

import numpy as np







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase=5							,  lowerCamelCase=5   )						->       Any:
        snake_case_       						=      depth
        snake_case_       						=      0
        snake_case_       						=      None
        snake_case_       						=      None
        snake_case_       						=      min_leaf_size
        snake_case_       						=      None







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       str:
        if labels.ndim != 1:
          print("""Error: Input labels must be one dimensional"""   )

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       int:
        if x.ndim != 1:
          print("""Error: Input data set must be one dimensional"""   )
          return
        if len(lowerCamelCase   ) != len(lowerCamelCase   ):
          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(lowerCamelCase   ) < 2 * self.min_leaf_size:
          snake_case_       						=      np.mean(lowerCamelCase   )
          return

        if self.depth == 1:
          snake_case_       						=      np.mean(lowerCamelCase   )
          return

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

        if best_split != 0:
          snake_case_       						=      x[:best_split]
          snake_case_       						=      y[:best_split]
          snake_case_       						=      x[best_split:]
          snake_case_       						=      y[best_split:]

          snake_case_       						=      x[best_split]
          snake_case_       						=      DecisionTree(
              depth=self.depth - 1							,  min_leaf_size=self.min_leaf_size   )
          snake_case_       						=      DecisionTree(
              depth=self.depth - 1							,  min_leaf_size=self.min_leaf_size   )
          self.left.train(lowerCamelCase							,  lowerCamelCase   )
          self.right.train(lowerCamelCase							,  lowerCamelCase   )
        else:
          snake_case_       						=      np.mean(lowerCamelCase   )

        return







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase   )						->       Optional[int]:
        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(lowerCamelCase   )
          else:
            return self.left.predict(lowerCamelCase   )
        else:
          print("""Error: Decision tree not yet trained"""   )
          return None







class  __lowerCamelCase				:







      @staticmethod
      def 	lowerCAmelCase_    (     lowerCamelCase							,  lowerCamelCase   )						->       str:
        snake_case_       						=      float(0   )
        for label in labels:
          squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size   )



def    UpperCamelCase(					)	->				int:





  '''simple docstring'''





  snake_case_       						=      np.arange(-1.0			,					1.0			,					0.0_05							)
  snake_case_       						=      np.sin(lowercase_							)

  snake_case_       						=      DecisionTree(depth=10			,					min_leaf_size=10							)
  tree.train(lowercase_			,					lowercase_							)

  snake_case_       						=      (np.random.rand(10							) * 2) - 1
  snake_case_       						=      np.array([tree.predict(lowercase_							) for x in test_cases]							)
  snake_case_       						=      np.mean((predictions - test_cases) ** 2							)

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


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

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


import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class  __lowerCamelCase				(	unittest.TestCase	):
      lowerCamelCase_	:      Tuple          =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      lowerCamelCase_	:      int          =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Any:
        if isinstance(model.config							,  lowerCamelCase   ):
          snake_case_						,				snake_case_       						=      list(tokenizer.lang_code_to_id.keys()   )[:2]
          snake_case_       						=      TranslationPipeline(model=lowerCamelCase							,  tokenizer=lowerCamelCase							,  src_lang=lowerCamelCase							,  tgt_lang=lowerCamelCase   )
        else:
          snake_case_       						=      TranslationPipeline(model=lowerCamelCase							,  tokenizer=lowerCamelCase   )
        return translator, ["Some string", "Some other text"]







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       List[str]:
        snake_case_       						=      translator("""Some string"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}]   )

        snake_case_       						=      translator(["""Some string"""]   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}]   )

        snake_case_       						=      translator(["""Some string""", """other string"""]   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}, {"""translation_text""": ANY(lowerCamelCase   )}]   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                        """ Beide Beide"""
                    )
                }
            ]							,  )







      @require_tf
      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                        """ Beide Beide"""
                    )
                }
            ]							,  )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                        """ urine urine urine urine urine urine urine"""
                    )
                }
            ]							,  )







      @require_tf
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                        """ urine urine urine urine urine urine urine"""
                    )
                }
            ]							,  )







class  __lowerCamelCase				(	unittest.TestCase	):







      @require_torch
      @slow
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        # We don't provide a default for this pair
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation_cn_to_ar"""   )

        # but we do for this one
        snake_case_       						=      pipeline(task="""translation_en_to_de"""   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """en"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """de"""   )







      @require_torch
      @slow
      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""   )
        snake_case_       						=      MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""   )

        snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase							,  tokenizer=lowerCamelCase   )
        # Missing src_lang, tgt_lang
        with self.assertRaises(lowerCamelCase   ):
          translator("""This is a test"""   )

        snake_case_       						=      translator("""This is a test"""							,  src_lang="""en_XX"""							,  tgt_lang="""ar_AR"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """هذا إختبار"""}]   )

        snake_case_       						=      translator("""This is a test"""							,  src_lang="""en_XX"""							,  tgt_lang="""hi_IN"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """यह एक परीक्षण है"""}]   )

        # src_lang, tgt_lang can be defined at pipeline call time
        snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase							,  tokenizer=lowerCamelCase							,  src_lang="""en_XX"""							,  tgt_lang="""ar_AR"""   )
        snake_case_       						=      translator("""This is a test"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """هذا إختبار"""}]   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      """patrickvonplaten/t5-tiny-random"""
        snake_case_       						=      pipeline(task="""translation_cn_to_ar"""							,  model=lowerCamelCase   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """cn"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """ar"""   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      """patrickvonplaten/t5-tiny-random"""
        with pytest.warns(lowerCamelCase							,  match=r""".*translation_en_to_de.*"""   ):
          snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase   )
        self.assertEqual(translator.task							,  """translation_en_to_de"""   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """en"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """de"""   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       Any:
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation"""   )