
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)

__SCREAMING_SNAKE_CASE			:   Tuple         =     logging.getLogger()
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Tuple  	=     argparse.ArgumentParser()
					parser.add_argument('''-f'''  )
					lowerCAmelCase		:					Any  	=     parser.parse_args()
					return args.f


class 		__magic_name__   (						snake_case    ):





						def       _A							(							self    :	List[Any]				):
											lowerCAmelCase		:					List[str]  	=     logging.StreamHandler(sys.stdout				)
											logger.addHandler(lowerCamelCase__				)

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

											with patch.object(lowerCamelCase__						,  '''argv'''						,  lowerCamelCase__				):
																lowerCAmelCase		:					List[str]  	=     run_glue_with_pabee.main()
																for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__						,  0.7_5				)


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 		__magic_name__   (						unittest.TestCase    ):





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					List[Any]  	=     {
							    '''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''': 5_5.1, '''ROUGE-1''': 7_6},
							}





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Dict  	=     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    :	Optional[Any]				):
							lowerCAmelCase		:					Tuple  	=     ModelCard.from_dict(self.inputs_dict				)
							lowerCAmelCase		:					Dict  	=     json.loads(modelcard.to_json_string()				)
							for key, value in self.inputs_dict.items():
												self.assertEqual(obj[key]						,  lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					Tuple  	=     ModelCard.from_dict(self.inputs_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												lowerCAmelCase		:					str  	=     os.path.join(lowerCamelCase__						,  '''modelcard.json'''				)
												model_card_first.to_json_file(lowerCamelCase__				)
												lowerCAmelCase		:					Dict  	=     ModelCard.from_json_file(lowerCamelCase__				)

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





		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase		:					int  	=     ModelCard.from_dict(self.inputs_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model_card_first.save_pretrained(lowerCamelCase__				)
												lowerCAmelCase		:					Any  	=     ModelCard.from_pretrained(lowerCamelCase__				)

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