





import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard



class  _a  (					unittest.TestCase				):






							"""simple docstring"""

							def   A_	(   self					:  Union[str, Any]     )						->Any:
													SCREAMING_SNAKE_CASE__  :					int =       {
													    "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   A_	(   self					:  str     )						->int:
													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   A_	(   self					:  Any     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       ModelCard.from_dict(self.inputs_dict     )
													SCREAMING_SNAKE_CASE__  :					Any =       json.loads(modelcard.to_json_string()     )
													for key, value in self.inputs_dict.items():
																			self.assertEqual(obj[key]				,						_lowerCAmelCase     )

							def   A_	(   self					:  Union[str, Any]     )						->Any:
													SCREAMING_SNAKE_CASE__  :					Dict =       ModelCard.from_dict(self.inputs_dict     )

													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       os.path.join(_lowerCAmelCase				,						"modelcard.json"     )
																			model_card_first.to_json_file(_lowerCAmelCase     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       ModelCard.from_json_file(_lowerCAmelCase     )

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






							def   A_	(   self					:  Union[str, Any]     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					str =       ModelCard.from_dict(self.inputs_dict     )

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model_card_first.save_pretrained(_lowerCAmelCase     )
																			SCREAMING_SNAKE_CASE__  :					str =       ModelCard.from_pretrained(_lowerCAmelCase     )

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





from unittest.mock import patch

import datasets
from datasets import Dataset



def    UpperCAmelCase__	(  ):
  lowercase     :Any   						=    Dataset.from_dict({"col_1": [3, 2, 0, 1]} )

  with patch("tqdm.auto.tqdm" ) as mock_tqdm:
    datasets.disable_progress_bar()
    dset.map(lambda lowerCamelCase : {"col_2": x["col_1"] + 1} )
    mock_tqdm.assert_not_called()

    mock_tqdm.reset_mock()

    datasets.enable_progress_bar()
    dset.map(lambda lowerCamelCase : {"col_2": x["col_1"] + 1} )
    mock_tqdm.assert_called()



