
'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFRoFormerForCausalLM,
     TFRoFormerForMaskedLM,
     TFRoFormerForMultipleChoice,
     TFRoFormerForQuestionAnswering,
     TFRoFormerForSequenceClassification,
     TFRoFormerForTokenClassification,
     TFRoFormerModel,
 )
 from transformers.models.roformer.modeling_tf_roformer import (
     TFRoFormerSelfAttention,
     TFRoFormerSinusoidalPositionalEmbedding,
 )

class 			UpperCAmelCase__    :



    def __init__(						self   :							Tuple,__A   :							Union[str, Any],__A   :							Union[str, Any]=1_3,__A   :							Optional[Any]=7,__A   :							int=True,__A   :							Union[str, Any]=True,__A   :							Tuple=True,__A   :							List[str]=True,__A   :							Union[str, Any]=9_9,__A   :							int=3_2,__A   :							List[str]=2,__A   :							Optional[int]=4,__A   :							int=3_7,__A   :							Union[str, Any]="gelu",__A   :							Optional[int]=0.1,__A   :							Optional[int]=0.1,__A   :							List[Any]=5_1_2,__A   :							int=1_6,__A   :							int=2,__A   :							str=0.02,__A   :							Tuple=3,__A   :							Union[str, Any]=4,__A   :							str=None,):
          _lowerCamelCase					: List[str]		    = parent
          _lowerCamelCase					: Dict		    = 1_3
          _lowerCamelCase					: Union[str, Any]		    = 7
          _lowerCamelCase					: int		    = True
          _lowerCamelCase					: int		    = True
          _lowerCamelCase					: List[str]		    = True
          _lowerCamelCase					: Tuple		    = True
          _lowerCamelCase					: List[str]		    = 9_9
          _lowerCamelCase					: Dict		    = 3_2
          _lowerCamelCase					: int		    = 2
          _lowerCamelCase					: str		    = 4
          _lowerCamelCase					: List[str]		    = 3_7
          _lowerCamelCase					: List[str]		    = "gelu"
          _lowerCamelCase					: List[Any]		    = 0.1
          _lowerCamelCase					: List[str]		    = 0.1
          _lowerCamelCase					: List[str]		    = 5_1_2
          _lowerCamelCase					: Optional[int]		    = 1_6
          _lowerCamelCase					: Optional[int]		    = 2
          _lowerCamelCase					: Optional[int]		    = 0.02
          _lowerCamelCase					: Dict		    = 3
          _lowerCamelCase					: List[str]		    = 4
          _lowerCamelCase					: int		    = None



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: List[Any]		    = ids_tensor([self.batch_size, self.seq_length],self.vocab_size  )

          _lowerCamelCase					: List[str]		    = None
          if self.use_input_mask:
                _lowerCamelCase					: List[str]		    = random_attention_mask([self.batch_size, self.seq_length]  )

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

          _lowerCamelCase					: Any		    = None
          _lowerCamelCase					: List[str]		    = None
          _lowerCamelCase					: List[Any]		    = None
          if self.use_labels:
                _lowerCamelCase					: str		    = ids_tensor([self.batch_size],self.type_sequence_label_size  )
                _lowerCamelCase					: List[Any]		    = ids_tensor([self.batch_size, self.seq_length],self.num_labels  )
                _lowerCamelCase					: List[str]		    = ids_tensor([self.batch_size],self.num_choices  )

          _lowerCamelCase					: 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,initializer_range=self.initializer_range,return_dict=__A,)

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



    def 	lowerCamelCase_	(						self   :							Union[str, Any],__A   :							Dict,__A   :							Optional[Any],__A   :							Union[str, Any],__A   :							Dict,__A   :							int,__A   :							Union[str, Any],__A   :							List[Any]  ):
          _lowerCamelCase					: Any		    = TFRoFormerModel(config=__A  )
          _lowerCamelCase					: Any		    = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

          _lowerCamelCase					: Dict		    = [input_ids, input_mask]
          _lowerCamelCase					: Optional[int]		    = model(__A  )

          _lowerCamelCase					: List[Any]		    = model(__A  )

          self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)  )



