

'''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


lowerCAmelCase_						   =			1_0_0_0_0_0_0_0_0_0_0_0
lowerCAmelCase_						   =			1_0_0_0_0

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

def     __magic_name__       ( A					,   A							)			->		datasets.Dataset:
	snake_case							 =				pa.Table.from_pydict({'col': [0] * chunk_size}							)
	snake_case							 =				pa.concat_tables([table] * (num_examples // chunk_size)							)
	return datasets.Dataset(A					,   fingerprint='table_100B'							)







@dataclass
class lowerCamelCase      :
				snake_case_           =   42
				snake_case_           =   42
				snake_case_           =   42
				snake_case_           =   42

				def 				_lowerCamelCase						(					self   )						->       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   )						->       Any:
					return iter(self._sampled_values   )






				def __len__(					self   )						->       str:
					return self.size

@get_duration
def     __magic_name__       ( A							)			->		List[Any]:
	snake_case							 =				dataset[0]

@get_duration
def     __magic_name__       ( A							)			->		int:
	snake_case							 =				dataset[-1]

@get_duration
def     __magic_name__       ( A							)			->		Dict:
	snake_case							 =				dataset[range(len(A							) // 2					,   len(A							) // 2 + 1_0_2_4							)]

@get_duration
def     __magic_name__       ( A							)			->		Optional[Any]:
	snake_case							 =				dataset[RandIter(0					,   len(A							)					,   1_0_2_4					,   seed=4_2							)]

def     __magic_name__       ( )			->		Optional[int]:
	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=A					,   chunk_size=A							)
	print('Functions'							)
	for func in functions:
		print(func.__name__							)
		snake_case							 =				func(A							)

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


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








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_						   =			{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["LEDTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			[
						    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LEDForConditionalGeneration",
						    "LEDForQuestionAnswering",
						    "LEDForSequenceClassification",
						    "LEDModel",
						    "LEDPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

						lowerCAmelCase_						   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





