


'''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:		Union[str, Any]    						=  {
    "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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[int] )		->       List[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    task_cls()
   UpperCamelCase__			    =    asdict(_UpperCamelCase )
   UpperCamelCase__			    =    task_template_from_dict(_UpperCamelCase )
   assert task == reloaded







class 			UpperCAmelCase			:

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



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







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    sorted(["pos", "neg"]       )

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Tuple       ):





    """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(a_       )
    self.assertEqual("text-classification",       task.task       )
    self.assertEqual(a_,       task.input_schema       )
    self.assertEqual(a_,       task.label_schema       )

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



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







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						str       ):





    """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 		lowercase_    (			self			:						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(a_       )
    self.assertEqual("question-answering-extractive",       task.task       )
    self.assertEqual(a_,       task.input_schema       )
    self.assertEqual(a_,       task.label_schema       )







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Tuple       ):





    """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(a_       )
    self.assertEqual("summarization",       task.task       )
    self.assertEqual(a_,       task.input_schema       )
    self.assertEqual(a_,       task.label_schema       )







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



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







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    UpperCamelCase__			    =    sorted(["pos", "neg"]       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



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







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    sorted(["pos", "neg"]       )

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



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







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



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

    def keep_task(a_			:						Optional[Any]       ):
       return x

    def dont_keep_task(a_			:						Dict       ):
       UpperCamelCase__			    =    deepcopy(a_       )
       UpperCamelCase__			    =    0
       return out

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

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

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



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

    def process(a_			:						Union[str, Any]       ):
       return example

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







'''simple docstring'''





from typing import TYPE_CHECKING

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


__A				:				Union[str, Any]					=		{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""GPT2TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[int]					=		[
     """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPT2DoubleHeadsModel""",
     """GPT2ForQuestionAnswering""",
     """GPT2ForSequenceClassification""",
     """GPT2ForTokenClassification""",
     """GPT2LMHeadModel""",
     """GPT2Model""",
     """GPT2PreTrainedModel""",
     """load_tf_weights_in_gpt2""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[Any]					=		[
     """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFGPT2DoubleHeadsModel""",
     """TFGPT2ForSequenceClassification""",
     """TFGPT2LMHeadModel""",
     """TFGPT2MainLayer""",
     """TFGPT2Model""",
     """TFGPT2PreTrainedModel""",
 ]

try:
 if not is_keras_nlp_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Any					=		["""TFGPT2Tokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
 from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
 from .tokenization_gpta import GPTaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_fast import GPTaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpta import (
      GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTaDoubleHeadsModel,
      GPTaForQuestionAnswering,
      GPTaForSequenceClassification,
      GPTaForTokenClassification,
      GPTaLMHeadModel,
      GPTaModel,
      GPTaPreTrainedModel,
      load_tf_weights_in_gpta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gpta import (
      TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGPTaDoubleHeadsModel,
      TFGPTaForSequenceClassification,
      TFGPTaLMHeadModel,
      TFGPTaMainLayer,
      TFGPTaModel,
      TFGPTaPreTrainedModel,
  )

 try:
  if not is_keras_nlp_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_tf import TFGPTaTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
 import sys

 __A				:				Tuple					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

