





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

lowercase_			       =				logging.getLogger()





def lowerCamelCase		(  )		->Dict:
    _SCREAMING_SNAKE_CASE             =    argparse.ArgumentParser()
    parser.add_argument("""-f"""					)
    _SCREAMING_SNAKE_CASE             =    parser.parse_args()
    return args.f






class       a_  (       snake_case_ ):

    '''simple docstring'''


    def     snake_case_(	self )	->     str:
        _SCREAMING_SNAKE_CASE             =    logging.StreamHandler(sys.stdout )
        logger.addHandler(A )

        _SCREAMING_SNAKE_CASE             =    self.get_auto_remove_tmp_dir()
        _SCREAMING_SNAKE_CASE             =    f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

        with patch.object(A				,	"""argv"""				,	A ):
            _SCREAMING_SNAKE_CASE             =    run_glue_with_pabee.main()
            for value in result.values():
                self.assertGreaterEqual(A				,	0.75 )










'''simple docstring'''

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class       a_  (       unittest.TestCase ):

    '''simple docstring'''


    def     snake_case_(	self )	->     int:
        _SCREAMING_SNAKE_CASE             =    {
            """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     snake_case_(	self )	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
        self.assertTrue(hasattr(A				,	"""model_details""" ) )
        self.assertTrue(hasattr(A				,	"""intended_use""" ) )
        self.assertTrue(hasattr(A				,	"""factors""" ) )
        self.assertTrue(hasattr(A				,	"""metrics""" ) )
        self.assertTrue(hasattr(A				,	"""evaluation_data""" ) )
        self.assertTrue(hasattr(A				,	"""training_data""" ) )
        self.assertTrue(hasattr(A				,	"""quantitative_analyses""" ) )
        self.assertTrue(hasattr(A				,	"""ethical_considerations""" ) )
        self.assertTrue(hasattr(A				,	"""caveats_and_recommendations""" ) )


    def     snake_case_(	self )	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
        _SCREAMING_SNAKE_CASE             =    json.loads(modelcard.to_json_string() )
        for key, value in self.inputs_dict.items():
            self.assertEqual(obj[key]				,	A )


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

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

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





    def     snake_case_(	self )	->     List[Any]:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )

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

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



