import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_SCREAMING_SNAKE_CASE   		=			5_0_0_0_0_0

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





@get_duration
def  lowercase(  lowerCamelCase					)		->  Optional[int]:







 '''simple docstring'''
 UpperCamelCase      =			dataset.select(range(0       ,				len(UpperCamelCase_					)       ,				2					)					)





@get_duration
def  lowercase(  lowerCamelCase					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			dataset.sort("""numbers"""					)





@get_duration
def  lowercase(  lowerCamelCase					)		->  List[Any]:







 '''simple docstring'''
 UpperCamelCase      =			dataset.shuffle()





@get_duration
def  lowercase(  lowerCamelCase					)		->  Optional[Any]:







 '''simple docstring'''
 UpperCamelCase      =			dataset.train_test_split(0.1					)





@get_duration
def  lowercase(  lowerCamelCase       ,				lowerCamelCase=10					)		->  int:







 '''simple docstring'''
 for shard_id in range(UpperCamelCase_					):
  UpperCamelCase      =			dataset.shard(UpperCamelCase_       ,				UpperCamelCase_					)





def  lowercase(  )		->  Optional[Any]:







 '''simple docstring'''
 UpperCamelCase      =			{"""num examples""": SPEED_TEST_N_EXAMPLES}
 UpperCamelCase      =			(select, sort, shuffle, train_test_split, shard)
 with tempfile.TemporaryDirectory() as tmp_dir:
  print("""generating dataset"""					)
  UpperCamelCase      =			datasets.Features({"""text""": datasets.Value("""string"""					), """numbers""": datasets.Value("""float32"""					)}					)
  UpperCamelCase      =			generate_example_dataset(
      os.path.join(UpperCamelCase_       ,				"""dataset.arrow"""					)       ,				UpperCamelCase_       ,				num_examples=UpperCamelCase_					)
  print("""Functions"""					)
  for func in functions:
   print(func.__name__					)
   UpperCamelCase      =			func(UpperCamelCase_					)

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


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

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   		=			{
    """configuration_align""": [
        """ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AlignConfig""",
        """AlignTextConfig""",
        """AlignVisionConfig""",
    ],
    """processing_align""": ["""AlignProcessor"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   		=			[
         """ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AlignModel""",
         """AlignPreTrainedModel""",
         """AlignTextModel""",
         """AlignVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

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