


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_snake_case				=			1_0_0_0_0_0_0_0_0_0_0_0
_snake_case				=			1_0_0_0_0

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







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




@dataclass
class  A_ :







 """simple docstring"""





 _UpperCamelCase					:			int
 _UpperCamelCase					:			int
 _UpperCamelCase					:			int
 _UpperCamelCase					:			int
 def 	_UpperCamelCase     (      self       :			Any					)     ->      Union[str, Any]:
      UpperCAmelCase    :					Any			=      np.random.default_rng(self.seed					)
      UpperCAmelCase    :					Union[str, Any]			=      rng.integers(low=self.low							,     high=self.high							,     size=self.size					).tolist()
 def __iter__(      self       :			Dict					)     ->      Optional[Any]:
      return iter(self._sampled_values					)

 def __len__(      self       :			Dict					)     ->      Optional[Any]:
      return self.size







@get_duration
def 	A (		A__			):
     UpperCAmelCase    :					List[str]			=      dataset[0]







@get_duration
def 	A (		A__			):
     UpperCAmelCase    :					Any			=      dataset[-1]







@get_duration
def 	A (		A__			):
     UpperCAmelCase    :					Optional[Any]			=      dataset[range(len(A__			) // 2		,		len(A__			) // 2 + 1_0_2_4			)]







@get_duration
def 	A (		A__			):
     UpperCAmelCase    :					Dict			=      dataset[RandIter(0		,		len(A__			)		,		1_0_2_4		,		seed=4_2			)]







def 	A (		):
     UpperCAmelCase    :					Optional[int]			=      {"num examples": SPEED_TEST_N_EXAMPLES}
     UpperCAmelCase    :					Any			=      (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
     print("generating dataset"			)
     UpperCAmelCase    :					Any			=      generate_100B_dataset(num_examples=A__		,		chunk_size=A__			)
     print("Functions"			)
     for func in functions:
          print(func.__name__			)
          UpperCAmelCase    :					Any			=      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()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case				=			{
    "configuration_oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig"],
    "processing_oneformer": ["OneFormerProcessor"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			["OneFormerImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "OneFormerForUniversalSegmentation",
          "OneFormerModel",
          "OneFormerPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure)



