


'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import 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 (
       TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFXLMForMultipleChoice,
       TFXLMForQuestionAnsweringSimple,
       TFXLMForSequenceClassification,
       TFXLMForTokenClassification,
       TFXLMModel,
       TFXLMWithLMHeadModel,
       XLMConfig,
   )







class A__	:






      def __init__(			self      :Dict					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						)	-> Any:




             '''simple docstring'''
             _a   :			List[str]					 =parent
             _a   :			Tuple					 =1_3
             _a   :			Optional[Any]					 =7
             _a   :			List[Any]					 =True
             _a   :			Union[str, Any]					 =True
             _a   :			Dict					 =True
             _a   :			str					 =True
             _a   :			int					 =True
             _a   :			str					 =False
             _a   :			Optional[Any]					 =False
             _a   :			str					 =False
             _a   :			int					 =2
             _a   :			Union[str, Any]					 =9_9
             _a   :			List[Any]					 =0
             _a   :			Any					 =3_2
             _a   :			Optional[int]					 =2
             _a   :			int					 =4
             _a   :			Any					 =0.1
             _a   :			int					 =0.1
             _a   :			List[str]					 =5_1_2
             _a   :			Union[str, Any]					 =1_6
             _a   :			List[str]					 =2
             _a   :			Optional[int]					 =0.02
             _a   :			Dict					 =3
             _a   :			List[Any]					 =4
             _a   :			Union[str, Any]					 ="""last"""
             _a   :			int					 =True
             _a   :			str					 =None
             _a   :			List[str]					 =0






      def __UpperCAmelCase	(			self      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size     )
             _a   :			Tuple					 =random_attention_mask([self.batch_size, self.seq_length]					,						dtype=tf.floataa     )

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

             _a   :			Any					 =None
             if self.use_token_type_ids:
                    _a   :			Any					 =ids_tensor([self.batch_size, self.seq_length]					,						self.n_langs     )

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

             _a   :			List[Any]					 =XLMConfig(
                 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 __UpperCAmelCase	(			self      :Tuple					,						SCREAMING_SNAKE_CASE      :Tuple					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :Tuple					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :List[str]					,						)	-> str:




             '''simple docstring'''
             _a   :			int					 =TFXLMModel(config=SCREAMING_SNAKE_CASE     )
             _a   :			Optional[Any]					 ={"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
             _a   :			Union[str, Any]					 =model(SCREAMING_SNAKE_CASE     )

             _a   :			str					 =[input_ids, input_mask]
             _a   :			List[str]					 =model(SCREAMING_SNAKE_CASE     )
             self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)     )






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						)	-> Dict:




             '''simple docstring'''
             _a   :			Optional[Any]					 =TFXLMWithLMHeadModel(SCREAMING_SNAKE_CASE     )

             _a   :			Any					 ={"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
             _a   :			List[Any]					 =model(SCREAMING_SNAKE_CASE     )

             _a   :			Optional[int]					 =outputs

             self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)     )






      def __UpperCAmelCase	(			self      :Tuple					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						SCREAMING_SNAKE_CASE      :str					,						)	-> Union[str, Any]:




             '''simple docstring'''
             _a   :			Any					 =TFXLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE     )

             _a   :			List[Any]					 ={"""input_ids""": input_ids, """lengths""": input_lengths}

             _a   :			List[str]					 =model(SCREAMING_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 __UpperCAmelCase	(			self      :Dict					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						SCREAMING_SNAKE_CASE      :Tuple					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :List[Any]					,						)	-> Tuple:




             '''simple docstring'''
             _a   :			Optional[int]					 =TFXLMForSequenceClassification(SCREAMING_SNAKE_CASE     )

             _a   :			List[str]					 ={"""input_ids""": input_ids, """lengths""": input_lengths}

             _a   :			List[str]					 =model(SCREAMING_SNAKE_CASE     )

             self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.type_sequence_label_size)     )






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :Tuple					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :List[str]					,						)	-> int:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =self.num_labels
             _a   :			Optional[int]					 =TFXLMForTokenClassification(config=SCREAMING_SNAKE_CASE     )
             _a   :			Union[str, Any]					 ={"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
             _a   :			Tuple					 =model(SCREAMING_SNAKE_CASE     )
             self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)     )






      def __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :List[Any]					,						)	-> Dict:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =self.num_choices
             _a   :			Tuple					 =TFXLMForMultipleChoice(config=SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE					,						1     )					,						(1, self.num_choices, 1)     )
             _a   :			Union[str, Any]					 =tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE					,						1     )					,						(1, self.num_choices, 1)     )
             _a   :			str					 =tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE					,						1     )					,						(1, self.num_choices, 1)     )
             _a   :			int					 ={
                 """input_ids""": multiple_choice_inputs_ids,
                 """attention_mask""": multiple_choice_input_mask,
                 """token_type_ids""": multiple_choice_token_type_ids,
             }
             _a   :			Tuple					 =model(SCREAMING_SNAKE_CASE     )
             self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices)     )






