"""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     					=     10_00_00_00_00_00
UpperCamelCase     					=     1_00_00

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

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:int  )				-> datasets.Dataset:
						a_							:			int     		=					pa.Table.from_pydict({"col": [0] * chunk_size}  )
						a_							:			List[str]     		=					pa.concat_tables([table] * (num_examples // chunk_size)  )
						return datasets.Dataset(_SCREAMING_SNAKE_CASE		,			fingerprint="table_100B"  )
@dataclass
class   UpperCAmelCase__				:


	"""simple docstring"""
	lowerCAmelCase__    :					int
	lowerCAmelCase__    :					int
	lowerCAmelCase__    :					int
	lowerCAmelCase__    :					int







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







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







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




@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:datasets.Dataset  )				-> Dict:
						a_							:			List[Any]     		=					dataset[0]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:datasets.Dataset  )				-> Any:
						a_							:			Optional[Any]     		=					dataset[-1]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:datasets.Dataset  )				-> List[Any]:
						a_							:			Optional[int]     		=					dataset[range(len(_SCREAMING_SNAKE_CASE  ) // 2		,			len(_SCREAMING_SNAKE_CASE  ) // 2 + 1024  )]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:datasets.Dataset  )				-> int:
						a_							:			Optional[int]     		=					dataset[RandIter(0		,			len(_SCREAMING_SNAKE_CASE  )		,			1024		,			seed=42  )]

def  lowerCAmelCase_      ()				-> List[str]:
						a_							:			Optional[Any]     		=					{"num examples": SPEED_TEST_N_EXAMPLES}
						a_							:			Union[str, Any]     		=					(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
						print("generating dataset"  )
						a_							:			str     		=					generate_100B_dataset(num_examples=_SCREAMING_SNAKE_CASE		,			chunk_size=_SCREAMING_SNAKE_CASE  )
						print("Functions"  )
						for func in functions:
												print(func.__name__  )
												a_							:			List[Any]     		=					func(_SCREAMING_SNAKE_CASE  )

						with open(_SCREAMING_SNAKE_CASE		,			"wb"  ) as f:
												f.write(json.dumps(_SCREAMING_SNAKE_CASE  ).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_torch_available,
)


UpperCamelCase     					=     {
    'configuration_bark': [
        'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BarkCoarseConfig',
        'BarkConfig',
        'BarkFineConfig',
        'BarkSemanticConfig',
    ],
    'processing_bark': ['BarkProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BarkFineModel',
				    'BarkSemanticModel',
				    'BarkCoarseModel',
				    'BarkModel',
				    'BarkPreTrainedModel',
				    'BarkCausalModel',
				]

if TYPE_CHECKING:
				from .configuration_bark import (
				    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BarkCoarseConfig,
				    BarkConfig,
				    BarkFineConfig,
				    BarkSemanticConfig,
				)
				from .processing_bark import BarkProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bark import (
								    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BarkCausalModel,
								    BarkCoarseModel,
								    BarkFineModel,
								    BarkModel,
								    BarkPreTrainedModel,
								    BarkSemanticModel,
								)

else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




