
"""simple docstring"""




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


UpperCAmelCase__   :					Optional[int]												=				logging.get_logger(__name__)

UpperCAmelCase__   :					int												=				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[int]       =     '''megatron-bert'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=2_90_56     , SCREAMING_SNAKE_CASE__=10_24     , SCREAMING_SNAKE_CASE__=24     , SCREAMING_SNAKE_CASE__=16     , SCREAMING_SNAKE_CASE__=40_96     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=1E-12     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__="absolute"     , SCREAMING_SNAKE_CASE__=True     , **SCREAMING_SNAKE_CASE__     , )      ->	Any:



											"""simple docstring"""




											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     hidden_size
											SCREAMING_SNAKE_CASE__		: int     =     num_hidden_layers
											SCREAMING_SNAKE_CASE__		: List[Any]     =     num_attention_heads
											SCREAMING_SNAKE_CASE__		: int     =     hidden_act
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     intermediate_size
											SCREAMING_SNAKE_CASE__		: Tuple     =     hidden_dropout_prob
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__		: int     =     max_position_embeddings
											SCREAMING_SNAKE_CASE__		: Tuple     =     type_vocab_size
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     initializer_range
											SCREAMING_SNAKE_CASE__		: Dict     =     layer_norm_eps
											SCREAMING_SNAKE_CASE__		: Dict     =     position_embedding_type
											SCREAMING_SNAKE_CASE__		: List[str]     =     use_cache






"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class 			lowerCAmelCase_		(datasets.BuilderConfig						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[datasets.Features]       =     None






class 			lowerCAmelCase_		(datasets.ArrowBasedBuilder						):


				"""simple docstring"""



				__UpperCamelCase   :		Union[str, Any]       =     PandasConfig



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											return datasets.DatasetInfo(features=self.config.features					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											if not self.config.data_files:
																		raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dl_manager.download_and_extract(self.config.data_files					)
											if isinstance(SCREAMING_SNAKE_CASE__     , (str, list, tuple)					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     data_files
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: str     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		return [datasets.SplitGenerator(name=datasets.Split.TRAIN     , gen_kwargs={"""files""": files}					)]
											SCREAMING_SNAKE_CASE__		: int     =     []
											for split_name, files in data_files.items():
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: List[str]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: Dict     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__     , gen_kwargs={"""files""": files}					)					)
											return splits



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	pa.Table:



											"""simple docstring"""




											if self.config.features is not None:
																		# more expensive cast to support nested features with keys in a different order
																		# allows str <-> int/float or str to Audio for example
																		SCREAMING_SNAKE_CASE__		: Tuple     =     table_cast(SCREAMING_SNAKE_CASE__     , self.config.features.arrow_schema					)
											return pa_table



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__					)					):
																		with open(SCREAMING_SNAKE_CASE__     , """rb"""					) as f:
																									SCREAMING_SNAKE_CASE__		: List[str]     =     pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__					)					)
																									yield i, self._cast_table(SCREAMING_SNAKE_CASE__					)




