
'''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


A_      =							{
    "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_15]},
}





@pytest.mark.parametrize(
    "task_cls" ,    [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ] ,    )
def 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:Any   =   task_cls()
  SCREAMING_SNAKE_CASE:str   =   asdict(lowercase__	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   task_template_from_dict(lowercase__	)
  assert task == reloaded





class 							_snake_case	:
   def       __UpperCamelCase  (	self     : Optional[int]							):
     SCREAMING_SNAKE_CASE:List[Any]   =   LanguageModeling(text_column="input_text"							)
     assert {"input_text": "text"} == task.column_mapping







   def       __UpperCamelCase  (	self     : Optional[int]							):
     SCREAMING_SNAKE_CASE:List[Any]   =   Features({"text": Value("string"							)}							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   {"text_column": "input_text"}
     SCREAMING_SNAKE_CASE:Optional[Any]   =   LanguageModeling.from_dict(SCREAMING_SNAKE_CASE__							)
     assert "language-modeling" == task.task
     assert input_schema == task.input_schema





class 							_snake_case	(    lowerCamelCase_  ):
   def       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   sorted(["pos", "neg"]							)
   def       __UpperCamelCase  (	self     : str							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   TextClassification(text_column="input_text"       ,label_column="input_label"							)
     self.assertDictEqual({"input_text": "text", "input_label": "labels"}       ,task.column_mapping							)
   def       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:Any   =   Features({"text": Value("string"							)}							)
     # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
     SCREAMING_SNAKE_CASE:Dict   =   Features({"labels": ClassLabel}							)
     SCREAMING_SNAKE_CASE:str   =   {"text_column": "input_text", "label_column": "input_labels"}
     SCREAMING_SNAKE_CASE:Optional[int]   =   TextClassification.from_dict(SCREAMING_SNAKE_CASE__							)
     self.assertEqual("text-classification"       ,task.task							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.input_schema							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.label_schema							)







   def       __UpperCamelCase  (	self     : Optional[int]							):
     SCREAMING_SNAKE_CASE:str   =   TextClassification(text_column="input_text"       ,label_column="input_label"							)
     self.assertEqual(task.label_schema["labels"]       ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   task.align_with_features(Features({"input_label": ClassLabel(names=self.labels							)}							)							)
     self.assertEqual(task.label_schema["labels"]       ,ClassLabel(names=self.labels							)							)





class 							_snake_case	(    lowerCamelCase_  ):
   def       __UpperCamelCase  (	self     : Optional[Any]							):
     SCREAMING_SNAKE_CASE:str   =   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       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:int   =   Features({"question": Value("string"							), "context": Value("string"							)}							)
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   Features(
         {
             "answers": Sequence(
                 {
                     "text": Value("string"							),
                     "answer_start": Value("int32"							),
                 }							)
         }							)
     SCREAMING_SNAKE_CASE:List[Any]   =   {
         "context_column": "input_input_context",
         "question_column": "input_question",
         "answers_column": "input_answers",
     }
     SCREAMING_SNAKE_CASE:Dict   =   QuestionAnsweringExtractive.from_dict(SCREAMING_SNAKE_CASE__							)
     self.assertEqual("question-answering-extractive"       ,task.task							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.input_schema							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.label_schema							)





class 							_snake_case	(    lowerCamelCase_  ):
   def       __UpperCamelCase  (	self     : Optional[int]							):
     SCREAMING_SNAKE_CASE:Dict   =   Summarization(text_column="input_text"       ,summary_column="input_summary"							)
     self.assertDictEqual({"input_text": "text", "input_summary": "summary"}       ,task.column_mapping							)







   def       __UpperCamelCase  (	self     : List[str]							):
     SCREAMING_SNAKE_CASE:List[Any]   =   Features({"text": Value("string"							)}							)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   Features({"summary": Value("string"							)}							)
     SCREAMING_SNAKE_CASE:str   =   {"text_column": "input_text", "summary_column": "input_summary"}
     SCREAMING_SNAKE_CASE:int   =   Summarization.from_dict(SCREAMING_SNAKE_CASE__							)
     self.assertEqual("summarization"       ,task.task							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.input_schema							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.label_schema							)





class 							_snake_case	(    lowerCamelCase_  ):
   def       __UpperCamelCase  (	self     : Tuple							):
     SCREAMING_SNAKE_CASE:List[Any]   =   AutomaticSpeechRecognition(audio_column="input_audio"       ,transcription_column="input_transcription"							)
     self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}       ,task.column_mapping							)







