




"""simple docstring"""




import numpy as np
class 							lowerCamelCase						:







   def __init__(							self			,					SCREAMING_SNAKE_CASE_=5			,					SCREAMING_SNAKE_CASE_=5       ):
       UpperCamelCase :      Dict      				=							depth
       UpperCamelCase :      List[str]      				=							0
       UpperCamelCase :      List[Any]      				=							None
       UpperCamelCase :      Optional[Any]      				=							None
       UpperCamelCase :      Optional[Any]      				=							min_leaf_size
       UpperCamelCase :      int      				=							None







   def 					a_							(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_       ):

       if labels.ndim != 1:
           print("""Error: Input labels must be one dimensional"""       )

       return np.mean((labels - prediction) ** 2       )







   def 					a_							(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_       ):

       if x.ndim != 1:
           print("""Error: Input data set must be one dimensional"""       )
           return
       if len(snake_case_       ) != len(snake_case_       ):
           print("""Error: X and y have different lengths"""       )
           return
       if y.ndim != 1:
           print("""Error: Data set labels must be one dimensional"""       )
           return

       if len(snake_case_       ) < 2 * self.min_leaf_size:
           UpperCamelCase :      Optional[Any]      				=							np.mean(snake_case_       )
           return

       if self.depth == 1:
           UpperCamelCase :      List[Any]      				=							np.mean(snake_case_       )
           return

       UpperCamelCase :      Optional[int]      				=							0
       UpperCamelCase :      int      				=							self.mean_squared_error(snake_case_			,					np.mean(snake_case_       )       ) * 2
       for i in range(len(snake_case_       )       ):
           if len(x[:i]       ) < self.min_leaf_size:
               continue
           elif len(x[i:]       ) < self.min_leaf_size:
               continue
           else:
               UpperCamelCase :      Any      				=							self.mean_squared_error(x[:i]			,					np.mean(y[:i]       )       )
               UpperCamelCase :      int      				=							self.mean_squared_error(x[i:]			,					np.mean(y[i:]       )       )
               UpperCamelCase :      List[str]      				=							error_left + error_right
               if error < min_error:
                   UpperCamelCase :      List[Any]      				=							i
                   UpperCamelCase :      Tuple      				=							error

       if best_split != 0:
           UpperCamelCase :      List[str]      				=							x[:best_split]
           UpperCamelCase :      str      				=							y[:best_split]
           UpperCamelCase :      Union[str, Any]      				=							x[best_split:]
           UpperCamelCase :      Any      				=							y[best_split:]

           UpperCamelCase :      List[Any]      				=							x[best_split]
           UpperCamelCase :      int      				=							DecisionTree(
               depth=self.depth - 1			,					min_leaf_size=self.min_leaf_size       )
           UpperCamelCase :      Dict      				=							DecisionTree(
               depth=self.depth - 1			,					min_leaf_size=self.min_leaf_size       )
           self.left.train(snake_case_			,					snake_case_       )
           self.right.train(snake_case_			,					snake_case_       )
       else:
           UpperCamelCase :      Union[str, Any]      				=							np.mean(snake_case_       )

       return







   def 					a_							(							self			,					SCREAMING_SNAKE_CASE_       ):

       if self.prediction is not None:
           return self.prediction
       elif self.left or self.right is not None:
           if x >= self.decision_boundary:
               return self.right.predict(snake_case_       )
           else:
               return self.left.predict(snake_case_       )
       else:
           print("""Error: Decision tree not yet trained"""       )
           return None







class 							lowerCamelCase						:







   @staticmethod
   def 					a_							(							SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_       ):
       UpperCamelCase :      Optional[int]      				=							float(0       )
       for label in labels:
           squared_error_sum += (label - prediction) ** 2

       return float(squared_error_sum / labels.size       )







def     A_	(						):







    '''simple docstring'''
    UpperCamelCase :      Tuple      				=							np.arange(-1.0  ,1.0  ,0.005			)
    UpperCamelCase :      Union[str, Any]      				=							np.sin(lowerCamelCase_			)

    UpperCamelCase :      Optional[Any]      				=							DecisionTree(depth=1_0  ,min_leaf_size=1_0			)
    tree.train(lowerCamelCase_  ,lowerCamelCase_			)

