




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


_lowerCamelCase							=      {
    "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 							lowerCamelCase    (							UpperCAmelCase_	:      Optional[Any]      )->			Any:

       """simple docstring"""




       a			   =task_cls()
       a			   =asdict(_snake_case      )
       a			   =task_template_from_dict(_snake_case      )
       assert task == reloaded


class        UpperCAmelCase__				:



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =LanguageModeling(text_column="""input_text"""	)
            assert {"input_text": "text"} == task.column_mapping






     def        lowerCAmelCase__  (				self	):
            a			   =Features({"""text""": Value("""string"""	)}	)
            a			   ={"""text_column""": """input_text"""}
            a			   =LanguageModeling.from_dict(_lowercase	)
            assert "language-modeling" == task.task
            assert input_schema == task.input_schema



class        UpperCAmelCase__				(							lowercase__				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =sorted(["""pos""", """neg"""]	)
     def        lowerCAmelCase__  (				self	):
            a			   =TextClassification(text_column="""input_text""" ,    label_column="""input_label"""	)
            self.assertDictEqual({"""input_text""": """text""", """input_label""": """labels"""} ,    task.column_mapping	)
     def        lowerCAmelCase__  (				self	):
            a			   =Features({"""text""": Value("""string"""	)}	)
            # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
            a			   =Features({"""labels""": ClassLabel}	)
            a			   ={"""text_column""": """input_text""", """label_column""": """input_labels"""}
            a			   =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	):
            a			   =TextClassification(text_column="""input_text""" ,    label_column="""input_label"""	)
            self.assertEqual(task.label_schema["""labels"""] ,    _lowercase	)
            a			   =task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
            self.assertEqual(task.label_schema["""labels"""] ,    ClassLabel(names=self.labels	)	)



class        UpperCAmelCase__				(							lowercase__				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =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	):
            a			   =Features({"""question""": Value("""string"""	), """context""": Value("""string"""	)}	)
            a			   =Features(
                {
                    """answers""": Sequence(
                        {
                            """text""": Value("""string"""	),
                            """answer_start""": Value("""int32"""	),
                        }	)
                }	)
            a			   ={
                """context_column""": """input_input_context""",
                """question_column""": """input_question""",
                """answers_column""": """input_answers""",
            }
            a			   =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__				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =Summarization(text_column="""input_text""" ,    summary_column="""input_summary"""	)
            self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""} ,    task.column_mapping	)






     def        lowerCAmelCase__  (				self	):
            a			   =Features({"""text""": Value("""string"""	)}	)
            a			   =Features({"""summary""": Value("""string"""	)}	)
            a			   ={"""text_column""": """input_text""", """summary_column""": """input_summary"""}
            a			   =Summarization.from_dict(_lowercase	)
            self.assertEqual("""summarization""" ,    task.task	)
            self.assertEqual(_lowercase ,    task.input_schema	)
            self.assertEqual(_lowercase ,    task.label_schema	)



class        UpperCAmelCase__				(							lowercase__				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =AutomaticSpeechRecognition(audio_column="""input_audio""" ,    transcription_column="""input_transcription"""	)
            self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""} ,    task.column_mapping	)






     def        lowerCAmelCase__  (				self	):
            a			   =Features({"""audio""": Audio()}	)
            a			   =Features({"""transcription""": Value("""string"""	)}	)
            a			   ={
                """audio_column""": """input_audio""",
                """transcription_column""": """input_transcription""",
            }
            a			   =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__				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =sorted(["""pos""", """neg"""]	)
     def        lowerCAmelCase__  (				self	):
            a			   =AudioClassification(audio_column="""input_audio""" ,    label_column="""input_label"""	)
            self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""} ,    task.column_mapping	)
     def        lowerCAmelCase__  (				self	):
            a			   =Features({"""audio""": Audio()}	)
            a			   =Features({"""labels""": ClassLabel}	)
            a			   ={
                """audio_column""": """input_image""",
                """label_column""": """input_label""",
            }
            a			   =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	):
            a			   =AudioClassification(audio_column="""input_audio""" ,    label_column="""input_label"""	)
            self.assertEqual(task.label_schema["""labels"""] ,    _lowercase	)
            a			   =task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
            self.assertEqual(task.label_schema["""labels"""] ,    ClassLabel(names=self.labels	)	)



class        UpperCAmelCase__				(							lowercase__				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =sorted(["""pos""", """neg"""]	)
     def        lowerCAmelCase__  (				self	):
            a			   =ImageClassification(image_column="""input_image""" ,    label_column="""input_label"""	)
            self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""} ,    task.column_mapping	)
     def        lowerCAmelCase__  (				self	):
            a			   =Features({"""image""": Image()}	)
            a			   =Features({"""labels""": ClassLabel}	)
            a			   ={
                """image_column""": """input_image""",
                """label_column""": """input_label""",
            }
            a			   =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	):
            a			   =ImageClassification(image_column="""input_image""" ,    label_column="""input_label"""	)
            self.assertEqual(task.label_schema["""labels"""] ,    _lowercase	)
            a			   =task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
            self.assertEqual(task.label_schema["""labels"""] ,    ClassLabel(names=self.labels	)	)



class        UpperCAmelCase__				(							lowercase__				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =DatasetInfo(task_templates=QuestionAnsweringExtractive()	)
            a			   =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(_lowerCAmelCase	):
                   return x

            def dont_keep_task(_lowerCAmelCase	):
                   a			   =deepcopy(_lowercase	)
                   a			   =0
                   return out

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

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






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

            def process(_lowerCAmelCase	):
                   return example

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

"""simple docstring"""


__UpperCamelCase       :    List[Any]         =		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

__UpperCamelCase       :    Any         =		[{"type": "code", "content": INSTALL_CONTENT}]