optional[int]       =				hidden_size
       UpperCAmelCase_							:       Any       =				num_hidden_layers
       UpperCAmelCase_							:       List[str]       =				num_attention_heads
       UpperCAmelCase_							:       List[str]       =				intermediate_size
       UpperCAmelCase_							:       str       =				hidden_act
       UpperCAmelCase_							:       List[str]       =				hidden_dropout_prob
       UpperCAmelCase_							:       Union[str, Any]       =				attention_probs_dropout_prob
       UpperCAmelCase_							:       List[Any]       =				max_position_embeddings
       UpperCAmelCase_							:       Optional[Any]       =				type_vocab_size
       UpperCAmelCase_							:       Tuple       =				type_sequence_label_size
       UpperCAmelCase_							:       Union[str, Any]       =				initializer_range
       UpperCAmelCase_							:       int       =				num_choices


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size						)

       UpperCAmelCase_							:       Dict       =				None
       if self.use_attention_mask:
            UpperCAmelCase_							:       List[Any]       =				random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCAmelCase_							:       str       =				None
       if self.use_token_type_ids:
            UpperCAmelCase_							:       List[str]       =				ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size						)

       UpperCAmelCase_							:       List[str]       =				AlbertConfig(
           vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=snake_case_				,		initializer_range=self.initializer_range				,		)

       return config, input_ids, token_type_ids, attention_mask






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				self.prepare_config_and_inputs()
       UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				config_and_inputs
       UpperCAmelCase_							:       Dict       =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
       return config, inputs_dict







@require_flax
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Any						   =		(
      (
          FlaxAlbertModel,
          FlaxAlbertForPreTraining,
          FlaxAlbertForMaskedLM,
          FlaxAlbertForMultipleChoice,
          FlaxAlbertForQuestionAnswering,
          FlaxAlbertForSequenceClassification,
          FlaxAlbertForTokenClassification,
          FlaxAlbertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				FlaxAlbertModelTester(self						)






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       for model_class_name in self.all_model_classes:
            UpperCAmelCase_							:       Optional[Any]       =				model_class_name.from_pretrained('albert-base-v2'						)
            UpperCAmelCase_							:       Optional[Any]       =				model(np.ones((1, 1)						)						)
            self.assertIsNotNone(snake_case_						)







@require_flax
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				FlaxAlbertModel.from_pretrained('albert-base-v2'						)
       UpperCAmelCase_							:       Any       =				np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]						)
       UpperCAmelCase_							:       Union[str, Any]       =				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
       UpperCAmelCase_							:       str       =				model(snake_case_				,		attention_mask=snake_case_						)[0]
       UpperCAmelCase_							:       List[str]       =				(1, 1_1, 7_6_8)
       self.assertEqual(output.shape				,		snake_case_						)
       UpperCAmelCase_							:       Any       =				np.array(
           [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]						)

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]				,		snake_case_				,		atol=1E-4						)						)







'''simple docstring'''


from sklearn.metrics import fa_score

import datasets


snake_case__					:      str				      =  '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


snake_case__					:      int				      =  '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    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.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    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\'`.

        - \'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.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'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.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> 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])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


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






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







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       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						):




       '''simple docstring'''

       UpperCAmelCase_							:       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}






