

'''simple docstring'''
import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
    )


class        a__  (							unittest.TestCase  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase=13      ,    _UpperCamelCase=7      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=99      ,    _UpperCamelCase=32      ,    _UpperCamelCase=5      ,    _UpperCamelCase=4      ,    _UpperCamelCase=37      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=16      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=4      ,    ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size  )

      _lowercase      :				Union[str, Any]      					=       None
      if self.use_attention_mask:
         _lowercase      :				int      					=       random_attention_mask([self.batch_size, self.seq_length]  )

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

      _lowercase      :				List[Any]      					=       RoFormerConfig(
          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=_UpperCamelCase      ,    initializer_range=self.initializer_range      ,    )

      return config, input_ids, token_type_ids, attention_mask






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       self.prepare_config_and_inputs()
      _lowercase					,				_lowercase					,				_lowercase					,				_lowercase      :				Union[str, Any]      					=       config_and_inputs
      _lowercase      :				str      					=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
      return config, inputs_dict




@require_flax
class        a__  (							lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							True

   _SCREAMING_SNAKE_CASE   :					List[str]     	=							(
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       FlaxRoFormerModelTester(self  )






   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for model_class_name in self.all_model_classes:
         _lowercase      :				str      					=       model_class_name.from_pretrained("junnyu/roformer_chinese_small"      ,    from_pt=_UpperCamelCase  )
         _lowercase      :				Tuple      					=       model(np.ones((1, 1)  )  )
         self.assertIsNotNone(_UpperCamelCase  )




@require_flax
class        a__  (							unittest.TestCase  ):


   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"  )
      _lowercase      :				Union[str, Any]      					=       jnp.array([[0, 1, 2, 3, 4, 5]]  )
      _lowercase      :				int      					=       model(_UpperCamelCase  )[0]

      _lowercase      :				Dict      					=       50000

      _lowercase      :				Any      					=       (1, 6, vocab_size)
      self.assertEqual(output.shape      ,    _UpperCamelCase  )

      _lowercase      :				Union[str, Any]      					=       jnp.array(
          [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]  )

      self.assertTrue(jnp.allclose(output[:, :3, :3]      ,    _UpperCamelCase      ,    atol=1E-4  )  )









'''simple docstring'''
from sklearn.metrics import mean_squared_error

import datasets


_snake_case         =  '\\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'

_snake_case         =  '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


_snake_case         =  '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'


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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return datasets.MetricInfo(
          description=_DESCRIPTION      ,    citation=_CITATION      ,    inputs_description=_KWARGS_DESCRIPTION      ,    features=datasets.Features(self._get_feature_types()  )      ,    reference_urls=[
              "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
          ]      ,    )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"  )  ),
             "references": datasets.Sequence(datasets.Value("float"  )  ),
         }
      else:
         return {
             "predictions": datasets.Value("float"  ),
             "references": datasets.Value("float"  ),
         }






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase="uniform_average"      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      _lowercase      :				str      					=       mean_squared_error(
          _UpperCamelCase      ,    _UpperCamelCase      ,    sample_weight=_UpperCamelCase      ,    multioutput=_UpperCamelCase      ,    squared=_UpperCamelCase  )

      return {"mse": mse}






