
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 		lowercase_     (  A__						)				->				List[str]:
    """simple docstring"""

    snake_case						=   task_cls()
    snake_case						=   asdict(A__						)
    snake_case						=   task_template_from_dict(A__						)
    assert task == reloaded







class 	lowerCamelCase						:





     def 						UpperCAmelCase(self :       List[Any]  )		->		Tuple:
         snake_case						=   LanguageModeling(text_column="input_text"  )
         assert {"input_text": "text"} == task.column_mapping





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Tuple:
         snake_case						=   Features({"text": Value("string"  )}  )
         snake_case						=   {"text_column": "input_text"}
         snake_case						=   LanguageModeling.from_dict(_A  )
         assert "language-modeling" == task.task
         assert input_schema == task.input_schema







class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Union[str, Any]  )		->		Union[str, Any]:
         snake_case						=   sorted(["pos", "neg"]  )





     def 						UpperCAmelCase(self :       Tuple  )		->		Dict:
         snake_case						=   TextClassification(text_column="input_text"							,       label_column="input_label"  )
         self.assertDictEqual({"input_text": "text", "input_label": "labels"}							,       task.column_mapping  )





     def 						UpperCAmelCase(self :       str  )		->		Union[str, Any]:
         snake_case						=   Features({"text": Value("string"  )}  )
         # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
         snake_case						=   Features({"labels": ClassLabel}  )
         snake_case						=   {"text_column": "input_text", "label_column": "input_labels"}
         snake_case						=   TextClassification.from_dict(_A  )
         self.assertEqual("text-classification"							,       task.task  )
         self.assertEqual(_A							,       task.input_schema  )
         self.assertEqual(_A							,       task.label_schema  )





     def 						UpperCAmelCase(self :       Dict  )		->		Dict:
         snake_case						=   TextClassification(text_column="input_text"							,       label_column="input_label"  )
         self.assertEqual(task.label_schema["labels"]							,       _A  )
         snake_case						=   task.align_with_features(Features({"input_label": ClassLabel(names=self.labels  )}  )  )
         self.assertEqual(task.label_schema["labels"]							,       ClassLabel(names=self.labels  )  )







class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Any  )		->		str:
         snake_case						=   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 :       Dict  )		->		int:
         snake_case						=   Features({"question": Value("string"  ), "context": Value("string"  )}  )
         snake_case						=   Features(
             {
                 "answers": Sequence(
                     {
                         "text": Value("string"  ),
                         "answer_start": Value("int32"  ),
                     }  )
             }  )
         snake_case						=   {
             "context_column": "input_input_context",
             "question_column": "input_question",
             "answers_column": "input_answers",
         }
         snake_case						=   QuestionAnsweringExtractive.from_dict(_A  )
         self.assertEqual("question-answering-extractive"							,       task.task  )
         self.assertEqual(_A							,       task.input_schema  )
         self.assertEqual(_A							,       task.label_schema  )







class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       str  )		->		str:
         snake_case						=   Summarization(text_column="input_text"							,       summary_column="input_summary"  )
         self.assertDictEqual({"input_text": "text", "input_summary": "summary"}							,       task.column_mapping  )





     def 						UpperCAmelCase(self :       List[Any]  )		->		Union[str, Any]:
         snake_case						=   Features({"text": Value("string"  )}  )
         snake_case						=   Features({"summary": Value("string"  )}  )
         snake_case						=   {"text_column": "input_text", "summary_column": "input_summary"}
         snake_case						=   Summarization.from_dict(_A  )
         self.assertEqual("summarization"							,       task.task  )
         self.assertEqual(_A							,       task.input_schema  )
         self.assertEqual(_A							,       task.label_schema  )







class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       str  )		->		Any:
         snake_case						=   AutomaticSpeechRecognition(audio_column="input_audio"							,       transcription_column="input_transcription"  )
         self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}							,       task.column_mapping  )





     def 						UpperCAmelCase(self :       Any  )		->		List[Any]:
         snake_case						=   Features({"audio": Audio()}  )
         snake_case						=   Features({"transcription": Value("string"  )}  )
         snake_case						=   {
             "audio_column": "input_audio",
             "transcription_column": "input_transcription",
         }
         snake_case						=   AutomaticSpeechRecognition.from_dict(_A  )
         self.assertEqual("automatic-speech-recognition"							,       task.task  )
         self.assertEqual(_A							,       task.input_schema  )
         self.assertEqual(_A							,       task.label_schema  )