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							Union[str, Any],__A   :							Tuple,__A   :							Any,__A   :							Optional[Any],__A   :							int,__A   :							Optional[Any],__A   :							int  ):
          _lowerCamelCase					: Tuple		    = True
          _lowerCamelCase					: Optional[int]		    = TFRoFormerForCausalLM(config=__A  )
          _lowerCamelCase					: Tuple		    = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          _lowerCamelCase					: Union[str, Any]		    = model(__A  )["logits"]
          self.parent.assertListEqual(
              list(prediction_scores.numpy().shape  ),[self.batch_size, self.seq_length, self.vocab_size]  )



    def 	lowerCamelCase_	(						self   :							str,__A   :							Any,__A   :							Tuple,__A   :							str,__A   :							int,__A   :							Any,__A   :							Any,__A   :							str  ):
          _lowerCamelCase					: Optional[int]		    = TFRoFormerForMaskedLM(config=__A  )
          _lowerCamelCase					: Optional[int]		    = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          _lowerCamelCase					: List[str]		    = model(__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)  )



    def 	lowerCamelCase_	(						self   :							Any,__A   :							int,__A   :							Dict,__A   :							List[str],__A   :							Tuple,__A   :							List[str],__A   :							int,__A   :							Union[str, Any]  ):
          _lowerCamelCase					: int		    = self.num_labels
          _lowerCamelCase					: int		    = TFRoFormerForSequenceClassification(config=__A  )
          _lowerCamelCase					: Optional[int]		    = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          _lowerCamelCase					: Union[str, Any]		    = model(__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)  )



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							Optional[Any],__A   :							Optional[Any],__A   :							int,__A   :							Dict,__A   :							Union[str, Any],__A   :							Optional[int],__A   :							Optional[int]  ):
          _lowerCamelCase					: Optional[Any]		    = self.num_choices
          _lowerCamelCase					: Optional[int]		    = TFRoFormerForMultipleChoice(config=__A  )
          _lowerCamelCase					: int		    = tf.tile(tf.expand_dims(__A,1  ),(1, self.num_choices, 1)  )
          _lowerCamelCase					: int		    = tf.tile(tf.expand_dims(__A,1  ),(1, self.num_choices, 1)  )
          _lowerCamelCase					: Any		    = tf.tile(tf.expand_dims(__A,1  ),(1, self.num_choices, 1)  )
          _lowerCamelCase					: List[Any]		    = {
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          _lowerCamelCase					: Dict		    = model(__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)  )



    def 	lowerCamelCase_	(						self   :							Optional[Any],__A   :							Union[str, Any],__A   :							List[Any],__A   :							Union[str, Any],__A   :							Optional[int],__A   :							Any,__A   :							Any,__A   :							Optional[Any]  ):
          _lowerCamelCase					: str		    = self.num_labels
          _lowerCamelCase					: Optional[int]		    = TFRoFormerForTokenClassification(config=__A  )
          _lowerCamelCase					: Optional[Any]		    = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          _lowerCamelCase					: Optional[int]		    = model(__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)  )



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							Union[str, Any],__A   :							Dict,__A   :							Optional[Any],__A   :							List[Any],__A   :							int,__A   :							Tuple,__A   :							Optional[int]  ):
          _lowerCamelCase					: Any		    = TFRoFormerForQuestionAnswering(config=__A  )
          _lowerCamelCase					: int		    = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          _lowerCamelCase					: Union[str, Any]		    = model(__A  )
          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 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: str		    = self.prepare_config_and_inputs()
          (
              (
              _lowerCamelCase
          )    ,		(
              _lowerCamelCase
          )    ,		(
              _lowerCamelCase
          )    ,		(
              _lowerCamelCase
          )    ,		(
              _lowerCamelCase
          )    ,		(
              _lowerCamelCase
          )    ,		(
              _lowerCamelCase
          )    ,		
          )					: Dict		    = config_and_inputs
          _lowerCamelCase					: List[Any]		    = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict


@require_tf
class 			UpperCAmelCase__    (							A    ,						A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    lowerCAmelCase_					     =		(
        {
            'feature-extraction': TFRoFormerModel,
            'fill-mask': TFRoFormerForMaskedLM,
            'question-answering': TFRoFormerForQuestionAnswering,
            'text-classification': TFRoFormerForSequenceClassification,
            'text-generation': TFRoFormerForCausalLM,
            'token-classification': TFRoFormerForTokenClassification,
            'zero-shot': TFRoFormerForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		False



