"""simple docstring"""


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


__UpperCamelCase       :    List[str]         =		{
    "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": [5_1_5]},
}


@pytest.mark.parametrize(
    "task_cls",				[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],				)
def   __UpperCAmelCase     (	_snake_case		:       Optional[Any]       ):
   _lowercase							     =       task_cls()
   _lowercase							     =       asdict(_snake_case       )
   _lowercase							     =       task_template_from_dict(_snake_case       )
   assert task == reloaded
class   UpperCAmelCase_	:
    def 		_lowerCamelCase      (	self			:       int		)						->    Tuple:
       _lowercase							     =       LanguageModeling(text_column="input_text"		)
       assert {"input_text": "text"} == task.column_mapping




    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    List[str]:
       _lowercase							     =       Features({"text": Value("string"		)}		)
       _lowercase							     =       {"text_column": "input_text"}
       _lowercase							     =       LanguageModeling.from_dict(_lowercase		)
       assert "language-modeling" == task.task
       assert input_schema == task.input_schema
class   UpperCAmelCase_	(				lowercase__   ):
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    int:
       _lowercase							     =       sorted(["pos", "neg"]		)
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Tuple:
       _lowercase							     =       TextClassification(text_column="input_text"   ,						label_column="input_label"		)
       self.assertDictEqual({"input_text": "text", "input_label": "labels"}   ,						task.column_mapping		)
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Union[str, Any]:
       _lowercase							     =       Features({"text": Value("string"		)}		)
       # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
       _lowercase							     =       Features({"labels": ClassLabel}		)
       _lowercase							     =       {"text_column": "input_text", "label_column": "input_labels"}
       _lowercase							     =       TextClassification.from_dict(_lowercase		)
       self.assertEqual("text-classification"   ,						task.task		)
       self.assertEqual(_lowercase   ,						task.input_schema		)
       self.assertEqual(_lowercase   ,						task.label_schema		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    Tuple:
       _lowercase							     =       TextClassification(text_column="input_text"   ,						label_column="input_label"		)
       self.assertEqual(task.label_schema["labels"]   ,						_lowercase		)
       _lowercase							     =       task.align_with_features(Features({"input_label": ClassLabel(names=self.labels		)}		)		)
       self.assertEqual(task.label_schema["labels"]   ,						ClassLabel(names=self.labels		)		)
class   UpperCAmelCase_	(				lowercase__   ):
    def 		_lowerCamelCase      (	self			:       Any		)						->    Union[str, Any]:
       _lowercase							     =       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 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    List[Any]:
       _lowercase							     =       Features({"question": Value("string"		), "context": Value("string"		)}		)
       _lowercase							     =       Features(
           {
               "answers": Sequence(
                   {
                       "text": Value("string"		),
                       "answer_start": Value("int32"		),
                   }		)
           }		)
       _lowercase							     =       {
           "context_column": "input_input_context",
           "question_column": "input_question",
           "answers_column": "input_answers",
       }
       _lowercase							     =       QuestionAnsweringExtractive.from_dict(_lowercase		)
       self.assertEqual("question-answering-extractive"   ,						task.task		)
       self.assertEqual(_lowercase   ,						task.input_schema		)
       self.assertEqual(_lowercase   ,						task.label_schema		)
class   UpperCAmelCase_	(				lowercase__   ):
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    int:
       _lowercase							     =       Summarization(text_column="input_text"   ,						summary_column="input_summary"		)
       self.assertDictEqual({"input_text": "text", "input_summary": "summary"}   ,						task.column_mapping		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    Optional[int]:
       _lowercase							     =       Features({"text": Value("string"		)}		)
       _lowercase							     =       Features({"summary": Value("string"		)}		)
       _lowercase							     =       {"text_column": "input_text", "summary_column": "input_summary"}
       _lowercase							     =       Summarization.from_dict(_lowercase		)
       self.assertEqual("summarization"   ,						task.task		)
       self.assertEqual(_lowercase   ,						task.input_schema		)
       self.assertEqual(_lowercase   ,						task.label_schema		)
class   UpperCAmelCase_	(				lowercase__   ):
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Optional[int]:
       _lowercase							     =       AutomaticSpeechRecognition(audio_column="input_audio"   ,						transcription_column="input_transcription"		)
       self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}   ,						task.column_mapping		)




    def 		_lowerCamelCase      (	self			:       str		)						->    Optional[int]:
       _lowercase							     =       Features({"audio": Audio()}		)
       _lowercase							     =       Features({"transcription": Value("string"		)}		)
       _lowercase							     =       {
           "audio_column": "input_audio",
           "transcription_column": "input_transcription",
       }
       _lowercase							     =       AutomaticSpeechRecognition.from_dict(_lowercase		)
       self.assertEqual("automatic-speech-recognition"   ,						task.task		)
       self.assertEqual(_lowercase   ,						task.input_schema		)
       self.assertEqual(_lowercase   ,						task.label_schema		)
class   UpperCAmelCase_	(				lowercase__   ):
    def 		_lowerCamelCase      (	self			:       Dict		)						->    str:
       _lowercase							     =       sorted(["pos", "neg"]		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    Optional[int]:
       _lowercase							     =       AudioClassification(audio_column="input_audio"   ,						label_column="input_label"		)
       self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}   ,						task.column_mapping		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    int:
       _lowercase							     =       Features({"audio": Audio()}		)
       _lowercase							     =       Features({"labels": ClassLabel}		)
       _lowercase							     =       {
           "audio_column": "input_image",
           "label_column": "input_label",
       }
       _lowercase							     =       AudioClassification.from_dict(_lowercase		)
       self.assertEqual("audio-classification"   ,						task.task		)
       self.assertEqual(_lowercase   ,						task.input_schema		)
       self.assertEqual(_lowercase   ,						task.label_schema		)