class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Any  )		->		str:
         snake_case						=   sorted(["pos", "neg"]  )





     def 						UpperCAmelCase(self :       int  )		->		Any:
         snake_case						=   AudioClassification(audio_column="input_audio"							,       label_column="input_label"  )
         self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}							,       task.column_mapping  )





     def 						UpperCAmelCase(self :       Optional[int]  )		->		Any:
         snake_case						=   Features({"audio": Audio()}  )
         snake_case						=   Features({"labels": ClassLabel}  )
         snake_case						=   {
             "audio_column": "input_image",
             "label_column": "input_label",
         }
         snake_case						=   AudioClassification.from_dict(_A  )
         self.assertEqual("audio-classification"							,       task.task  )
         self.assertEqual(_A							,       task.input_schema  )
         self.assertEqual(_A							,       task.label_schema  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Dict:
         snake_case						=   AudioClassification(audio_column="input_audio"							,       label_column="input_label"  )
         self.assertEqual(task.label_schema["labels"]							,       _A  )
         snake_case						=   task.align_with_features(Features({"input_label": ClassLabel(names=self.labels  )}  )  )
         self.assertEqual(task.label_schema["labels"]							,       ClassLabel(names=self.labels  )  )







class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       int  )		->		Tuple:
         snake_case						=   sorted(["pos", "neg"]  )





     def 						UpperCAmelCase(self :       Any  )		->		Optional[Any]:
         snake_case						=   ImageClassification(image_column="input_image"							,       label_column="input_label"  )
         self.assertDictEqual({"input_image": "image", "input_label": "labels"}							,       task.column_mapping  )





     def 						UpperCAmelCase(self :       str  )		->		Dict:
         snake_case						=   Features({"image": Image()}  )
         snake_case						=   Features({"labels": ClassLabel}  )
         snake_case						=   {
             "image_column": "input_image",
             "label_column": "input_label",
         }
         snake_case						=   ImageClassification.from_dict(_A  )
         self.assertEqual("image-classification"							,       task.task  )
         self.assertEqual(_A							,       task.input_schema  )
         self.assertEqual(_A							,       task.label_schema  )





     def 						UpperCAmelCase(self :       Optional[int]  )		->		int:
         snake_case						=   ImageClassification(image_column="input_image"							,       label_column="input_label"  )
         self.assertEqual(task.label_schema["labels"]							,       _A  )
         snake_case						=   task.align_with_features(Features({"input_label": ClassLabel(names=self.labels  )}  )  )
         self.assertEqual(task.label_schema["labels"]							,       ClassLabel(names=self.labels  )  )







class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       List[Any]  )		->		int:
         snake_case						=   DatasetInfo(task_templates=QuestionAnsweringExtractive()  )
         snake_case						=   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 :       Dict  ):
             return x

         def dont_keep_task(_A :       Any  ):
             snake_case						=   deepcopy(_A  )
             snake_case						=   0
             return out

         snake_case						=   dataset.map(_A  )
         assert mapped_dataset.info.task_templates == dataset.info.task_templates
         # reload from cache
         snake_case						=   dataset.map(_A  )
         assert mapped_dataset.info.task_templates == dataset.info.task_templates

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





     def 						UpperCAmelCase(self :       List[Any]  )		->		Optional[Any]:
         snake_case						=   Features({"text": Value("string"  ), "label": ClassLabel(names=("pos", "neg")  )}  )
         snake_case						=   TextClassification(text_column="text"							,       label_column="label"  )
         snake_case						=   DatasetInfo(features=_A							,       task_templates=_A  )
         snake_case						=   Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}							,       info=_A  )

         def process(_A :       List[str]  ):
             return example

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



from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       str							,       _A :       Tuple							,       _A :       Any  )		->		Optional[int]:
         return True





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"  )
         snake_case						=   "Hugging Face大法好，谁用谁知道。"
         snake_case						=   "Hugging Face大法好,谁用谁知道。<unk>"
         snake_case						=   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

         snake_case						=   tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokens + [tokenizer.unk_token]

         snake_case						=   [1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )							,       _A  )

         snake_case						=   tokenizer.decode(_A  )
         self.assertEqual(_A							,       _A  )