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





class 							_snake_case	(    lowerCamelCase_  ):
   def       __UpperCamelCase  (	self     : Optional[Any]							):
     SCREAMING_SNAKE_CASE:str   =   sorted(["pos", "neg"]							)
   def       __UpperCamelCase  (	self     : Optional[int]							):
     SCREAMING_SNAKE_CASE:int   =   AudioClassification(audio_column="input_audio"       ,label_column="input_label"							)
     self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}       ,task.column_mapping							)
   def       __UpperCamelCase  (	self     : Dict							):
     SCREAMING_SNAKE_CASE:Any   =   Features({"audio": Audio()}							)
     SCREAMING_SNAKE_CASE:int   =   Features({"labels": ClassLabel}							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   {
         "audio_column": "input_image",
         "label_column": "input_label",
     }
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   AudioClassification.from_dict(SCREAMING_SNAKE_CASE__							)
     self.assertEqual("audio-classification"       ,task.task							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.input_schema							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.label_schema							)







   def       __UpperCamelCase  (	self     : Optional[Any]							):
     SCREAMING_SNAKE_CASE:Any   =   AudioClassification(audio_column="input_audio"       ,label_column="input_label"							)
     self.assertEqual(task.label_schema["labels"]       ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int   =   task.align_with_features(Features({"input_label": ClassLabel(names=self.labels							)}							)							)
     self.assertEqual(task.label_schema["labels"]       ,ClassLabel(names=self.labels							)							)





class 							_snake_case	(    lowerCamelCase_  ):
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:List[str]   =   sorted(["pos", "neg"]							)
   def       __UpperCamelCase  (	self     : Tuple							):
     SCREAMING_SNAKE_CASE:Any   =   ImageClassification(image_column="input_image"       ,label_column="input_label"							)
     self.assertDictEqual({"input_image": "image", "input_label": "labels"}       ,task.column_mapping							)
   def       __UpperCamelCase  (	self     : int							):
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   Features({"image": Image()}							)
     SCREAMING_SNAKE_CASE:str   =   Features({"labels": ClassLabel}							)
     SCREAMING_SNAKE_CASE:Dict   =   {
         "image_column": "input_image",
         "label_column": "input_label",
     }
     SCREAMING_SNAKE_CASE:int   =   ImageClassification.from_dict(SCREAMING_SNAKE_CASE__							)
     self.assertEqual("image-classification"       ,task.task							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.input_schema							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,task.label_schema							)







   def       __UpperCamelCase  (	self     : Tuple							):
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   ImageClassification(image_column="input_image"       ,label_column="input_label"							)
     self.assertEqual(task.label_schema["labels"]       ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict   =   task.align_with_features(Features({"input_label": ClassLabel(names=self.labels							)}							)							)
     self.assertEqual(task.label_schema["labels"]       ,ClassLabel(names=self.labels							)							)





class 							_snake_case	(    lowerCamelCase_  ):
   def       __UpperCamelCase  (	self     : Tuple							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   DatasetInfo(task_templates=QuestionAnsweringExtractive()							)
     SCREAMING_SNAKE_CASE:str   =   Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}       ,info=SCREAMING_SNAKE_CASE__							)
     assert isinstance(dataset.info.task_templates       ,SCREAMING_SNAKE_CASE__							)
     assert len(dataset.info.task_templates							) == 1

     def keep_task(SCREAMING_SNAKE_CASE__     : Dict							):
       return x

     def dont_keep_task(SCREAMING_SNAKE_CASE__     : Tuple							):
       SCREAMING_SNAKE_CASE:int   =   deepcopy(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Any   =   0
       return out

     SCREAMING_SNAKE_CASE:Dict   =   dataset.map(SCREAMING_SNAKE_CASE__							)
     assert mapped_dataset.info.task_templates == dataset.info.task_templates
     # reload from cache
     SCREAMING_SNAKE_CASE:List[str]   =   dataset.map(SCREAMING_SNAKE_CASE__							)
     assert mapped_dataset.info.task_templates == dataset.info.task_templates

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







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

     def process(SCREAMING_SNAKE_CASE__     : Optional[Any]							):
       return example

     SCREAMING_SNAKE_CASE:List[Any]   =   dataset.remove_columns("label"							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   modified_dataset.map(SCREAMING_SNAKE_CASE__							)
     assert mapped_dataset.info.task_templates == []









'''simple docstring'''

def 				A_					(	snake_case	):
  return "".join(chr(ord(snake_case	) + 32	) if "A" <= char <= "Z" else char for char in word	)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






