


"""simple docstring"""




import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


__magic_name__						: Tuple    =				'sshleifer/bart-tiny-random'
__magic_name__						: Tuple    =				'patrickvonplaten/t5-tiny-random'




@require_torch
class 			__snake_case   (unittest.TestCase  ):




							@cached_property
							def   __a   (     self:    int   ):
										return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase     ,*__lowerCamelCase          =      create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_     ,							tempfile.mkdtemp()     ,							e=1     ,							d=1   )
										self.assertEqual(student.config.num_hidden_layers     ,							1   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase     ,*__lowerCamelCase          =      create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_     ,							tempfile.mkdtemp()     ,							e=1     ,							d=SCREAMING_SNAKE_CASE_   )




							def   __a   (     self:    Dict   ):
										__lowerCamelCase     ,*__lowerCamelCase          =      create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_     ,							tempfile.mkdtemp()     ,							e=1     ,							d=SCREAMING_SNAKE_CASE_   )
										self.assertEqual(student.config.encoder_layers     ,							1   )
										self.assertEqual(student.config.decoder_layers     ,							self.teacher_config.encoder_layers   )




							def   __a   (     self:    List[str]   ):
										__lowerCamelCase     ,*__lowerCamelCase          =      create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_     ,							tempfile.mkdtemp()     ,							e=1     ,							d=1   )
										self.assertEqual(student.config.encoder_layers     ,							1   )
										self.assertEqual(student.config.decoder_layers     ,							1   )




							def   __a   (     self:    Tuple   ):
										with self.assertRaises(SCREAMING_SNAKE_CASE_   ):
													create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_     ,							tempfile.mkdtemp()     ,							e=SCREAMING_SNAKE_CASE_     ,							d=SCREAMING_SNAKE_CASE_   )






import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__a         =   namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 							a		(				snake_case__:   Dict		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				return (abs(source - target   ) / target) < 0.0_1




@pytest.mark.integration
def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   _TestCommandArgs(dataset=snake_case__		,  all_configs=snake_case__		,  save_infos=snake_case__   )
				lowercase_	    =   TestCommand(*snake_case__   )
				test_command.run()
				lowercase_	    =   os.path.join(snake_case__		,  '''README.md'''   )
				assert os.path.exists(snake_case__   )
				lowercase_	    =   DatasetInfosDict.from_directory(snake_case__   )
				lowercase_	    =   DatasetInfosDict(
				    {
				        '''default''': DatasetInfo(
				            features=Features(
				                {
				                    '''tokens''': Sequence(Value('''string'''   )   ),
				                    '''ner_tags''': Sequence(
				                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']   )   ),
				                    '''langs''': Sequence(Value('''string'''   )   ),
				                    '''spans''': Sequence(Value('''string'''   )   ),
				                }   )		,  splits=[
				                {
				                    '''name''': '''train''',
				                    '''num_bytes''': 2_351_563,
				                    '''num_examples''': 10_000,
				                },
				                {
				                    '''name''': '''validation''',
				                    '''num_bytes''': 238_418,
				                    '''num_examples''': 1_000,
				                },
				            ]		,  download_size=3_940_680		,  dataset_size=2_589_981		,  )
				    }   )
				assert dataset_infos.keys() == expected_dataset_infos.keys()
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								lowercase_    ,      lowercase_	    =   getattr(dataset_infos['''default''']		,  snake_case__   ), getattr(expected_dataset_infos['''default''']		,  snake_case__   )
								if key == "num_bytes":
												assert is_apercent_close(snake_case__		,  snake_case__   )
								elif key == "splits":
												assert list(snake_case__   ) == list(snake_case__   )
												for split in result:
																assert result[split].name == expected[split].name
																assert result[split].num_examples == expected[split].num_examples
																assert is_apercent_close(result[split].num_bytes		,  expected[split].num_bytes   )
								else:
												result == expected




