




"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__ :    str 					=							logging.get_logger(__name__)

UpperCamelCase__ :    int 					=							{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class     A__      (      lowercase_     ):
	A_							:				List[str]						     =      """rembert"""




	def __init__(   self  :	List[Any]      ,    lowercase  :	str=250_300      ,    lowercase  :	Tuple=1_152      ,    lowercase  :	Optional[int]=32      ,    lowercase  :	Optional[int]=18      ,    lowercase  :	Any=256      ,    lowercase  :	Optional[Any]=1_664      ,    lowercase  :	int=4_608      ,    lowercase  :	Optional[int]="gelu"      ,    lowercase  :	Any=0.0      ,    lowercase  :	Tuple=0.0      ,    lowercase  :	Optional[Any]=0.1      ,    lowercase  :	Dict=512      ,    lowercase  :	List[str]=2      ,    lowercase  :	Optional[Any]=0.02      ,    lowercase  :	Optional[Any]=1e-12      ,    lowercase  :	Optional[Any]=True      ,    lowercase  :	List[str]=0      ,    lowercase  :	Optional[Any]=312      ,    lowercase  :	str=313      ,    **lowercase  :	Dict      ,    )					-> str:
						super().__init__(pad_token_id=lowercase      ,    bos_token_id=lowercase      ,    eos_token_id=lowercase      ,    **lowercase	)

						__snake_case          =	vocab_size
						__snake_case          =	input_embedding_size
						__snake_case          =	output_embedding_size
						__snake_case          =	max_position_embeddings
						__snake_case          =	hidden_size
						__snake_case          =	num_hidden_layers
						__snake_case          =	num_attention_heads
						__snake_case          =	intermediate_size
						__snake_case          =	hidden_act
						__snake_case          =	hidden_dropout_prob
						__snake_case          =	attention_probs_dropout_prob
						__snake_case          =	classifier_dropout_prob
						__snake_case          =	initializer_range
						__snake_case          =	type_vocab_size
						__snake_case          =	layer_norm_eps
						__snake_case          =	use_cache
						__snake_case          =	False

class     A__      (      lowercase_     ):




	@property
	def 							a_		(   self  :	List[Any]	)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
											__snake_case          =	{0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
											__snake_case          =	{0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						        ("""token_type_ids""", dynamic_axis),
						    ]	)




	@property
	def 							a_		(   self  :	Union[str, Any]	)					-> float:
						return 1e-4








"""simple docstring"""

import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__ :    Optional[int] 					=							10_00_00_00_00_00
UpperCamelCase__ :    int 					=							1_00_00

UpperCamelCase__					,					UpperCamelCase__ :    List[str] 					=							os.path.split(__file__)
UpperCamelCase__ :    Any 					=							os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))




def       snake_case__	(       lowerCAmelCase   :		int				,					lowerCAmelCase   :		int		):
					__snake_case          =	pa.Table.from_pydict({"""col""": [0] * chunk_size}		)
					__snake_case          =	pa.concat_tables([table] * (num_examples // chunk_size)		)
					return datasets.Dataset(lowerCAmelCase				,					fingerprint="""table_100B"""		)
@dataclass
class     A__      :
	A_							:				int
	A_							:				int
	A_							:				int
	A_							:				int




	def 							a_		(   self  :	Optional[Any]	)					-> Any:
						__snake_case          =	np.random.default_rng(self.seed	)
						__snake_case          =	rng.integers(low=self.low      ,    high=self.high      ,    size=self.size	).tolist()




	def __iter__(   self  :	str	)					-> int:
						return iter(self._sampled_values	)




	def __len__(   self  :	Union[str, Any]	)					-> Any:
						return self.size




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[0]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[-1]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[range(len(lowerCAmelCase		) // 2				,					len(lowerCAmelCase		) // 2 + 1_024		)]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[RandIter(0				,					len(lowerCAmelCase		)				,					1_024				,					seed=42		)]




def       snake_case__	(       ):
					__snake_case          =	{"""num examples""": SPEED_TEST_N_EXAMPLES}
					__snake_case          =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("""generating dataset"""		)
					__snake_case          =	generate_100B_dataset(num_examples=lowerCAmelCase				,					chunk_size=lowerCAmelCase		)
					print("""Functions"""		)
					for func in functions:
										print(func.__name__		)
										__snake_case          =	func(lowerCAmelCase		)

					with open(lowerCAmelCase				,					"""wb"""		) as f:
										f.write(json.dumps(lowerCAmelCase		).encode("""utf-8"""		)		)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()