    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       AudioClassification(audio_column="input_audio"   ,						label_column="input_label"		)
       self.assertEqual(task.label_schema["labels"]   ,						_lowercase		)
       _lowercase							     =       task.align_with_features(Features({"input_label": ClassLabel(names=self.labels		)}		)		)
       self.assertEqual(task.label_schema["labels"]   ,						ClassLabel(names=self.labels		)		)
class   UpperCAmelCase_	(				lowercase__   ):
    def 		_lowerCamelCase      (	self			:       int		)						->    List[str]:
       _lowercase							     =       sorted(["pos", "neg"]		)
    def 		_lowerCamelCase      (	self			:       Dict		)						->    List[str]:
       _lowercase							     =       ImageClassification(image_column="input_image"   ,						label_column="input_label"		)
       self.assertDictEqual({"input_image": "image", "input_label": "labels"}   ,						task.column_mapping		)
    def 		_lowerCamelCase      (	self			:       Any		)						->    str:
       _lowercase							     =       Features({"image": Image()}		)
       _lowercase							     =       Features({"labels": ClassLabel}		)
       _lowercase							     =       {
           "image_column": "input_image",
           "label_column": "input_label",
       }
       _lowercase							     =       ImageClassification.from_dict(_lowercase		)
       self.assertEqual("image-classification"   ,						task.task		)
       self.assertEqual(_lowercase   ,						task.input_schema		)
       self.assertEqual(_lowercase   ,						task.label_schema		)




    def 		_lowerCamelCase      (	self			:       Dict		)						->    Tuple:
       _lowercase							     =       ImageClassification(image_column="input_image"   ,						label_column="input_label"		)
       self.assertEqual(task.label_schema["labels"]   ,						_lowercase		)
       _lowercase							     =       task.align_with_features(Features({"input_label": ClassLabel(names=self.labels		)}		)		)
       self.assertEqual(task.label_schema["labels"]   ,						ClassLabel(names=self.labels		)		)
class   UpperCAmelCase_	(				lowercase__   ):
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    str:
       _lowercase							     =       DatasetInfo(task_templates=QuestionAnsweringExtractive()		)
       _lowercase							     =       Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}   ,						info=_lowercase		)
       assert isinstance(dataset.info.task_templates   ,						_lowercase		)
       assert len(dataset.info.task_templates		) == 1

       def keep_task(_lowercase			:       Dict		):
          return x

       def dont_keep_task(_lowercase			:       int		):
          _lowercase							     =       deepcopy(_lowercase		)
          _lowercase							     =       0
          return out

       _lowercase							     =       dataset.map(_lowercase		)
       assert mapped_dataset.info.task_templates == dataset.info.task_templates
       # reload from cache
       _lowercase							     =       dataset.map(_lowercase		)
       assert mapped_dataset.info.task_templates == dataset.info.task_templates

       _lowercase							     =       dataset.map(_lowercase		)
       assert mapped_dataset.info.task_templates == []
       # reload from cache
       _lowercase							     =       dataset.map(_lowercase		)
       assert mapped_dataset.info.task_templates == []




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

       def process(_lowercase			:       str		):
          return example

       _lowercase							     =       dataset.remove_columns("label"		)
       _lowercase							     =       modified_dataset.map(_lowercase		)
       assert mapped_dataset.info.task_templates == []
"""simple docstring"""


from .number_theory.prime_numbers import next_prime
class   UpperCAmelCase_	:
    def __init__(	self			:       str   ,						_lowercase			:       int   ,						_lowercase			:       int | None = None   ,						_lowercase			:       float | None = None   ,						)						->    None:
       _lowercase							     =       size_table
       _lowercase							     =       [None] * self.size_table
       _lowercase							     =       0.75 if lim_charge is None else lim_charge
       _lowercase							     =       1 if charge_factor is None else charge_factor
       _lowercase							     =       []
       _lowercase							     =       {}
    def 		_lowerCamelCase      (	self			:       Any		)						->    Optional[Any]:
       return self._keys
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Dict:
       return sum(1 for slot in self.values if slot is not None		) / (
           self.size_table * self.charge_factor
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[str]		)						->    str:
       return key % self.size_table
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Union[str, Any]		)						->    Optional[Any]:
       print(f"""step {step_ord}"""		)
       print(list(range(len(self.values		)		)		)		)
       print(self.values		)
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[Any]		)						->    List[Any]:
       _lowercase							     =       1
       _lowercase							     =       values
       for value in values:
          self.insert_data(_lowercase		)
          self._step_by_step(_lowercase		)
          i += 1
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Union[str, Any]		)						->    List[Any]:
       _lowercase							     =       data
       _lowercase							     =       data
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple=None		)						->    Any:
       _lowercase							     =       self.hash_function(key + 1		)

       while self.values[new_key] is not None and self.values[new_key] != key:
          if self.values.count(_lowercase		) > 0:
             _lowercase							     =       self.hash_function(new_key + 1		)
          else:
             _lowercase							     =       None
             break

       return new_key
    def 		_lowerCamelCase      (	self			:       str		)						->    Any:
       _lowercase							     =       [value for value in self.values if value is not None]
       _lowercase							     =       next_prime(self.size_table   ,						factor=2		)
       self._keys.clear()
       _lowercase							     =       [None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
          self.insert_data(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       self.hash_function(_lowercase		)

       if self.values[key] is None:
          self._set_value(_lowercase   ,						_lowercase		)

       elif self.values[key] == data:
          pass

       else:
          _lowercase							     =       self._collision_resolution(_lowercase   ,						_lowercase		)
          if collision_resolution is not None:
             self._set_value(_lowercase   ,						_lowercase		)
          else:
             self.rehashing()
             self.insert_data(_lowercase		)