"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class 							__snake_case(					unittest.TestCase				,	__a	):


    '''simple docstring'''





    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[Any]								=		load_tool('document-question-answering'	)
         self.tool.setup()
         __A					:					Optional[int]								=		load_tool('document-question-answering'					,       remote=UpperCamelCase__	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		load_dataset('hf-internal-testing/example-documents'					,       split='test'	)
         __A					:					List[str]								=		dataset[0]['image']

         __A					:					Optional[int]								=		self.tool(UpperCamelCase__					,       'When is the coffee break?'	)
         self.assertEqual(UpperCamelCase__					,       '11-14 to 11:39 a.m.'	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		load_dataset('hf-internal-testing/example-documents'					,       split='test'	)
         __A					:					List[Any]								=		dataset[0]['image']

         __A					:					Optional[Any]								=		self.remote_tool(UpperCamelCase__					,       'When is the coffee break?'	)
         self.assertEqual(UpperCamelCase__					,       '11-14 to 11:39 a.m.'	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Any								=		load_dataset('hf-internal-testing/example-documents'					,       split='test'	)
         __A					:					str								=		dataset[0]['image']

         self.tool(document=UpperCamelCase__					,       question='When is the coffee break?'	)

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[Any]								=		load_dataset('hf-internal-testing/example-documents'					,       split='test'	)
         __A					:					Tuple								=		dataset[0]['image']

         __A					:					Optional[int]								=		self.remote_tool(document=UpperCamelCase__					,       question='When is the coffee break?'	)
         self.assertEqual(UpperCamelCase__					,       '11-14 to 11:39 a.m.'	)




"""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


__lowercase    =				{
    '''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							:    List[str]			):
							"""simple docstring"""


							__UpperCamelCase							=task_cls()
							__UpperCamelCase							=asdict(__UpperCamelCase			)
							__UpperCamelCase							=task_template_from_dict(__UpperCamelCase			)
							assert task == reloaded






class 		_lowercase :



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=LanguageModeling(text_column='''input_text'''				)
													assert {"input_text": "text"} == task.column_mapping





						def   UpperCAmelCase_ (				self							:    str				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''text''': Value('''string'''				)}				)
													__UpperCamelCase							={'''text_column''': '''input_text'''}
													__UpperCamelCase							=LanguageModeling.from_dict(UpperCamelCase__				)
													assert "language-modeling" == task.task
													assert input_schema == task.input_schema






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=sorted(['''pos''', '''neg''']				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=TextClassification(text_column='''input_text'''     , label_column='''input_label'''				)
													self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''text''': Value('''string'''				)}				)
													# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
													__UpperCamelCase							=Features({'''labels''': ClassLabel}				)
													__UpperCamelCase							={'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
													__UpperCamelCase							=TextClassification.from_dict(UpperCamelCase__				)
													self.assertEqual('''text-classification'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=TextClassification(text_column='''input_text'''     , label_column='''input_label'''				)
													self.assertEqual(task.label_schema['''labels''']     , UpperCamelCase__				)
													__UpperCamelCase							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
													self.assertEqual(task.label_schema['''labels''']     , ClassLabel(names=self.labels				)				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Dict				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=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							:    Optional[Any]				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''question''': Value('''string'''				), '''context''': Value('''string'''				)}				)
													__UpperCamelCase							=Features(
													    {
													        '''answers''': Sequence(
													            {
													                '''text''': Value('''string'''				),
													                '''answer_start''': Value('''int32'''				),
													            }				)
													    }				)
													__UpperCamelCase							={
													    '''context_column''': '''input_input_context''',
													    '''question_column''': '''input_question''',
													    '''answers_column''': '''input_answers''',
													}
													__UpperCamelCase							=QuestionAnsweringExtractive.from_dict(UpperCamelCase__				)
													self.assertEqual('''question-answering-extractive'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=Summarization(text_column='''input_text'''     , summary_column='''input_summary'''				)
													self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    int				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''text''': Value('''string'''				)}				)
													__UpperCamelCase							=Features({'''summary''': Value('''string'''				)}				)
													__UpperCamelCase							={'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
													__UpperCamelCase							=Summarization.from_dict(UpperCamelCase__				)
													self.assertEqual('''summarization'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=AutomaticSpeechRecognition(audio_column='''input_audio'''     , transcription_column='''input_transcription'''				)
													self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''audio''': Audio()}				)
													__UpperCamelCase							=Features({'''transcription''': Value('''string'''				)}				)
													__UpperCamelCase							={
													    '''audio_column''': '''input_audio''',
													    '''transcription_column''': '''input_transcription''',
													}
													__UpperCamelCase							=AutomaticSpeechRecognition.from_dict(UpperCamelCase__				)
													self.assertEqual('''automatic-speech-recognition'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=sorted(['''pos''', '''neg''']				)





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=AudioClassification(audio_column='''input_audio'''     , label_column='''input_label'''				)
													self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    Dict				)				->				Any:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''audio''': Audio()}				)
													__UpperCamelCase							=Features({'''labels''': ClassLabel}				)
													__UpperCamelCase							={
													    '''audio_column''': '''input_image''',
													    '''label_column''': '''input_label''',
													}
													__UpperCamelCase							=AudioClassification.from_dict(UpperCamelCase__				)
													self.assertEqual('''audio-classification'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=AudioClassification(audio_column='''input_audio'''     , label_column='''input_label'''				)
													self.assertEqual(task.label_schema['''labels''']     , UpperCamelCase__				)
													__UpperCamelCase							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
													self.assertEqual(task.label_schema['''labels''']     , ClassLabel(names=self.labels				)				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=sorted(['''pos''', '''neg''']				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=ImageClassification(image_column='''input_image'''     , label_column='''input_label'''				)
													self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''image''': Image()}				)
													__UpperCamelCase							=Features({'''labels''': ClassLabel}				)
													__UpperCamelCase							={
													    '''image_column''': '''input_image''',
													    '''label_column''': '''input_label''',
													}
													__UpperCamelCase							=ImageClassification.from_dict(UpperCamelCase__				)
													self.assertEqual('''image-classification'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=ImageClassification(image_column='''input_image'''     , label_column='''input_label'''				)
													self.assertEqual(task.label_schema['''labels''']     , UpperCamelCase__				)
													__UpperCamelCase							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
													self.assertEqual(task.label_schema['''labels''']     , ClassLabel(names=self.labels				)				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=DatasetInfo(task_templates=QuestionAnsweringExtractive()				)
													__UpperCamelCase							=Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}     , info=UpperCamelCase__				)
													assert isinstance(dataset.info.task_templates     , UpperCamelCase__				)
													assert len(dataset.info.task_templates				) == 1

													def keep_task(UpperCamelCase__							:    Optional[int]				):
																				return x

													def dont_keep_task(UpperCamelCase__							:    Optional[int]				):
																				__UpperCamelCase							=deepcopy(UpperCamelCase__				)
																				__UpperCamelCase							=0
																				return out

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

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





						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''text''': Value('''string'''				), '''label''': ClassLabel(names=('''pos''', '''neg''')				)}				)
													__UpperCamelCase							=TextClassification(text_column='''text'''     , label_column='''label'''				)
													__UpperCamelCase							=DatasetInfo(features=UpperCamelCase__     , task_templates=UpperCamelCase__				)
													__UpperCamelCase							=Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}     , info=UpperCamelCase__				)

													def process(UpperCamelCase__							:    Any				):
																				return example

													__UpperCamelCase							=dataset.remove_columns('''label'''				)
													__UpperCamelCase							=modified_dataset.map(UpperCamelCase__				)
													assert mapped_dataset.info.task_templates == []



