

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard






class 			__magic_name__					(    unittest.TestCase   ):





			def 	__magic_name__					(	self      )     ->				Dict:




						'''simple docstring'''
						__a						   ={
						    'model_details': {
						        'Organization': 'testing',
						        'Model date': 'today',
						        'Model version': 'v2.1, Developed by Test Corp in 2019.',
						        'Architecture': 'Convolutional Neural Network.',
						    },
						    'metrics': 'BLEU and ROUGE-1',
						    'evaluation_data': {
						        'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
						        'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
						    },
						    'training_data': {
						        'Dataset': 'English Wikipedia dump dated 2018-12-01',
						        'Preprocessing': (
						            'Using SentencePiece vocabulary of size 52k tokens. See details on'
						            ' https://arxiv.org/pdf/1810.03993.pdf'
						        ),
						    },
						    'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 76},
						}





			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						__a						   =ModelCard.from_dict(self.inputs_dict      )
						self.assertTrue(hasattr(__snake_case      ,						'model_details'      )      )
						self.assertTrue(hasattr(__snake_case      ,						'intended_use'      )      )
						self.assertTrue(hasattr(__snake_case      ,						'factors'      )      )
						self.assertTrue(hasattr(__snake_case      ,						'metrics'      )      )
						self.assertTrue(hasattr(__snake_case      ,						'evaluation_data'      )      )
						self.assertTrue(hasattr(__snake_case      ,						'training_data'      )      )
						self.assertTrue(hasattr(__snake_case      ,						'quantitative_analyses'      )      )
						self.assertTrue(hasattr(__snake_case      ,						'ethical_considerations'      )      )
						self.assertTrue(hasattr(__snake_case      ,						'caveats_and_recommendations'      )      )





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =ModelCard.from_dict(self.inputs_dict      )
						__a						   =json.loads(modelcard.to_json_string()      )
						for key, value in self.inputs_dict.items():
									self.assertEqual(obj[key]      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				Dict:




						'''simple docstring'''
						__a						   =ModelCard.from_dict(self.inputs_dict      )

						with tempfile.TemporaryDirectory() as tmpdirname:
									__a						   =os.path.join(__snake_case      ,						'modelcard.json'      )
									model_card_first.to_json_file(__snake_case      )
									__a						   =ModelCard.from_json_file(__snake_case      )

						self.assertEqual(model_card_second.to_dict()      ,						model_card_first.to_dict()      )





			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a						   =ModelCard.from_dict(self.inputs_dict      )

						with tempfile.TemporaryDirectory() as tmpdirname:
									model_card_first.save_pretrained(__snake_case      )
									__a						   =ModelCard.from_pretrained(__snake_case      )

						self.assertEqual(model_card_second.to_dict()      ,						model_card_first.to_dict()      )









from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCAmelCase      :    List[Any]						       =    [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

_lowerCAmelCase      :    List[str]						       =    get_logger(__name__)


_lowerCAmelCase      :    Tuple						       =    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def  UpperCamelCase_(							_snake_case     :						dict			):




			"""simple docstring"""






			__a						   =task_template_dict.get('task'			)
			if task_name is None:
						logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_snake_case			)}'			)
						return None
			__a						   =NAMEaTEMPLATE.get(_snake_case			)
			return template.from_dict(_snake_case			)






