
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a         =   logging.get_logger(__name__)

__a         =   {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Any       	=      'openai-gpt'
	a     :Optional[int]       	=      {
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}
	def __init__(			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple=4_0_4_7_8			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=5_1_2			,				SCREAMING_SNAKE_CASE_    :     List[str]=7_6_8			,				SCREAMING_SNAKE_CASE_    :     List[str]=1_2			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1_2			,				SCREAMING_SNAKE_CASE_    :     Any="gelu"			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     str=1e-5			,				SCREAMING_SNAKE_CASE_    :     int=0.02			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="cls_index"			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=None			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=True			,				SCREAMING_SNAKE_CASE_    :     List[str]=0.1			,				**SCREAMING_SNAKE_CASE_    :     Tuple			,				)			->	Any:
					lowercase_	    =   vocab_size
					lowercase_	    =   n_positions
					lowercase_	    =   n_embd
					lowercase_	    =   n_layer
					lowercase_	    =   n_head
					lowercase_	    =   afn
					lowercase_	    =   resid_pdrop
					lowercase_	    =   embd_pdrop
					lowercase_	    =   attn_pdrop
					lowercase_	    =   layer_norm_epsilon
					lowercase_	    =   initializer_range
					lowercase_	    =   summary_type
					lowercase_	    =   summary_use_proj
					lowercase_	    =   summary_activation
					lowercase_	    =   summary_first_dropout
					lowercase_	    =   summary_proj_to_labels
					super().__init__(**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




