





import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard





class 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							{
                "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": 7_6},
            }




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		str  			=							ModelCard.from_dict(self.inputs_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "model_details"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "intended_use"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "factors"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "metrics"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "evaluation_data"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "training_data"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "quantitative_analyses"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "ethical_considerations"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "caveats_and_recommendations"							)							)




       def 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							ModelCard.from_dict(self.inputs_dict							)
            _SCREAMING_SNAKE_CASE				:		int  			=							json.loads(modelcard.to_json_string()							)
            for key, value in self.inputs_dict.items():
                 self.assertEqual(obj[key]			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							ModelCard.from_dict(self.inputs_dict							)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							os.path.join(__lowerCamelCase			,  "modelcard.json"							)
                 model_card_first.to_json_file(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							ModelCard.from_json_file(__lowerCamelCase							)

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







       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							ModelCard.from_dict(self.inputs_dict							)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model_card_first.save_pretrained(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							ModelCard.from_pretrained(__lowerCamelCase							)

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





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     list:
     a      =      []
     a      =      1

     for i in range(len(__UpperCamelCase)):
          if i + 1 < len(__UpperCamelCase) and text[i] == text[i + 1]:
               count += 1
          else:
               encoded.append((text[i], count))
               a      =      1

     return encoded

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     str:
     return "".join(char * length for char, length in encoded)


if __name__ == "__main__":
   from doctest import testmod

   testmod(name="run_length_encode", verbose=True)
   testmod(name="run_length_decode", verbose=True)

