





"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


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

_snake_case	:						int					=					{
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class    _UpperCAmelCase  (      a_					):
					UpperCamelCase						=				'''plbart'''
					UpperCamelCase						=				['''past_key_values''']
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :str=5_00_05	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=6	,			__UpperCamelCase     :str=30_72	,			__UpperCamelCase     :List[Any]=12	,			__UpperCamelCase     :Tuple=6	,			__UpperCamelCase     :Union[str, Any]=30_72	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :int=0.0	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[Any]=True	,			__UpperCamelCase     :Tuple="gelu"	,			__UpperCamelCase     :Tuple=7_68	,			__UpperCamelCase     :List[str]=0.1	,			__UpperCamelCase     :Union[str, Any]=0.1	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :Any=0.02	,			__UpperCamelCase     :Tuple=0.0	,			__UpperCamelCase     :Optional[int]=True	,			__UpperCamelCase     :int=1	,			__UpperCamelCase     :Dict=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Union[str, Any]=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    vocab_size
												A						=    max_position_embeddings
												A						=    d_model
												A						=    encoder_ffn_dim
												A						=    encoder_layers
												A						=    encoder_attention_heads
												A						=    decoder_ffn_dim
												A						=    decoder_layers
												A						=    decoder_attention_heads
												A						=    dropout
												A						=    attention_dropout
												A						=    activation_dropout
												A						=    activation_function
												A						=    init_std
												A						=    encoder_layerdrop
												A						=    decoder_layerdrop
												A						=    classifier_dropout
												A						=    use_cache
												A						=    encoder_layers
												A						=    scale_embedding  # scale factor will be sqrt(d_model) if True
												super().__init__(
												    pad_token_id=snake_case__	,			bos_token_id=snake_case__	,			eos_token_id=snake_case__	,			is_encoder_decoder=snake_case__	,			forced_eos_token_id=snake_case__	,			**snake_case__	,			)







class    _UpperCAmelCase  (      a_					):


					@property
					def       lowerCamelCase			(   self     :str						):
												return OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "sequence"}),
												        ("attention_mask", {0: "batch", 1: "sequence"}),
												    ]						)


					@property
					def       lowerCamelCase			(   self     :List[Any]						):


												if self.use_past:
																			return OrderedDict(
																			    [
																			        ("last_hidden_state", {0: "batch", 1: "sequence"}),
																			        ("past_keys", {0: "batch", 2: "sequence"}),
																			        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
																			    ]						)
												else:
																			return OrderedDict(
																			    [
																			        ("last_hidden_state", {0: "batch", 1: "sequence"}),
																			        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
																			    ]						)







import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCAmelCase :				Any							=1000_0000_0000
UpperCAmelCase :				Any							=1_0000

UpperCAmelCase			,			UpperCAmelCase :				Tuple							=os.path.split(__file__)
UpperCAmelCase :				Tuple							=os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= pa.Table.from_pydict({"col": [0] * chunk_size})
					UpperCamelCase_			= pa.concat_tables([table] * (num_examples // chunk_size))
					return datasets.Dataset(_lowerCAmelCase ,						fingerprint="table_100B")


@dataclass
class    _lowercase :
				'''simple docstring'''







				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= np.random.default_rng(self.seed     )
									UpperCamelCase_			= rng.integers(low=self.low  ,			high=self.high  ,			size=self.size     ).tolist()

				def __iter__(			self     ):

									'''simple docstring'''


									return iter(self._sampled_values     )

				def __len__(			self     ):

									'''simple docstring'''


									return self.size




@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[0]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[-1]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[range(len(_lowerCAmelCase) // 2 ,						len(_lowerCAmelCase) // 2 + 10_24)]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[RandIter(0 ,						len(_lowerCAmelCase) ,						10_24 ,						seed=42)]



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