    UpperCamelCase :      Optional[int]      				=							(np.random.rand(1_0			) * 2) - 1
    UpperCamelCase :      Optional[Any]      				=							np.array([tree.predict(lowerCamelCase_			) for x in test_cases]			)
    UpperCamelCase :      Optional[Any]      				=							np.mean((predictions - test_cases) ** 2			)

    print("""Test values: """ + str(lowerCamelCase_			)			)
    print("""Predictions: """ + str(lowerCamelCase_			)			)
    print("""Average error: """ + str(lowerCamelCase_			)			)


if __name__ == "__main__":
  main()
  import doctest

  doctest.testmod(name='''mean_squarred_error''', verbose=True)





'''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      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		):




       '''simple docstring'''

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


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size						)
       UpperCAmelCase_							:   Tuple   =      random_attention_mask([self.batch_size, self.seq_length]      ,		dtype=tf.floataa						)

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

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

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

       UpperCAmelCase_							:   int   =      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      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      TFXLMModel(config=snake_case_						)
       UpperCAmelCase_							:   int   =      {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       UpperCAmelCase_							:   Tuple   =      model(snake_case_						)

       UpperCAmelCase_							:   Any   =      [input_ids, input_mask]
       UpperCAmelCase_							:   Optional[Any]   =      model(snake_case_						)
       self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      TFXLMWithLMHeadModel(snake_case_						)

       UpperCAmelCase_							:   Union[str, Any]   =      {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       UpperCAmelCase_							:   Optional[Any]   =      model(snake_case_						)

       UpperCAmelCase_							:   Tuple   =      outputs

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


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      TFXLMForQuestionAnsweringSimple(snake_case_						)

       UpperCAmelCase_							:   Optional[int]   =      {'input_ids': input_ids, 'lengths': input_lengths}

       UpperCAmelCase_							:   Optional[int]   =      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 _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      TFXLMForSequenceClassification(snake_case_						)

       UpperCAmelCase_							:   Dict   =      {'input_ids': input_ids, 'lengths': input_lengths}

       UpperCAmelCase_							:   Tuple   =      model(snake_case_						)

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


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   int   =      self.num_labels
       UpperCAmelCase_							:   List[str]   =      TFXLMForTokenClassification(config=snake_case_						)
       UpperCAmelCase_							:   str   =      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
       UpperCAmelCase_							:   Optional[int]   =      model(snake_case_						)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.num_labels)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self.num_choices
       UpperCAmelCase_							:   Optional[Any]   =      TFXLMForMultipleChoice(config=snake_case_						)
       UpperCAmelCase_							:   Tuple   =      tf.tile(tf.expand_dims(snake_case_      ,		1						)      ,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:   Dict   =      tf.tile(tf.expand_dims(snake_case_      ,		1						)      ,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:   str   =      tf.tile(tf.expand_dims(snake_case_      ,		1						)      ,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:   Tuple   =      {
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       UpperCAmelCase_							:   int   =      model(snake_case_						)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_choices)						)






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			
       )							:   Optional[int]   =      config_and_inputs
       UpperCAmelCase_							:   Any   =      {
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'langs': token_type_ids,
           'lengths': input_lengths,
       }
       return config, inputs_dict







@require_tf
class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_						,	lowerCamelCase_						,	unittest.TestCase       ):







  '''simple docstring'''






  lowerCamelCase_		:List[Any]						    =		(
      (
          TFXLMModel,
          TFXLMWithLMHeadModel,
          TFXLMForSequenceClassification,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForTokenClassification,
          TFXLMForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  lowerCamelCase_		:str						    =		(
      (TFXLMWithLMHeadModel,) if is_tf_available() else ()
  )  # TODO (PVP): Check other models whether language generation is also applicable
  lowerCamelCase_		: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 {}
  )
  lowerCamelCase_		:List[Any]						    =		False
  lowerCamelCase_		:Dict						    =		False


  def _UpperCamelCase     ( 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 _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      TFXLMModelTester(self						)
       UpperCAmelCase_							:   int   =      ConfigTester(self      ,		config_class=snake_case_      ,		emb_dim=3_7						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_token_classification(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case_						)






  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_							:   int   =      TFXLMModel.from_pretrained(snake_case_						)
            self.assertIsNotNone(snake_case_						)







@require_tf
class      __SCREAMING_SNAKE_CASE						(       unittest.TestCase       ):







  '''simple docstring'''






  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'						)
       UpperCAmelCase_							:   List[str]   =      tf.convert_to_tensor([[1_4, 4_4_7]]      ,		dtype=tf.intaa						)  # the president
       UpperCAmelCase_							:   Optional[int]   =      [
           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
       UpperCAmelCase_							:   Optional[int]   =      model.generate(snake_case_      ,		do_sample=snake_case_						)
       self.assertListEqual(output_ids[0].numpy().tolist()      ,		snake_case_						)