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							List[str],__A   :							List[Any],__A   :							int,__A   :							str,__A   :							Optional[int]  ):
          if pipeline_test_casse_name == "TextGenerationPipelineTests":
                return True

          return False



    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          _lowerCamelCase					: Dict		    = TFRoFormerModelTester(self  )
          _lowerCamelCase					: List[Any]		    = ConfigTester(self,config_class=__A,hidden_size=3_7  )



    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          self.config_tester.run_common_tests()



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__A  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Optional[Any]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head(*__A  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: int		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: Optional[int]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*__A  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: Dict		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*__A  )



    @slow
    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: List[Any]		    = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"  )
          self.assertIsNotNone(__A  )


@require_tf
class 			UpperCAmelCase__    (							unittest.TestCase			):



    @slow
    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: Optional[Any]		    = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"  )
          _lowerCamelCase					: str		    = tf.constant([[0, 1, 2, 3, 4, 5]]  )
          _lowerCamelCase					: List[str]		    = model(__A  )[0]

          # TODO Replace vocab size
          _lowerCamelCase					: List[Any]		    = 5_0_0_0_0

          _lowerCamelCase					: List[Any]		    = [1, 6, vocab_size]
          self.assertEqual(output.shape,__A  )

          print(output[:, :3, :3]  )

          # TODO Replace values below with what was printed above.
          _lowerCamelCase					: Any		    = tf.constant(
              [
                  [
                      [-0.12053341, -1.0264901, 0.29221946],
                      [-1.5133783, 0.197433, 0.15190607],
                      [-5.0135403, -3.900256, -0.84038764],
                  ]
              ]  )
          tf.debugging.assert_near(output[:, :3, :3],__A,atol=1e-4  )


@require_tf
class 			UpperCAmelCase__    (							unittest.TestCase			):
    lowerCAmelCase_					     =		1E-4



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: Tuple		    = tf.constant([[4, 1_0]]  )
          _lowerCamelCase					: Any		    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6,embedding_dim=6  )

          _lowerCamelCase					: int		    = emba(input_ids.shape  )
          _lowerCamelCase					: Union[str, Any]		    = tf.constant(
              [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]  )

          tf.debugging.assert_near(__A,__A,atol=self.tolerance  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Any		    = tf.constant(
              [
                  [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                  [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                  [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
              ]  )
          _lowerCamelCase					: Tuple		    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2,embedding_dim=5_1_2  )
          emba([2, 1_6, 5_1_2]  )
          _lowerCamelCase					: List[str]		    = emba.weight[:3, :5]

          tf.debugging.assert_near(__A,__A,atol=self.tolerance  )


@require_tf
class 			UpperCAmelCase__    (							unittest.TestCase			):
    lowerCAmelCase_					     =		1E-4



    def 	lowerCamelCase_	(						self   :							int  ):
          # 2,12,16,64
          _lowerCamelCase					: Optional[Any]		    = tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4,dtype=tf.floataa  ),shape=(2, 1_2, 1_6, 6_4)  ) / 1_0_0

          _lowerCamelCase					: Union[str, Any]		    = -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4,dtype=tf.floataa  ),shape=(2, 1_2, 1_6, 6_4)  ) / 1_0_0

          _lowerCamelCase					: Union[str, Any]		    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2,embedding_dim=6_4  )
          _lowerCamelCase					: Tuple		    = embed_positions([2, 1_6, 7_6_8]  )[None, None, :, :]

          _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
              __A,__A,__A  )

          _lowerCamelCase					: Union[str, Any]		    = tf.constant(
              [
                  [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                  [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                  [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                  [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                  [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                  [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
              ]  )
          _lowerCamelCase					: Dict		    = tf.constant(
              [
                  [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                  [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                  [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                  [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                  [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                  [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
              ]  )

          tf.debugging.assert_near(query_layer[0, 0, :6, :8],__A,atol=self.tolerance  )
          tf.debugging.assert_near(key_layer[0, 0, :6, :8],__A,atol=self.tolerance  )






from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__							:      List[str]         =    """\
@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}
}
"""

SCREAMING_SNAKE_CASE__							:      Tuple         =    """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""







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





   def 						_snake_case			(			self   )					->	List[str]:
      """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 						_snake_case			(			self   )					->	Optional[int]:
      """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 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case=None       ,      snake_case="uniform_average"       ,      snake_case=True   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[str]   		=	mean_squared_error(
          snake_case       ,      snake_case       ,      sample_weight=snake_case       ,      multioutput=snake_case       ,      squared=snake_case   )

      return {"mse": mse}

