



'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

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







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               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(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

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

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

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

        return







 def _snake_case					(				self    ,a_				)						->  List[Any]:
        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(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					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)








from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


__SCREAMING_SNAKE_CASE     =	{
    """id""": """5733be284776f41900661182""",
    """title""": """University_of_Notre_Dame""",
    """context""": """Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.""",
    """question""": """To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?""",
    """answers""": {"""text""": ["""Saint Bernadette Soubirous"""], """answer_start""": [515]},
}




@pytest.mark.parametrize(
    "task_cls"			,     [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,     )
def 						UpperCAmelCase (    _lowerCamelCase     ):
			A				:       Optional[int]														=    task_cls()
			A				:       int														=    asdict(_lowerCamelCase     )
			A				:       int														=    task_template_from_dict(_lowerCamelCase     )
			assert task == reloaded

class 						lowerCamelCase_     :






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       str														=    LanguageModeling(text_column="input_text"			)
									assert {"input_text": "text"} == task.column_mapping







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					str:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									A				:       Dict														=    {"text_column": "input_text"}
									A				:       Optional[int]														=    LanguageModeling.from_dict(__lowerCamelCase			)
									assert "language-modeling" == task.task
									assert input_schema == task.input_schema







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       List[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Optional[int]:
									A				:       List[Any]														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertDictEqual({"input_text": "text", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					Optional[int]:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[Any]														=    {"text_column": "input_text", "label_column": "input_labels"}
									A				:       Any														=    TextClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("text-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       Dict														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Any														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Dict:
									A				:       Optional[int]														=    QuestionAnsweringExtractive(
									    context_column="input_context"						,						question_column="input_question"						,						answers_column="input_answers"			)
									self.assertDictEqual(
									    {"input_context": "context", "input_question": "question", "input_answers": "answers"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Tuple:
									A				:       Dict														=    Features({"question": Value("string"			), "context": Value("string"			)}			)
									A				:       int														=    Features(
									    {
									        "answers": Sequence(
									            {
									                "text": Value("string"			),
									                "answer_start": Value("int32"			),
									            }			)
									    }			)
									A				:       Optional[int]														=    {
									    "context_column": "input_input_context",
									    "question_column": "input_question",
									    "answers_column": "input_answers",
									}
									A				:       Tuple														=    QuestionAnsweringExtractive.from_dict(__lowerCamelCase			)
									self.assertEqual("question-answering-extractive"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    Summarization(text_column="input_text"						,						summary_column="input_summary"			)
									self.assertDictEqual({"input_text": "text", "input_summary": "summary"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Any:
									A				:       Union[str, Any]														=    Features({"text": Value("string"			)}			)
									A				:       Tuple														=    Features({"summary": Value("string"			)}			)
									A				:       Optional[Any]														=    {"text_column": "input_text", "summary_column": "input_summary"}
									A				:       str														=    Summarization.from_dict(__lowerCamelCase			)
									self.assertEqual("summarization"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       Union[str, Any]														=    AutomaticSpeechRecognition(audio_column="input_audio"						,						transcription_column="input_transcription"			)
									self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       Tuple														=    Features({"audio": Audio()}			)
									A				:       Any														=    Features({"transcription": Value("string"			)}			)
									A				:       List[str]														=    {
									    "audio_column": "input_audio",
									    "transcription_column": "input_transcription",
									}
									A				:       Dict														=    AutomaticSpeechRecognition.from_dict(__lowerCamelCase			)
									self.assertEqual("automatic-speech-recognition"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Union[str, Any]:
									A				:       Optional[int]														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									A				:       Union[str, Any]														=    Features({"audio": Audio()}			)
									A				:       Dict														=    Features({"labels": ClassLabel}			)
									A				:       Tuple														=    {
									    "audio_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Any														=    AudioClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("audio-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Any:
									A				:       str														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       List[str]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									A				:       int														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       List[Any]														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertDictEqual({"input_image": "image", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					int:
									A				:       Optional[int]														=    Features({"image": Image()}			)
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[str]														=    {
									    "image_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Tuple														=    ImageClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("image-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Tuple:
									A				:       str														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Optional[Any]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Optional[int]:
									A				:       Dict														=    DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
									A				:       List[Any]														=    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}						,						info=__lowerCamelCase			)
									assert isinstance(dataset.info.task_templates						,						__lowerCamelCase			)
									assert len(dataset.info.task_templates			) == 1

									def keep_task(__lowerCamelCase   :      List[str]			):
												return x

									def dont_keep_task(__lowerCamelCase   :      Any			):
												A				:       Optional[Any]														=    deepcopy(__lowerCamelCase			)
												A				:       List[Any]														=    0
												return out

									A				:       Optional[int]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates
									# reload from cache
									A				:       List[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates

									A				:       Union[str, Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []
									# reload from cache
									A				:       Optional[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Tuple:
									A				:       Optional[Any]														=    Features({"text": Value("string"			), "label": ClassLabel(names=("pos", "neg")			)}			)
									A				:       Optional[Any]														=    TextClassification(text_column="text"						,						label_column="label"			)
									A				:       Optional[Any]														=    DatasetInfo(features=__lowerCamelCase						,						task_templates=__lowerCamelCase			)
									A				:       Optional[int]														=    Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}						,						info=__lowerCamelCase			)

									def process(__lowerCamelCase   :      Dict			):
												return example

									A				:       List[Any]														=    dataset.remove_columns("label"			)
									A				:       Dict														=    modified_dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []