

from sklearn.metrics import recall_score

import datasets


lowerCAmelCase							:  Optional[int]   	=						"""
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


lowerCAmelCase							:  int   	=						"""
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while 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 y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If 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 target labels and predictions 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. Note that it 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`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


lowerCAmelCase							:  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   __lowercase				( datasets.Metric	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									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.recall_score.html"]   ,						)




			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Tuple=None   ,						lowerCAmelCase__  :     str=1   ,						lowerCAmelCase__  :     List[str]="binary"   ,						lowerCAmelCase__  :     Tuple=None   ,						lowerCAmelCase__  :     str="warn"   ,						):
									SCREAMING_SNAKE_CASE_: List[str]  		=       recall_score(
									    lowerCAmelCase__   ,						lowerCAmelCase__   ,						labels=lowerCAmelCase__   ,						pos_label=lowerCAmelCase__   ,						average=lowerCAmelCase__   ,						sample_weight=lowerCAmelCase__   ,						zero_division=lowerCAmelCase__   ,						)
									return {"recall": float(lowerCAmelCase__) if score.size == 1 else score}








from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertConfig,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertWithLMHeadModel,
  )

class __snake_case		:
    def __init__(  self		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      parent
           lowercase							:     Tuple       	=      13
           lowercase							:     str       	=      7
           lowercase							:     Dict       	=      True
           lowercase							:     Dict       	=      True
           lowercase							:     str       	=      True
           lowercase							:     List[str]       	=      True
           lowercase							:     int       	=      True
           lowercase							:     Union[str, Any]       	=      False
           lowercase							:     Dict       	=      False
           lowercase							:     List[Any]       	=      False
           lowercase							:     List[Any]       	=      2
           lowercase							:     Optional[Any]       	=      99
           lowercase							:     int       	=      0
           lowercase							:     Tuple       	=      32
           lowercase							:     int       	=      2
           lowercase							:     Tuple       	=      4
           lowercase							:     List[Any]       	=      0.1
           lowercase							:     Tuple       	=      0.1
           lowercase							:     List[Any]       	=      512
           lowercase							:     int       	=      16
           lowercase							:     Dict       	=      2
           lowercase							:     int       	=      0.02
           lowercase							:     Union[str, Any]       	=      3
           lowercase							:     Any       	=      4
           lowercase							:     List[Any]       	=      """last"""
           lowercase							:     Tuple       	=      True
           lowercase							:     List[Any]       	=      None
           lowercase							:     Any       	=      0
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size      )
           lowercase							:     List[str]       	=      random_attention_mask([self.batch_size, self.seq_length]		,dtype=tf.floataa      )

           lowercase							:     Tuple       	=      None
           if self.use_input_lengths:
                  lowercase							:     List[str]       	=      (
                      ids_tensor([self.batch_size]		,vocab_size=2      ) + self.seq_length - 2
                  )  # small variation of seq_length

           lowercase							:     Tuple       	=      None
           if self.use_token_type_ids:
                  lowercase							:     Optional[int]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.n_langs      )

           lowercase							:     List[str]       	=      None
           lowercase							:     List[str]       	=      None
           lowercase							:     Optional[Any]       	=      None
           if self.use_labels:
                  lowercase							:     List[str]       	=      ids_tensor([self.batch_size]		,self.type_sequence_label_size      )
                  lowercase							:     List[str]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.num_labels      )
                  lowercase							:     str       	=      ids_tensor([self.batch_size]		,2		,dtype=tf.floataa      )
                  lowercase							:     Optional[Any]       	=      ids_tensor([self.batch_size]		,self.num_choices      )

           lowercase							:     str       	=      FlaubertConfig(
               vocab_size=self.vocab_size		,n_special=self.n_special		,emb_dim=self.hidden_size		,n_layers=self.num_hidden_layers		,n_heads=self.num_attention_heads		,dropout=self.hidden_dropout_prob		,attention_dropout=self.attention_probs_dropout_prob		,gelu_activation=self.gelu_activation		,sinusoidal_embeddings=self.sinusoidal_embeddings		,asm=self.asm		,causal=self.causal		,n_langs=self.n_langs		,max_position_embeddings=self.max_position_embeddings		,initializer_range=self.initializer_range		,summary_type=self.summary_type		,use_proj=self.use_proj		,bos_token_id=self.bos_token_id		,)

           return (
               config,
               input_ids,
               token_type_ids,
               input_lengths,
               sequence_labels,
               token_labels,
               is_impossible_labels,
               choice_labels,
               input_mask,
           )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      TFFlaubertModel(config=snake_case      )
           lowercase							:     str       	=      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
           lowercase							:     Optional[Any]       	=      model(snake_case      )

           lowercase							:     List[Any]       	=      [input_ids, input_mask]
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      TFFlaubertWithLMHeadModel(snake_case      )

           lowercase							:     Optional[Any]       	=      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
           lowercase							:     int       	=      model(snake_case      )

