




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__						=		{
    '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    snake_case   (				A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						Any				  =				task_cls()
	lowerCamelCase_				:						int				  =				asdict(A     )
	lowerCamelCase_				:						List[str]				  =				task_template_from_dict(A     )
	assert task == reloaded


class 							__SCREAMING_SNAKE_CASE				:

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				LanguageModeling(text_column='input_text'   )
						assert {"input_text": "text"} == task.column_mapping





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				Features({'text': Value('string'   )}   )
						lowerCamelCase_				:						List[str]				  =				{'text_column': 'input_text'}
						lowerCamelCase_				:						Dict				  =				LanguageModeling.from_dict(__lowercase   )
						assert "language-modeling" == task.task
						assert input_schema == task.input_schema


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[Any]				  =				sorted(['pos', 'neg']   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				TextClassification(text_column='input_text'					,			label_column='input_label'   )
						self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}					,			task.column_mapping   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




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





					def       lowercase_				(							self   ):







						'''simple docstring'''




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


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				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       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				Features({'question': Value('string'   ), 'context': Value('string'   )}   )
						lowerCamelCase_				:						List[Any]				  =				Features(
						    {
						        'answers': Sequence(
						            {
						                'text': Value('string'   ),
						                'answer_start': Value('int32'   ),
						            }   )
						    }   )
						lowerCamelCase_				:						Optional[Any]				  =				{
						    'context_column': 'input_input_context',
						    'question_column': 'input_question',
						    'answers_column': 'input_answers',
						}
						lowerCamelCase_				:						Dict				  =				QuestionAnsweringExtractive.from_dict(__lowercase   )
						self.assertEqual('question-answering-extractive'					,			task.task   )
						self.assertEqual(__lowercase					,			task.input_schema   )
						self.assertEqual(__lowercase					,			task.label_schema   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




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





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				Features({'text': Value('string'   )}   )
						lowerCamelCase_				:						List[Any]				  =				Features({'summary': Value('string'   )}   )
						lowerCamelCase_				:						Optional[Any]				  =				{'text_column': 'input_text', 'summary_column': 'input_summary'}
						lowerCamelCase_				:						List[Any]				  =				Summarization.from_dict(__lowercase   )
						self.assertEqual('summarization'					,			task.task   )
						self.assertEqual(__lowercase					,			task.input_schema   )
						self.assertEqual(__lowercase					,			task.label_schema   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




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





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				Features({'audio': Audio()}   )
						lowerCamelCase_				:						Any				  =				Features({'transcription': Value('string'   )}   )
						lowerCamelCase_				:						int				  =				{
						    'audio_column': 'input_audio',
						    'transcription_column': 'input_transcription',
						}
						lowerCamelCase_				:						Optional[int]				  =				AutomaticSpeechRecognition.from_dict(__lowercase   )
						self.assertEqual('automatic-speech-recognition'					,			task.task   )
						self.assertEqual(__lowercase					,			task.input_schema   )
						self.assertEqual(__lowercase					,			task.label_schema   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				sorted(['pos', 'neg']   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[Any]				  =				AudioClassification(audio_column='input_audio'					,			label_column='input_label'   )
						self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}					,			task.column_mapping   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				Features({'audio': Audio()}   )
						lowerCamelCase_				:						Tuple				  =				Features({'labels': ClassLabel}   )
						lowerCamelCase_				:						List[Any]				  =				{
						    'audio_column': 'input_image',
						    'label_column': 'input_label',
						}
						lowerCamelCase_				:						List[str]				  =				AudioClassification.from_dict(__lowercase   )
						self.assertEqual('audio-classification'					,			task.task   )
						self.assertEqual(__lowercase					,			task.input_schema   )
						self.assertEqual(__lowercase					,			task.label_schema   )





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				AudioClassification(audio_column='input_audio'					,			label_column='input_label'   )
						self.assertEqual(task.label_schema['labels']					,			__lowercase   )
						lowerCamelCase_				:						List[str]				  =				task.align_with_features(Features({'input_label': ClassLabel(names=self.labels   )}   )   )
						self.assertEqual(task.label_schema['labels']					,			ClassLabel(names=self.labels   )   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				sorted(['pos', 'neg']   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				Features({'image': Image()}   )
						lowerCamelCase_				:						Tuple				  =				Features({'labels': ClassLabel}   )
						lowerCamelCase_				:						str				  =				{
						    'image_column': 'input_image',
						    'label_column': 'input_label',
						}
						lowerCamelCase_				:						Tuple				  =				ImageClassification.from_dict(__lowercase   )
						self.assertEqual('image-classification'					,			task.task   )
						self.assertEqual(__lowercase					,			task.input_schema   )
						self.assertEqual(__lowercase					,			task.label_schema   )





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				ImageClassification(image_column='input_image'					,			label_column='input_label'   )
						self.assertEqual(task.label_schema['labels']					,			__lowercase   )
						lowerCamelCase_				:						str				  =				task.align_with_features(Features({'input_label': ClassLabel(names=self.labels   )}   )   )
						self.assertEqual(task.label_schema['labels']					,			ClassLabel(names=self.labels   )   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				DatasetInfo(task_templates=QuestionAnsweringExtractive()   )
						lowerCamelCase_				:						Tuple				  =				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   ):
							return x

						def dont_keep_task(__lowercase   ):
							lowerCamelCase_				:						Union[str, Any]				  =				deepcopy(__lowercase   )
							lowerCamelCase_				:						List[Any]				  =				0
							return out

						lowerCamelCase_				:						List[Any]				  =				dataset.map(__lowercase   )
						assert mapped_dataset.info.task_templates == dataset.info.task_templates
						# reload from cache
						lowerCamelCase_				:						Union[str, Any]				  =				dataset.map(__lowercase   )
						assert mapped_dataset.info.task_templates == dataset.info.task_templates

						lowerCamelCase_				:						Tuple				  =				dataset.map(__lowercase   )
						assert mapped_dataset.info.task_templates == []
						# reload from cache
						lowerCamelCase_				:						str				  =				dataset.map(__lowercase   )
						assert mapped_dataset.info.task_templates == []





					def       lowercase_				(							self   ):







						'''simple docstring'''




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

						def process(__lowercase   ):
							return example

						lowerCamelCase_				:						List[str]				  =				dataset.remove_columns('label'   )
						lowerCamelCase_				:						Optional[int]				  =				modified_dataset.map(__lowercase   )
						assert mapped_dataset.info.task_templates == []

"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Dict=					[
	    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTJForCausalLM",
	    "GPTJForQuestionAnswering",
	    "GPTJForSequenceClassification",
	    "GPTJModel",
	    "GPTJPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "FlaxGPTJForCausalLM",
	    "FlaxGPTJModel",
	    "FlaxGPTJPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	_a	:	Any=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