      def __UpperCAmelCase	(			self      :Any     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =self.prepare_config_and_inputs()
             (
                 (
                 _a
             ) ,   (
                 _a
             ) ,   (
                 _a
             ) ,   (
                 _a
             ) ,   (
                 _a
             ) ,   (
                 _a
             ) ,   (
                 _a
             ) ,   (
                 _a
             ) ,   (
                 _a
             ) ,   
             )   :			Tuple					 =config_and_inputs
             _a   :			int					 ={
                 """input_ids""": input_ids,
                 """token_type_ids""": token_type_ids,
                 """langs""": token_type_ids,
                 """lengths""": input_lengths,
             }
             return config, inputs_dict







@require_tf
class A__	(						UpperCAmelCase__			,					UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  Tuple     	=							(
          (
              TFXLMModel,
              TFXLMWithLMHeadModel,
              TFXLMForSequenceClassification,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForTokenClassification,
              TFXLMForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      __UpperCamelCase	:  Optional[int]     	=							(
          (TFXLMWithLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      __UpperCamelCase	:  Optional[int]     	=							(
          {
              "feature-extraction": TFXLMModel,
              "fill-mask": TFXLMWithLMHeadModel,
              "question-answering": TFXLMForQuestionAnsweringSimple,
              "text-classification": TFXLMForSequenceClassification,
              "text-generation": TFXLMWithLMHeadModel,
              "token-classification": TFXLMForTokenClassification,
              "zero-shot": TFXLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __UpperCamelCase	:  Optional[Any]     	=							False
      __UpperCamelCase	:  int     	=							False






      def __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :List[Any]     )	-> str:




             '''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 __UpperCAmelCase	(			self      :Optional[int]     )	-> List[Any]:




             '''simple docstring'''
             _a   :			int					 =TFXLMModelTester(self     )
             _a   :			Any					 =ConfigTester(self					,						config_class=SCREAMING_SNAKE_CASE					,						emb_dim=3_7     )






      def __UpperCAmelCase	(			self      :int     )	-> Dict:




             '''simple docstring'''
             self.config_tester.run_common_tests()






      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Optional[int]					 =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Dict     )	-> Any:




             '''simple docstring'''
             _a   :			Dict					 =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :str     )	-> str:




             '''simple docstring'''
             _a   :			Optional[int]					 =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Optional[int]     )	-> int:




             '''simple docstring'''
             _a   :			str					 =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int     )	-> Optional[Any]:




             '''simple docstring'''
             _a   :			List[str]					 =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_xlm_for_token_classification(*SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Any     )	-> Tuple:




             '''simple docstring'''
             _a   :			Dict					 =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE     )






      @slow
      def __UpperCAmelCase	(			self      :List[Any]     )	-> Optional[int]:




             '''simple docstring'''
             for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _a   :			Optional[Any]					 =TFXLMModel.from_pretrained(SCREAMING_SNAKE_CASE     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE     )







@require_tf
class A__	(						unittest.TestCase				):






      @slow
      def __UpperCAmelCase	(			self      :List[Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             _a   :			Optional[Any]					 =TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""     )
             _a   :			Union[str, Any]					 =tf.convert_to_tensor([[1_4, 4_4_7]]					,						dtype=tf.intaa     )  # the president
             _a   :			Any					 =[
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
                 1_4,
                 4_4_7,
             ]  # the president the president the president the president the president the president the president the president the president the president
             # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
             _a   :			Optional[int]					 =model.generate(SCREAMING_SNAKE_CASE					,						do_sample=SCREAMING_SNAKE_CASE     )
             self.assertListEqual(output_ids[0].numpy().tolist()					,						SCREAMING_SNAKE_CASE     )






'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


A__:   Dict    =	'''\
@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}
}
'''

A__:   Union[str, Any]    =	'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


A__:   str    =	'''
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.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

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

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''







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






      def __UpperCAmelCase	(			self      :List[Any]     )	-> Dict:




             '''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_absolute_error.html"""
                 ]					,						)






      def __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''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 __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=None					,						SCREAMING_SNAKE_CASE      :Any="uniform_average"     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =mean_absolute_error(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						sample_weight=SCREAMING_SNAKE_CASE					,						multioutput=SCREAMING_SNAKE_CASE     )

             return {"mae": mae_score}