           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      TFFlaubertForQuestionAnsweringSimple(snake_case      )

           lowercase							:     Union[str, Any]       	=      {"""input_ids""": input_ids, """lengths""": input_lengths}

           lowercase							:     Tuple       	=      model(snake_case      )

           self.parent.assertEqual(result.start_logits.shape		,(self.batch_size, self.seq_length)      )
           self.parent.assertEqual(result.end_logits.shape		,(self.batch_size, self.seq_length)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      TFFlaubertForSequenceClassification(snake_case      )

           lowercase							:     str       	=      {"""input_ids""": input_ids, """lengths""": input_lengths}

           lowercase							:     str       	=      model(snake_case      )

           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.type_sequence_label_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      self.num_labels
           lowercase							:     List[str]       	=      TFFlaubertForTokenClassification(config=snake_case      )
           lowercase							:     Dict       	=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      self.num_choices
           lowercase							:     Dict       	=      TFFlaubertForMultipleChoice(config=snake_case      )
           lowercase							:     Any       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Optional[Any]       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Dict       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Union[str, Any]       	=      {
               """input_ids""": multiple_choice_inputs_ids,
               """attention_mask""": multiple_choice_input_mask,
               """token_type_ids""": multiple_choice_token_type_ids,
           }
           lowercase							:     int       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_choices)      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.prepare_config_and_inputs()
           (
               (
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			(
               lowercase
           )	,			
           )							:     int       	=      config_and_inputs
           lowercase							:     List[str]       	=      {
               """input_ids""": input_ids,
               """token_type_ids""": token_type_ids,
               """langs""": token_type_ids,
               """lengths""": input_lengths,
           }
           return config, inputs_dict






@require_tf
class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Dict=				(
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    _a		:						Optional[Any]=				(
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    _a		:						Any=				(
        {
            "feature-extraction": TFFlaubertModel,
            "fill-mask": TFFlaubertWithLMHeadModel,
            "question-answering": TFFlaubertForQuestionAnsweringSimple,
            "text-classification": TFFlaubertForSequenceClassification,
            "token-classification": TFFlaubertForTokenClassification,
            "zero-shot": TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    _a		:						Tuple=				False
    _a		:						int=				False
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if (
               pipeline_test_casse_name == "QAPipelineTests"
               and tokenizer_name is not None
               and not tokenizer_name.endswith("""Fast"""      )
           ):
                  # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                  # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                  # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                  return True

           return False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      TFFlaubertModelTester(self      )
           lowercase							:     List[Any]       	=      ConfigTester(self		,config_class=snake_case		,emb_dim=37      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_model(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_lm_head(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_qa(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_token_classification(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_multiple_choice(*snake_case      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase							:     Dict       	=      TFFlaubertModel.from_pretrained(snake_case      )
                  self.assertIsNotNone(snake_case      )






@require_tf
@require_sentencepiece
@require_tokenizers
class __snake_case		(      unittest.TestCase     ):
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""      )

           lowercase							:     int       	=      tf.convert_to_tensor(
               [[0, 158, 735, 2592, 1424, 6727, 82, 1]]		,dtype=tf.intaa		,)  # "J'aime flaubert !"

           lowercase							:     Dict       	=      model(snake_case      )[0]
           lowercase							:     Union[str, Any]       	=      tf.TensorShape((1, 8, 512)      )
           self.assertEqual(output.shape		,snake_case      )
           # compare the actual values for a slice.
           lowercase							:     Tuple       	=      tf.convert_to_tensor(
               [
                   [
                       [-1.8_768_773, -1.566_555, 0.27_072_418],
                       [-1.6_920_038, -0.5_873_505, 1.9_329_599],
                       [-2.9_563_985, -1.6_993_835, 1.7_972_052],
                   ]
               ]		,dtype=tf.floataa		,)

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,expected_slice.numpy()		,atol=1e-4      )      )
