


from sklearn.metrics import fa_score

import datasets


SCREAMING_SNAKE_CASE       :		List[str]         =				"\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"


SCREAMING_SNAKE_CASE       :		Optional[int]         =				"\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"


SCREAMING_SNAKE_CASE       :		Tuple         =				"\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      UpperCamelCase					(		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   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=1   ,							UpperCamelCase_="binary"   ,							UpperCamelCase_=None		):
        lowercase_       :List[Any]     =       fa_score(
            UpperCamelCase_   ,							UpperCamelCase_   ,							labels=UpperCamelCase_   ,							pos_label=UpperCamelCase_   ,							average=UpperCamelCase_   ,							sample_weight=UpperCamelCase_		)
        return {"f1": float(UpperCamelCase_		) if score.size == 1 else score}





from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							):
        lowercase_       :Dict     =       parent
        lowercase_       :Optional[Any]     =       13
        lowercase_       :Optional[Any]     =       7
        lowercase_       :List[Any]     =       30
        lowercase_       :int     =       self.seq_length + self.mem_len
        lowercase_       :Any     =       15
        lowercase_       :Optional[Any]     =       True
        lowercase_       :List[Any]     =       True
        lowercase_       :Any     =       99
        lowercase_       :Optional[int]     =       [10, 50, 80]
        lowercase_       :Union[str, Any]     =       32
        lowercase_       :List[Any]     =       32
        lowercase_       :Tuple     =       4
        lowercase_       :Tuple     =       8
        lowercase_       :List[Any]     =       128
        lowercase_       :Any     =       2
        lowercase_       :Tuple     =       2
        lowercase_       :Dict     =       None
        lowercase_       :Optional[Any]     =       1
        lowercase_       :Optional[int]     =       0
        lowercase_       :List[str]     =       3
        lowercase_       :Optional[int]     =       self.vocab_size - 1
        lowercase_       :List[Any]     =       0.01
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)
        lowercase_       :List[str]     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :int     =       None
        if self.use_labels:
             lowercase_       :str     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :int     =       TransfoXLConfig(
            vocab_size=self.vocab_size   ,							mem_len=self.mem_len   ,							clamp_len=self.clamp_len   ,							cutoffs=self.cutoffs   ,							d_model=self.hidden_size   ,							d_embed=self.d_embed   ,							n_head=self.num_attention_heads   ,							d_head=self.d_head   ,							d_inner=self.d_inner   ,							div_val=self.div_val   ,							n_layer=self.num_hidden_layers   ,							eos_token_id=self.eos_token_id   ,							pad_token_id=self.vocab_size - 1   ,							init_range=self.init_range   ,							num_labels=self.num_labels   ,							)

        return (config, input_ids_a, input_ids_a, lm_labels)
   def 		UpperCamelCase  (     self		):
        random.seed(self.seed		)
        tf.random.set_seed(self.seed		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLModel(UpperCamelCase_		)

        lowercase_    ,      lowercase_       :List[Any]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Dict     =       {'''input_ids''': input_ids_a, '''mems''': mems_a}

        lowercase_    ,      lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel(UpperCamelCase_		)

        lowercase_    ,      lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Optional[int]     =       {'''input_ids''': input_ids_a, '''labels''': lm_labels}
        lowercase_    ,      lowercase_       :Optional[int]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_    ,      lowercase_       :Tuple     =       model([input_ids_a, mems_a]		).to_tuple()

        lowercase_       :Union[str, Any]     =       {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

        lowercase_    ,      lowercase_       :Union[str, Any]     =       model(UpperCamelCase_		).to_tuple()

        self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)

        self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :int     =       TFTransfoXLForSequenceClassification(UpperCamelCase_		)
        lowercase_       :int     =       model(UpperCamelCase_		)
        self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)




   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.prepare_config_and_inputs()
        ((lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_))       :Tuple     =       config_and_inputs
        lowercase_       :Dict     =       {'''input_ids''': input_ids_a}
        return config, inputs_dict






@require_tf
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	str									=(
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   lowercase					:	Dict									=() if is_tf_available() else ()
   lowercase					:	List[str]									=(
       {
           """feature-extraction""": TFTransfoXLModel,
           """text-classification""": TFTransfoXLForSequenceClassification,
           """text-generation""": TFTransfoXLLMHeadModel,
           """zero-shot""": TFTransfoXLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
   lowercase					:	Optional[int]									=False
   lowercase					:	Tuple									=False
   lowercase					:	Dict									=False
   lowercase					:	Dict									=False
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if pipeline_test_casse_name == "TextGenerationPipelineTests":
             # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
             # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
             # tokenizer.
             return True

        return False
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       TFTransfoXLModelTester(self		)
        lowercase_       :str     =       ConfigTester(self   ,							config_class=UpperCamelCase_   ,							d_embed=37		)
   def 		UpperCamelCase  (     self		):
        self.config_tester.run_common_tests()
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :str     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_       :List[str]     =       [TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
             lowercase_       :Dict     =       model_class(UpperCamelCase_		)
             assert isinstance(model.get_input_embeddings()   ,							tf.keras.layers.Layer		)
             if model_class in list_other_models_with_output_ebd:
                  lowercase_       :str     =       model.get_output_embeddings()
                  assert isinstance(UpperCamelCase_   ,							tf.keras.layers.Layer		)
                  lowercase_       :Optional[int]     =       model.get_bias()
                  assert name is None
             else:
                  lowercase_       :List[Any]     =       model.get_output_embeddings()
                  assert x is None
                  lowercase_       :Dict     =       model.get_bias()
                  assert name is None
   def 		UpperCamelCase  (     self		):
        # TODO JP: Make TransfoXL XLA compliant
        pass
   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :Dict     =       TFTransfoXLModel.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)




   @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''		)
   def 		UpperCamelCase  (     self		):
        pass






@require_tf
class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   @unittest.skip('''Skip test until #12651 is resolved.'''		)
   @slow
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''		)
        # fmt: off
        lowercase_       :List[str]     =       tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]   ,							dtype=tf.intaa		)  # noqa: E231
        # fmt: on
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
        #  remainder of the story . 1883 Western Siberia ,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        # fmt: off
        lowercase_       :List[Any]     =       [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (
        #  except for Alexei and Maria ) are discovered. The voice of young son,
        #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
        #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
        #  and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially
        #  slaps him for making such an accusation, Rasputin watches as the man
        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
        #  of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
        # Nicholas II and his family were discovered. The voice of <unk> young son,
        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

        lowercase_       :Any     =       model.generate(UpperCamelCase_   ,							max_length=200   ,							do_sample=UpperCamelCase_		)
        self.assertListEqual(output_ids[0].numpy().tolist()   ,							UpperCamelCase_		)

