





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()							)






# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

            # we want this test to run even if there is only one GPU, but if there are more we use them all
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							get_gpu_count()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
            _SCREAMING_SNAKE_CASE				:		int  			=							[sys.executable] + distributed_args + args
            execute_subprocess_async(__lowerCamelCase			,  env=self.get_env()							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)